Mainframe computers deliver mission-critical applications with strong performance, reliability and security. But unlocking insights comes at a cost. Are you looking to extract more value from your mainframe data – while offloading
processing to less expensive platforms, especially to the cloud?
Modern cloud architectures combine three essentials: the power of data warehousing, flexibility of Big Data platforms, and elasticity of cloud at a fraction of the cost to traditional solution users.
But which solution is the right one for you and your business? Download the eBook to see a side-by-side comparison.
Do you need an easy solution to help you get more out of your SAP data investments? With Qlik, you can leverage your SAP data to modernize your data warehouse or lake, integrate with your cloud, manage test data, and boost your analytics capabilities.
Ready to deliver trusted data to your analytics platform in real time – and save millions of dollars in the process? Qlik commissioned Forrester Consulting to conduct a Total Economic Impact™ (TEI) study and examine the potential return on investment (ROI) enterprises may realize by deploying Qlik’s Data Integration platform. In this study, you can read the key findings, drill into the details, and get a framework for evaluating the potential for savings in your own organization.
For years, there was a standard order of operations for data delivery: ETL, or extract, transform, and load. But things have changed.
The explosion in data, the vast array of new capabilities, and the dramatic increase in demands have changed how data needs to be moved, stored, processed and analyzed. But new architectures like data warehouses and lakes are creating additional bottlenecks within IT, because many existing processes are labor-intensive and insufficient.
Take a deep dive into data warehouse automation (DWA) to know its’ history, drivers and evolving capabilities. Learn how you can reduce the dependency on ETL scripting, advance your user experience, implementation, maintenance and updates of your data warehouse and data mart environments with Qlik Compose™.
As you make the decision to move your data warehouse from on-premise to the cloud or cloud to cloud, there are many things to take into consideration. Find out how a cloud data warehouse in Azure has advantages in cost, time to value, and the ability to work with real-time data across the organization for analytics.
Source-to-target time reduced from days to seconds. 60+ million rows replicated hourly. And improved data delivery by 400%. These are just a few of the outcomes that leading organizations have seen after solving their data integration challenges with Qlik. Around the world, Qlik is helping enterprises in every industry streamline, accelerate, and automate their data pipelines to deliver in-the-moment data for immediate action.
Your business relies on data. And your users want it fast. IT and data teams are seeing a dramatic increase in data demands, on top of a massive explosion in data volume and extensive array of new capabilities.
For years, TDWI research has tracked the evolution of data warehouse architectures as well as the emergence of the data lake. The two have recently converged to form a new and richer data architecture.
Within this environment, data warehouses and data lakes can incorporate distinct but integrated, overlapping, and interoperable architectures that incorporate data storage, mixed workload management, data virtualization, content ETL, and data governance and protection.
This TDWI Best Practices Report examines the convergence of the data warehouse and data lake, including drivers, challenges, and opportunities for the unified DW/DL and best practices for moving forward.
In today’s fast-moving marketplace, real-time decisions are essential – and they have to be informed by data. That requires Active Intelligence, or the ability to take immediate action based on up-to-the-minute data.
Cloud data warehouses are at the heart of digital transformation because they require no hardware, are infinitely scalable, and you only pay data resources you consume. However, that’s not the whole story.
Azure Synapse, Amazon Redshift, Google Big Query and Snowflake all require real-time data integration and lifecycle automation to realize their full potential. Yet these two capabilities are not included, forcing you to hand-code ETL scripts to close the gaps. As a result, your developers are constrained and your data transfers are constricted, compromising your initial ROI.
In this whitepaper, Eckerson Group discusses how to get maximum value from data lakes and how Qlik’s Data Integration Platform helps businesses get the most value out of their data lakes quickly, accurately, and with the agility to respond to shifting business needs.
Now more than ever, data is moving to the cloud, where data warehousing has been modernized and reinvented. The result is an explosion in adoption. And for Snowflake users, Qlik offers an end-to-end data integration solution that delivers rapid time-to-insight.
To adapt – and succeed – in today’s rapidly evolving digital landscape, enterprises have adopted new data architectures, including data lakes. But despite the investment, the insights still aren’t coming quickly enough – because traditional integration processes just can’t meet the demand.
For database managers and users, moving to the cloud means breaking through the confines imposed by capacity ceilings, speed limits, performance issues, and security concerns. At the same time, moving to the cloud does not mean outsourcing control or responsibility for data to an outside provider, and care must be taken to ensure migrations take place with as little disruption to the business as possible. In addition, organizations need to be prepared with the specific skills required to migrate to and manage databases in the cloud. Download this white paper for the questions you need to ask before undertaking a cloud migration.
This Analyst Brief lists the top 16 Key Value NoSQL Databases Ranked by Customer Satisfaction Score. Find out why Aerospike is voted number one.
Dream11 is India’s largest fantasy sports platform that allows users to play fantasy cricket, hockey, football, kabaddi and basketball. Aerospike runs as a cache in AWS in conjunction with RDS for real-time leaderboards and contest management handling 2.7 million concurrent users. Dream11 moved from Elasticache Redis to Aerospike for improved availability, cost, latency and elasticity. Aerospike is deployed with Spark and Kafka to manage traffic spikes across geos using microservices across seven clusters – all on AWS.
Consulting on behalf of Aerospike, customer analytics is the engine of a customer-centric, insights-driven business. And as the pace of business accelerates and real-time insights become a critical component to growth, new tech organizations must turn to platforms that can deliver analytics in real-time to support modern customer experience initiatives.
During this 45 minute BrightTALK Summit presentation Stuart Tarmy, Global Director of Financial Services Industry, Aerospike will be discussing how our customers are addressing the key challenges present in leveraging AI effectively in Financial Services.
Barclays processes circa 30 million-plus payment transactions a day for its 20 million-plus customers and had many fraud detection solutions in place across its various business units. Barclays knew transaction fraud detection required ultra low latency, but not having the ability to seamlessly re-use its large-scale user profile datasets across use cases across its business units resulted in multiple complex, bespoke engineering solutions. These solutions became increasingly difficult to both maintain and evolve, and thus posed a significant limitation to achieving the company’s strategy.
To thrive in today’s highly competitive digital environment, financial services companies will need to modernize their data infrastructure, connecting and streamlining information flow across exploding arrays of data sources and datasets. This will power
their customer-facing front office applications, risk mitigation and trading analysis, conduct faster and more cost-effective settlements, payments and adhere to compliance regulations.
Join us for an interactive live conversation with Ram Kumar Rengaswamy, VP Engineering, FreeWheel, a Comcast Company, Gerry Louw, Head of Worldwide Solutions Architectire, AWS and Srini Srinivasan, Founder and Chief Product Officer, Aerospike. Learn how Freewheel used a real-time data platform to predictably scale their business running Aerospike on AWS.
This whitepaper provides a simplified reference architecture for Demand Side Platform datastores for real-time bidding and campaign reporting using Aerospike as the datastore technology.
The race to compete for online ad bidding, placement and delivery is being waged on display, video, mobile and in-app. As a result, both speed and accuracy are at a premium, lest your customer go to your competitor for better results. Plus, with online cookie technology on its way out, and regional regulations on their way in, ad tech players must now leverage multiple additional sources of data just to rebuild online profiles, globally, for precise marketing and targeting, while controlling for new compliance needs. In short, the ad tech industry’s insatiable appetite for more data, faster has only grown in recent years, with the need for a real-time data platform.
It's time to make sense of the modern data stack. Read this guide to learn what a practical implementation of modern data tooling looks like along four phases: Starter, Growth, Machine Learning, & Real-Time. The 80-page resource delivers data stack architectures and tactical advice to help you progress towards data maturity. It will help you define where you are today and build on your existing toolset to take the next step on your journey.
Read this eBook to learn the four steps of building a modern data architecture that’s cost-effective, secure, and future proof. In addition, this eBook address the most common challenges of integrating mainframe data:
Different storage formats
Data masking is essential when working with sensitive data. It is a crucial part of maintaining a secure data environment and avoiding data breaches. While, data masking projects are seemingly simple, they are actually quite tricky. These projects are full of pitfalls and “yes, but” conditions, which aren’t flexible and dynamic enough to keep up with the changing data environment but are unfortunately common experiences of data team members. This document will cover some of the reasons why data teams struggle with data masking projects.
We are thrilled to be named a 2022 Gartner Peer Insights Customers’ Choice for Data Integration Tools. Per Gartner, “Vendors placed in the upper-right quadrant of the ‘Voice of the Customer’ quadrants are recognized with the Gartner Peer Insights Customers’ Choice distinction, denoted with a Customers’ Choice badge.” The recognized vendors meet or exceed both the market average Overall Rating and the market average User Interest and Adoption”. We are the ONLY vendor to receive it four times in a row!
“Master data management (MDM) is a technology-enabled business discipline in which business and IT work together to ensure the uniformity, accuracy, stewardship, governance, semantic consistency, and accountability of an enterprise’s official shared master data assets.” The Gartner “December 2021 Critical Capabilities for Master Data Management Solutions” evaluates MDM vendors technical capabilities and their ability to support five business use cases.
Deploying today’s flexible technology services and components— containers, microservices, and cloud-based infrastructure—may bring measures of improved agility to IT and data management operations, but translating this into business agility is what makes these technologies impactful. Here’s where an agile technology architecture demonstrates its true value. Download this special report for key capabilities and best practices for success.
To keep your MySQL environment running at peak performance, you need granular real-time information about database performance and availability. Automated alerts, change tracking, compliance reporting and centralized management are also critical, especially in highly distributed environments.
The datapocalypse is upon us. At the same time data volumes are growing at an exponential rate, business stakeholders are demanding expanded access to data for daily decision-making. Data is taking a central role as enterprises seek to harvest its maximum value, protect it from threats and remain compliant with data privacy regulations.
Explore iPaaS Customers’ Payback Period, 3-Yr ROI, TCO & More.
Choosing the right integration platform is key to embracing hyperautomation and digital transformation. It’s critical to consider your organization’s goals, pain points and resource constraints when evaluating integration platform-as-a-service (iPaaS) solutions.
Even the most complex integrations should be quick and easy. It’s time for next-gen iPaaS.
There’s never been a greater need for speed and agility. Are legacy applications and processes holding you back?
See why new cloud means new speeds.
The need to leverage data—at unprecedented scale and complexity, with unprecedented speed and accuracy—has led businesses to adopt cloud technologies, solutions, and applications, usually alongside existing on-premises infrastructures. The resulting complexity challenges IT’s ability to achieve the core goal of delivering trusted, actionable data when and how the business needs it.
Hyperautomation earned a top mention in the Gartner “Top Strategic Technology Trends for 2022” report. But, what is hyperautomation — and how can it empower you to tackle complex business processes and thrive in today’s digital-first economy?
Qlik Data Integration enables seamless Mainframe data movement to Microsoft Azure Synapse, Azure HDInsight, and/or Azure Event Hubs in real-time. Our Replicate solution extracts data from 40+ sources, including Db2 for z/OS, IMS, and VSAM as well as mid-range IBM Db2 for I (DB2/400; iSeries; AS/400) and IBM DB2 for LUW in real-time through our CDC technology.
If you’re like nearly 80% of organizations, you rely on a variety of cloud platforms to store your data and provide critical business functions.
In today’s digital economy, you need the speed and flexibility of a SaaS solution to deliver trusted, connected, and contextual master data.
Whether you want to sharpen the focus of marketing campaigns or identify opportunities to streamline business processes, Master Data Management (MDM) delivers the 360-degree view of data that makes it possible. But what’s the best way to build the right MDM solution for your organization?
To tie all of your systems and information together into a single source of trusted data, you need a master data management (MDM) strategy that combines scale and robust functionality with speed and flexibility. A cloud-native MDM solution enables you to obtain MDM as a service (SaaS), which allows greater focus on driving business value from master data.
Managing data in the cloud, but seeing the opposite of what you expected? Slow performance – and cost overruns? Cloud data management provides flexibility both in terms of resources and budget. But it can be hard to get right.
Is your organization hindered by data silos and a misaligned data strategy? If so, you’re not alone.
According to a recent survey of 300 data leaders like you, companies are juggling data across multiple solutions while their analytics are dispersed across various groups. Further, many report that their data strategy isn’t aligned with the business strategy.
Organizations of all types are turning to intelligent, automated cloud-native data management to deliver cloud analytics that accelerate insights and drive innovation.
As organizations consolidate and modernize their on-premises data warehouses and data lakes in the cloud – or stand up new ones in the cloud, it’s more important than ever to avoid the pitfalls of hand coding.
According to the "Gartner Sixth Annual Chief Data Officer Survey," "top data and analytics leaders are either leading (24%) or are heavily involved (48%) in digital transformation."
Looking for an enterprise data catalog to automate data discovery, curation and lineage? Forrester’s 2022 evaluation of enterprise data catalog providers can help.
When your success depends on high-quality, trusted data to deliver enterprise value, you can rely on our 14-time position as a Leader in 2021 Gartner® Magic Quadrant™ for Data Quality Solutions.
Achieving enterprise-wide data governance is a huge opportunity, so it makes sense to break that journey down into more manageable steps.
Just as pilots run through a pre-flight checklist before they ever leave the ground, taking steps to increase your understanding of and confidence in your data in the cloud is key to better data-driven decisions that send your business initiatives soaring.
How Data Integration Boosts a Competitive Edge in a Multi-Cloud Ecosystem
If you’re like nearly 80% of organizations,1 you rely on a variety of cloud platforms to store your data and provide critical business functions.
You need trusted, real-time data and analytics to fuel your business growth — and you need it fast.
O’Reilly’s Cloud Native Go provides developers with a comprehensive overview of the Go programming language and its unique properties that make it optimal for building, deploying, and managing services distributed across ten, one hundred, or even a thousand servers. Download the free 3-chapter excerpt, courtesy of Cockroach Labs, for hands-on guidance to start building resilient, cost-effective, cloud-native applications today.
Database management today is flush with new challenges and opportunities. More than ever before, businesses today desire speed, scalability, and flexibility from their data infrastructure. At the same time, the complexity of data environments continues to grow – spread across different database types and brands, applications, and on-premise and cloud sites. This makes new technologies and strategies critical. Download this special report today for key practices to managing databases in this highly diverse, emerging landscape.
Each year, Cockroach Labs publishes the industry’s only independent performance analysis of a variety of instance types across AWS, GCP, and Azure — to help you find the best options for your workloads.Beyond benchmarks alone, the report goes inside the numbers to offer practical advice you can apply to the applications you’re building. Download your free copy of Cockroach Labs’ 2022 Cloud Report now.
Analytics on the warehouse unlock insights and analysis that aren't possible in third party tools. Your organization can make better decisions, faster when you use your data warehouse as a central source of truth to fuel the downstream tools. In this guide, you’ll learn how to put together the infrastructure required to enable richer, more comprehensive analytics on top of your warehouse.
Organizations demand real-time analytics in order to make the best decisions for their business. They want data streamed from data sources such as IoT devices, online transactions and log files, which can only be gathered using modern data architecture that supports real-time ingestion, allows faster work speeds and enables the freshest data to be shared among multiple teams.
That’s where Quest SharePlex comes in. In this tech brief, we discuss how organizations can use Quest SharePlex to efficiently replicate data from Oracle databases into Kafka for real-time streaming.
It finally happened: Your CIO has told you to prepare for the cloud migration of your organization’s databases. The digital transformation process is lengthy and riddled with the risks of moving your on-premises databases to the Microsoft Azure SQL database. Where do you start?
Quest offers a variety of information and systems management tools, explained in our white paper that will guide you along the migration path.
Download your copy to learn:
The three phases of database migrations
How to navigate each phase
How to use Quest products to reduce the risks to system performance and service levels
Database developers and database administrators (DBAs) have been using open-source databases, such as MySQL and PostgreSQL, for years. The platforms are mature, offer flexibility with low license costs and have a huge community following. Plus, they help reduce your dependence on commercial databases, such as Oracle Database.
What’s getting in the way of your using them more?
This technical brief explores ways to use SharePlex® by Quest® to replicate your Oracle data to open-source databases. Data analysts and DBAs will see how SharePlex replicates data from Oracle in nearly real time to platforms like MySQL and PostgreSQL. The replication technology in SharePlex opens up your options for enjoying the maturity, flexibility and low cost of open source in your IT landscape.
Worried about your next big database migration or upgrade? You’re not alone. Whether it’s planned or unplanned, downtime is out of the question. You need to ensure high availability to keep your business up and running. Without that, you lose productivity and profits. So it may feel safer to put off your next big database project.
But holding off on switching to faster, more affordable databases puts you at a competitive disadvantage. To meet your business goals, you need modern databases that maximize data value and cut costs. What if you could run your mission-critical apps during migrations? This means you could meet SLAs while future-proofing your database environment.
In this three-part e-book, you’ll learn how to:
Move data safely and easily – without interrupting business.
Avoid risk, downtime and long hours.
Select the best tools for your next database migration or upgrade.
Sharing workloads between your data center and the cloud to form hybrid cloud and multi-cloud environments firms your IT resilience and business continuity. However, it also comes with a thorny technical question: How do you keep them in sync? You’re only as IT-resilient as the lag between production data and its replica, so true IT resilience depends on high speed and low fault tolerance as you replicate between databases.
This technical brief describes ways to use SharePlex® by Quest® to support your hybrid and multi-cloud strategies. Using examples from some SharePlex customers, it illustrates replication techniques that keep Oracle data synchronized in nearly real time so that you can apply those techniques in your own hybrid and multi-cloud scenarios.
Has Oracle 19c Standard Edition 2 (SE2) taken a step backward in high availability (HA) and disaster recovery (DR)?
It appears so. If you plan to stick with Standard Edition 2, take a second look at how you will handle server failures and avoid downtime in your environment. Oracle 19c will make you re-think your options for achieving the high availability and disaster recovery you’ve become accustomed to.
This technical brief examines those options in detail. You’ll see the differences — especially in recovery time — and discover how you can use SharePlex® by Quest® to stick with Oracle SE2 without putting your high availability and disaster recovery strategies at risk.
If you run Oracle, database upgrades and migrations are inevitable. While there are real benefits to performing these upgrades and migrations, changes of this scale introduce equally real risks of unforeseen issues and downtime. Native Oracle solutions provide some protection, but all have trade-offs or leave remaining risks.
Business requirements around uptime and continuity are ultimately what create pressure and stress around upgrades and migrations. And, while it’s reasonably easy to predict downtime associated with the known steps in upgrading, there’s always the threat of unplanned downtime associated with unexpected problems.
This paper explores the various drivers and challenges associated with upgrades and migrations and presents proven approaches used by SharePlex customers to mitigate the risks and flawlessly upgrade without impact to the business.
Meeting the demands of the rapid evolution to real-time business requires new perspectives and approaches. Download this report for seven recommendations to make the most of real-time capabilities today.
Integration is essential to cloud modernization. Your enterprise integration platform as a service (iPaaS) needs to be up to the challenge. Informatica is a Leader again in the 2021 Gartner® Magic Quadrant™ for Enterprise iPaaS. Download the report to see why.
When you choose an integration Platform as a Service (iPaaS), it’s important to consider your overall benefits and ROI. How long will it take you to achieve payback on your investment? What will your three-year ROI be? Where will you reap the most time and cost savings?
Hyperautomation and iPaaS: Automate key business processes Hyperautomation earned a top mention in the Gartner “Top Strategic Technology Trends for 2022” report.1 But, what is hyperautomation — and how can it empower you to tackle complex business processes and thrive in today’s digital-first economy?
The need to leverage data—at unprecedented scale and complexity, with unprecedented speed and accuracy—has led businesses to adopt cloud technologies, solutions, and applications, usually alongside existing on-premises infrastructures. The resulting complexity challenges IT’s ability to achieve the core goal of delivering trusted, actionable data when and how the business needs it.
There’s never been a greater need for speed and agility. Are legacy applications and processes holding you back?
See why new cloud means new speeds.
he “Voice of the Customer” synthesizes Gartner Peer Insights’ reviews into insights for IT decision makers. This aggregated peer perspective, along with the individual detailed reviews, is complementary to Gartner expert research and can play a key role in your buying process, as it focuses on direct peer experiences of implementing and operating a solution.
Looking for faster ROI from cloud data integration initiatives? Download TDWI’s Checklist Report, “Six Ways to Accelerate ROI from Data Warehouse and Data Lake Modernization,” now!
By 2025, more than 75% of the midsize, large, and global organizations will establish integration strategy empowerment
teams to support collaborative integration, up from 40% in 2021.
This ESG Technical Review documents the detailed evaluation of Quest QoreStor with Veeam. ESG evaluated how the Quest QoreStor solution provides easy installation and management, cyber resiliency, and enhanced performance.
This paper explains how you can significantly lower cloud object storage requirements and cost for data protection. With the right practices and technology in place, you can take advantage of object storage in the cloud to lower the overall cost of data protection.
Multiple backup solutions, clouds, and cloud storage tiers can all lead to escalating cloud storage costs and complexity. These, in turn, hamper an organization’s ability to perform a DR in the cloud. Three new best practices exist that account for these new variables. By adopting them, organizations better position themselves to more quickly perform DR in the cloud while incurring lower costs.
Cloud storage provides organizations with a simple and effective means to store their backup data off-premises. However, as they do so, they should strive to keep their cloud storage management practices as simple as possible. To achieve these ends, use backup solutions that interface with and manage multiple cloud storage offerings, such as Quest QoreStor.
These solutions equip organization to use a single cloud storage provider or multiple. They may then freely move between cloud providers at any time so they can adapt should their business or technical needs ever change.
Download a free copy of O’Reilly’s CockroachDB: The Definitive Guide. Whether building from scratch or rearchitecting an existing app, modern distributed applications need a distributed database. This essential reference guide to CockroachDB — the world’s most evolved distributed SQL database — shows how to architect apps for effortless scale, bulletproof resilience, and low-latency performance for users anywhere.
As data lakes increasingly make their move to the cloud, it’s easier than ever to set up, maintain, and scale storage to meet your all your analytic needs. But, with all the platforms out there, it can be hard to know exactly which is right for you.
So what makes a good API? The same thing that makes products outstanding – design.
Modern data analytics have the potential to reinvent your business. But to take advantage, IT has to reinvent how they move, store and process data. And integration is a big challenge.
We’ve compiled a range of expert outlooks on the state of data in an effort to understand what the near future may hold. From new technologies to emerging skill sets and beyond, the ideas collected in this Trendbook point towards a world centered around the importance of data.
In this Trendbook, you’ll explore:
How evolving technologies and tools are critical for properly leveraging data
Why democratizing access is more important than ever
How data teams are being diversified and upskilled to keep pace
Actions being taken to derive efficient, critical insights from data
Traditionally, organizations depended on monolithic architectures that initially served them well, but today’s on-demand business environment calls for a model that can support a more flexible, microservices-driven approach, and facilitate the pace of innovation. This eBook covers the evolution of data management and application environments, contributing factors to modern application requirements, common customer challenges with traditional database technologies, and presents the value of document databases and Amazon DocumentDB. We will also highlight Amazon DocumentDB customer use cases, target workloads, new features, migration programs, and key resources to get started.
Many organizations’ data assets are hidden away in silos or proprietary applications, which can take great amounts of time and resources to locate. This is made more complicated as the amount of data flowing through, into, and out of enterprises keeps growing exponentially. Data catalogs can enable self-service analytics and data lake modernization, as well as support data governance, privacy, and security initiatives. Download this special report, sponsored by Sandhill Consultants, to dive into best practices for harnessing the power of data catalogs in the enterprise today.
Despite greater awareness of threats, protecting data has not become easier in recent years. Data is being created at a rapid clip and there are more ways than ever before to store it. Understanding today’s data security and governance problems is the first step to solving them. Download this special report for 10 key tenets for improving data security and governance in today’s complex data environments.
More data is being collected, stored, transformed, analyzed, and acted upon than ever
before. The explosion in data generation has led to growth in the number of database platforms and instances deployed across organizations of all sizes. Many IT organizations and database professionals struggle to manage this, resulting in database operations not optimized for performance, agility, security, or cost.
Download this report to learn how organizations are turning to solutions like Nutanix Era on HPE GreenLake to solve these challenges by creating cloud-like agility and enabling business process automation to achieve faster time to value.
There is one clear direction data management has been going in as of late – to the cloud. The availability of cloud resources provides new options for building and leveraging enterprise-scale data environments. Support for hybrid and multi-cloud data warehousing is becoming mainstream, edge analytics adoption is rising, and the demand for real-time analytics capabilities is soaring. As more organizations invest in data warehouse and data lake modernization, these areas are also converging with concepts such as the “data lakehouse” and the “data mesh.” Download this special report to navigate the growing constellation of technologies and strategies emerging in modern data management and analytics today.
Data governance often conjures up the idea of some central authority instituting a “culture of no” but in reality it can be a powerful engine to scale the use and distribution of trusted data pipelines. Your organization’s data governance should be able to meet complex requirements, as well as enable your team to develop and deliver trusted data to the right users in the right format at the right time. With data governance in place, you can confidently ensure data privacy, proactively comply with regulations, and allow easy collaboration with data professionals in every function. Read this whitepaper to get your Data Governance Strategy started.
Based on a global survey conducted by Forrester, 86% of enterprises have adopted a multi-cloud strategy due to shifting business priorities. Organizations need to optimize the costs of running and managing data in the cloud while simultaneously enabling developer velocity to efficiently build the modern, intelligent applications of tomorrow.
This white paper details the benefits of a multi-cloud strategy and explains how to simplify deployment of your cloud database. Learn the top four reasons to consider multi-cloud:
Freedom of choice — host applications and data anywhere, on any device, any cloud, and at global scale
No single cloud provider lock-in — 73% of the enterprises consider multi-cloud for that reason
Higher (ROI) — 65% of the enterprises consider competitive pricing as a compelling reason to make a strategic shift to multi-cloud
Reduced downtime risk
Monolithic databases like SQL can’t handle the demands of today’s data-intensive workloads. They are too slow, too unscalable, and too prone to failure.
But there is a path forward. Modern, open NoSQL databases like Apache® Cassandra™ are designed for the data demands of customer-engagement apps and streaming IoT.
Read this white paper on the best approaches for data modernization, and learn:
The opportunities coming from turning business logic trapped in a monolithic database into lightweight, independent microservices.
Critical components for a successful transformation, including a scalable and open data API layer
SQL-to-Cassandra migration methodology and the value of testing and validating query patterns
The data management landscape can be very confusing, with many database vendors offering quite distinct technologies. This IDC brief provides you with a template to help define your workloads and identify combinations of database technologies that support extreme scalability, high performance, and flexible operations.
Read this report by IDC’s Analyst Carl Olofson, “Combining Scalable Database Technologies to Achieve Operational Flexibility and Analytic Power,” and learn:
How to achieve synergy among several distinct data management technologies
Differences between database workload types: operational, streaming, and analytical
Trends impacting the data management landscape, such as streaming data and AI
The growth of cloud-native DBMSs and benefits
Read the IDC brief now.
At a time when enterprises are seeking to leverage greater automation and intelligence, many are becoming acquainted with the advantages of using knowledge graphs to power real-time insights and machine learning. In fact, Gartner predicts that, by 2023, graph technology will play a role in the decision-making process for 30% of organizations worldwide. Download this special report to understand how knowledge graphs are accelerating analytics and AI in the enterprise today.
Watch this webinar with Incorta, Microsoft, & DBTA to learn how you can build a modern data architecture with a complete, end-to-end unified data analytics platform.
Why are organizations adopting SQL data lakehouses? The SQL data lake house is a type of cloud data architecture that queries object stores at high speed and provides access to data across multiple sources to support both business intelligence (BI) and data science workloads. Enterprises adopt the SQL data lakehouse to streamline their architectures, reduce cost, and simplify data governance. Common use cases include reporting and dashboards, ad-hoc queries, 360-degree customer views, and artificial intelligence/machine learning (AI/ML).
For most enterprises, information is a competitive weapon. Organizations increasingly compete on the effectiveness of their information systems to make better business decisions. As a result, data analytics has become a strategic imperative. Data engineers play a crucial role in designing, operating, and supporting the increasingly complex environments that power modern data analytics. What are their most important challenges and how can they solve them? Read this white paper to discover strategic solutions to data engineering challenges for increased effectiveness of analytic systems.
Download a free copy of the latest O'Reilly Ebook on Distributed SQL Databases, to learn what Distributed SQL means and how it helps support massive, global applications. In this report you will also learn what kinds of companies benefit from using Distributed SQL and what the future holds for Distributed SQL databases.
There’s no turning back from cloud as an enterprise data platform, and adoption continues to expand rapidly. The question is not whether to pursue a cloud strategy, but how to do so to meet your organization’s business requirements most efficiently and cost-effectively. Download this special report to gain a deeper understanding of emerging best practices, key enabling technologies, challenges, and solutions in the evolving world of cloud and data management.
Customers are increasingly migrating from legacy on-premises databases to purpose-built cloud databases because of the performance, scalability, availability, and cost benefits that cloud databases provide. In Gartner vendor evaluation covering cloud database management systems (CDBMS), AWS is named a Leader positioned highest in Ability to Execute among the 16 vendors evaluated. Read the report to discover why Gartner positioned AWS as a Leader and take a deep dive into the benefits of AWS purpose-built databases for modern applications.
Enterprise IT is undergoing massive transformation led by technologies that enable the virtualization, dynamic deployment, and elastic scalability of resources. Coupled with professional management services in the cloud, these technologies offer more control and cost-effective management of IT systems than has ever been seen before. This is particularly important in the database sphere. Without cloud managed database services, databases are managed manually through fixed compute and storage resources acquired for fixed periods and maintained by the datacenter staff.
The development of the star schema was a clever way to get around the performance issues of relational databases as BI and multi-dimensional analysis became more popular, but this design came with its own set of problems. Business users always think up new ways to query data. And the data itself often changes in unpredictable ways. This has resulted in the need for new dimensions, new (and mostly redundant) star schemas and indexes, maintenance difficulties in handling slow-changing dimensions, and other problems. Altogether, it has created an analytical environment that is overly complex, sluggish, and generally unsatisfactory for both users and those who maintain it. Watch this webinar today to learn about the three advances in database technologies that eliminate the need for star schemas and the resulting maintenance nightmare.
According to Gartner, by 2025, 75% of enterprise-generated data will be created and processed outside of a traditional centralized data center or cloud. This means data will come from everywhere, with applications—and users—geographically distributed.
Is your enterprise prepared for this new data environment?
Edge computing distributes computation and data storage closer to where the data is produced and consumed. New edge applications exploit devices and network gateways to perform tasks and provide services locally on behalf of the cloud—with the most powerful edge applications being stateful.
However, stateful edge applications demand a new data architecture that considers the scale, latency, availability, and security needs of applications.
In this white paper, we explore the issues, challenges, and opportunities created when you build stateful applications at the edge.
YOU WILL DISCOVER:
The key considerations when designing a data architecture for edge applications
Achieving data pipeline observability in complex data environments is becoming more and more challenging and is forcing businesses to allocate extra resources and spend hundreds of person-hours carrying out manual impact analyses before making any changes. Challenge this status quo and learn how to enable DataOps in your environment, ensure automated monitoring and testing, and make sure that your teams are not wasting their precious time on tedious manual tasks.
This session will help you:
Uncover your data blind spots
Define validation and reconciliation rules across your on-premises and cloud platforms
Deliver pipeline visibility to all data users so they know exactly which areas demand immediate attention
Monitor data conditioning over time to ensure data accuracy and trustworthiness
Carry out automated impact analyses to prevent data incidents and accelerate application migration
One of MANTA’s customers, a global Fortune 500 financial services company, was falling behind on BASEL II/III compliance. The manual reporting that was being done in Excel spreadsheets couldn’t keep up with the mounting compliance requirements of the regulation that requires financial institutions to prove they meet the minimum capital and reporting requirements with the goal of minimizing credit risks.
The customer initially had around 250 such metrics to meet, and the number eventually rose to 500. Manually collecting information about their data turned out to be inefficient and error-prone, and it led to numerous decisions that did not reflect the actual state of the data. In order to address these issues, the customer tried to implement several data governance and data catalog solutions. However, due to their limited lineage capabilities, they were not able to answer the most crucial questions.
What are the sources of the data that is being used to generate riskbased metrics?
Cloud transformation radically changes how an organization operates, how it budgets, and how it competes. And while being very disruptive,migration needs to be carefully controlled and closely observed in order to succeed.
Today’s enterprises are equipped with hundreds of specialized tools and technologies to enable data experts and business intelligence operators to collect, integrate, and analyze record volumes of data. So why does the divide between data creators and data consumers continue to widen? Why does it feel like the more we can do with our data, the less we understand what should be done with it?
The answer is that we are being challenged by the complexity that all these integrations, tools, and processes have introduced to the data environment, wreaking havoc on our ability to effectively manage data pipelines and trust the data in our reports.
Data-driven organizations are adopting augmented data management to deal with complexity, keep the ability to innovate, and iterate quickly. CTOs and CIOs of successful companies are facing one major challenge: the skyrocketing complexity of their data stack (data pipelines). Combined with a shortage of engineering talent, it limits their ability to cope with the fast pace of changes, negatively impacts innovation initiatives, increases the risk of data incidents, causes reputation issues, and leads to non-compliance with regulatory requirements.
“Active metadata” and “data lineage” are key cornerstones of any augmented data management initiative. While data lineage started as a simple way to describe the “journey of data”, it has now evolved and become the main tool for organizations to map, understand, and gain insights into their data pipelines.
The Ultimate Guide to Data Lineage in 2022 presents a vast array of data lineage perspectives and outlines several approaches to its
CTOs and CIOs of data-driven companies are facing one major challenge: the skyrocketing complexity of their data stack (data pipelines). Combined with a shortage of engineering talent, it limits their ability to cope with fast-paced changes, negatively impacts innovation initiatives, increases the risk of data incidents, and causes reputation issues and non-compliance with regulatory requirements.
It’s not news that effective metadata management is essential for organizations that want to be nimble and agile in today’s fast-moving business climate. However, what may not be as well known is the role that automated active metadata plays in effective metadata management.
“The world’s most valuable resource is no longer oil, but data,” stated The Economist. Data is the crux of successful business. Learn how to perform a data quality assessment to understand your data better – the good, the bad, and the money. Download now!
Internationalization is becoming a necessity for more and more businesses. As you expand your customer base, you will increasingly and inevitably start encountering data for clients outside of the United States. The data quality of address information, as most businesses are aware of already, is extremely important in terms of removing duplicates; saving in postal, shipping and product costs; as well as being able to perform effective business intelligence. This white paper will show you how to perform global address data quality with the Melissa Global Verify Component for use in Microsoft SQL Server Integration Services (SSIS). Download Now!
DataOps helps to improve processes throughout the data lifecycle – from initial collection and creation to delivery to the end user, but implementing the methodology requires effort. Download this special report to learn the ten key tenets for a successful DataOps implementation.
Cloud native application architectures let you make highly available, massively scalable, globally distributed applications. This O’Reilly technical guide for architects and developers details the most commonly used cloud native design patterns. You will learn how to build cloud native applications using APIs, data, events, and streams in both greenfield and brownfield development.
Kroger, America’s largest supermarket chain with over 2800 retail stores, has experienced tremendous growth in digital sales and e-commerce. In this video, Sriram Samu, VP of Engineering for Customer Technology at Kroger, describes the company’s journey and lessons learned from using YugabyteDB—the open source, high-performance distributed SQL database for transactional applications—to power business-critical microservices used by millions of customers across the United States.
Wells Fargo, one of America’s largest financial services companies with $1.92 trillion in assets, is delivering next generation services to savvy customers. In this video from Yugabyte’s latest Distributed SQL Summit, Chintan Mehta, CIO of Digital Technology and Innovation at Wells Fargo, shares his experiences building an effective and efficient digital ecosystem of capabilities that are safe, secure, resilient, and highly-responsive to change.
GM, one of the world’s largest automobile manufacturers, processes over 30 billion transactions per day from the company’s connected vehicles. In this video from Yugabyte’s latest Distributed SQL Summit, Logan McLeod, Director of Strategic Incubation at GM, explores cutting-edge innovation of the company’s data architecture. He reveals how a distributed SQL database allows GM to achieve continuous availability and linear scalability while enabling developers to build high-value data products quickly.
Data-driven decision-making and the ability to drive meaningful insights from increasing volumes of data is no longer just a competitive advantage: it’s a requirement for business leaders.
However, as the volume and complexity of data grows, data teams still struggle to manage data migration and maintenance, causing new and growing information gaps as well as burnout across the teams. In our ebook, find out what we heard from 450 data professionals just like you.
In this ebook, you’ll learn:
Key trends and pain points facing enterprise data teams
How to spot blind spots in your data as volumes continue to increase and how to turn those gaps into knowledge
The top three challenges of working with different data sources
Why introducing the right technology that can integrate and manage data at scale is the key to winning market, mindshare, and talent
If you are considering or planning to deploy data virtualization technology but are indecisive because you are not able to substantiate the tangible benefits of data virtualization, this report is a must read!
Users today need frictionless access to all the data, wherever it is stored, in a transactional database, a data warehouse, or a data lake. A popular new architecture that supports this approach is data fabric. This whitepaper describes how to develop data fabrics using data virtualization. It describes the benefits of this approach. A data fabric developed in this way is called a logical data fabric.
5G is significantly accelerating IoT and real-time services resulting in a huge influx of information that needs to be captured. With it brings a need for a higher performance data layer and a cutting edge data platform to take advantage of this data to provide new digital services and personalized experiences. The risk of churn for Communication Service Providers that don't rise to the challenge are huge.
While compliance requirements increase the number of strict limitations placed on data, the need for quick and easy access remains integral to the productive use of said data.
With data-driven businesses trapped in the middle, can this problem be solved while still meeting each side’s needs? With automated data access control, it can.
In this white paper, you’ll learn:
The distinction between data governance and data access control
Why passive access control models are no longer efficient or effective as cloud platform adoption accelerates
The five pillars of a modern automated data access control model
How organizations across industries are successfully meeting their data governance and analysis needs with automated data access control
The 2020 coronavirus pandemic has driven disruption and turbocharged digital transformation in banking. Disruptors are gaining ground, innovating around both customers' and businesses' needs. While a handful of leading banks are pushing ahead with their digital transformation, others are still struggling to create and execute a coherent transformation strategy. This report explores how digital technologies are changing the industry's customers, competitors, and business and tech priorities globally.
Digital banking is growing rapidly as going digital provides tremendous convenience with 24/7 services and an opportunity to reach more of the world’s population and demographics (i.e. the previously unbanked).
With 42 billion Internet of Things (IoT) devices expected to generate 80 zettabytes of data by 2025 and 5 billion mobile phone users currently generating 2.5 exabytes of data daily, it is no surprise that 95% of businesses cite the need to manage unstructured data as a serious problem for their businesses.
ROI of up to 320% and total benefits of over $9.86 million over three years for the Stardog Enterprise Knowledge Graph Platform.
Stardog commissioned Forrester Consulting to conduct a Total Economic Impact™ (TEI) study and examine the potential return on investment (ROI) enterprises may realize by deploying its Enterprise Knowledge Graph Platform.
Read this study to learn how several customers turned their data into knowledge, completed their data analytics projects faster, saved on infrastructure costs, and unlocked new business opportunities with Stardog including:
3x faster development of data analytics applications
$2.6 million in avoided infrastructure costs
$3.8 million in time savings for data scientists
$2.4 million in profit from incremental successful data analytics projects
Now, more than ever, businesses want scalable, agile data management processes that can enable faster time to market, greater self-service capabilities, and more streamlined internal processes. Download this report for seven key steps to designing and promoting a modern data architecture that meet today’s business requirements.
Secure your Redshift data warehouse in minutes! Learn more about Auditing, Monitoring, Network Access Control, Encryption and other topics for Amazon Redshift, in our AWS Redshift Security Guide.
Ransomware is getting smarter and the attackers shrewder. How well could you recover your critical data and processes if you became a target?
Learn how to secure your Snowflake data cloud in minutes. This guide will provide you an overview of Snowflake Security and its features as well as a practical guide for using them to their full potential.
An at-a-glance guide highlighting 37 trends and statistics impacting businesses today.
Does your ransomware protection plan contain the 5 essential components needed to be effective against attackers?
With all that’s happened in the past 2 years, it is often observed that there may be more risk in staying with the status quo than moving forward and trying something new. Today, agility, enabled by modern methodologies, such as DataOps and DevOps, and the use of new technologies, like AI and machine learning, is critical for addressing new challenges and flexibly pivoting to embrace new opportunities. As we get further into 2022, the annual Data Sourcebook issue puts the current data scene in perspective and looks under the covers of the key trends in data management and analytics. Download your copy today.
Protecting your data against ransomware involves putting together a multi-layer defensive plan all the way from thwarting such attacks to recovering quickly in the event of a breach. Download this checklist to guide you in building your own comprehensive data protection plan.
Learn to build and deploy production-ready serverless apps and services with Java using AWS Lambda
This hands-on guide includes hands-on tutorials and exercises for building, packaging, testing, and deploying Java-based Lambda code. Ultimately, developers will learn how serverless development can dramatically simplify how they build and scale their applications.
In this complete, 10-chapter book you will learn:
The fundamentals of serverless and functions as a service, using the AWS Lambda platform
How to build and package Java-based Lambda code and dependencies
How to create serverless applications by building a serverless API and data pipeline
How to automate testing for your serverless applications
Advanced techniques for building production-ready applications
In this whitepaper you’ll discover 3 of the coolest ways you can use Talend and AWS to maximize your data’s value in the fastest, most cost-effective manner possible. Plus, learn from AstraZeneca and how they built a data lake using AWS and Talend successfully.
The Future of Data
A special Raconteur report published in The Times
They say that data is the “new oil,” but companies are going to have to make sure they are drilling for it responsibly or risk damaging repercussions.
This Future of Data report explores the opportunity for companies to craft their own data strategies, the importance of trust and transparency, the power of decision intelligence, and whether companies really need a chief data officer.
Get your free report.
The modern data engineering technology market is dynamic, driven by the tectonic shift from on-premise databases and BI tools to modern, cloud-based data platforms built on lakehouse architectures.
More than the on-premises market that preceded it, the cloud data technology market is evolving rapidly, and spans a vast set of open source and commercial data technologies, tools, and products. At the same time, organizations are adopting multiple technologies to keep up with the scale, speed, and use cases that today’s
data environment demands.
To remain competitive and maximize the value of their data – including sensitive data – organizations are developing DataOps functions and frameworks to varying degrees. DataOps tools and processes enable continuous and automated delivery of data to power BI, analytics, data science, and data-powered products.
The 2022 Data Engineering Survey examined the changing landscape of data engineering and operations challenges, tools, and opportu
Per Gartner, ”Innovations continue to enter the field of data management every day. Their potential benefits are numerous, but challenging to understand and track.” The Gartner Hype Cycle for Data Management, 2021, assesses more than 30 different categories of data management technologies -- including data lakes, multi-model DBMS, and logical data warehouses -- based on their business benefit and years to mainstream adoption and maturity.
The report helps data and analytics leaders plan ahead and make informed decisions relative to emerging and maturing data management technologies.
This Deep Dive report by Eckerson Group takes a look at four cloud data platforms, and defines the index-driven data lake platform in the context of evolving cloud data platforms.
Understanding Cloud Data Platforms
An index-driven data lake platform helps enterprises increase the scale of their log analytics and BI workloads without incurring too many expensive compute cycles. It transforms, queries, and searches data objects to drive effective log analytics. With this approach, ITOps, DevOps, or CloudOps engineers can analyze more IT logs faster in order to manage the performance and reliability of their IT infrastructure.
Going beyond fundamentals, this report will compare the capabilities of ChaosSearch and three other cloud data platforms in the following categories:
Performance and scale
Ease of use
The cost of downtime is higher than ever, amplifying pressure on IT Ops, NOCs, and DevOps to minimize outages.
In our just-released collection of customer case studies, you will see how BigPanda helped each company turn their "what-if" into a reality.
This report will be of value to Infrastructure and Operations teams evaluating how AIOps can improve monitoring, service management and automation tasks with AI-powered anomaly detection, diagnostic information, event correlation, and root cause analysis (RCA).
The need for speed drives enterprises to adopt clouds, containers, micro-services and continuous delivery. The rise of DevOps has created a culture of optionality within organizations. With speed and optionality comes tremendous operational challenges. IT Ops, Site Reliability, DevOps teams have to deal with overwhelming alert volumes, continuous production changes, and dynamic service topologies.
Without enrichment, your AIOps tools will struggle to make sense of incoming data. The AI/ML technology powering your AIOps tools will struggle to create high-quality incidents. Incidents that lack valuable, actionable context result in frequent, long and painful incidents and outages. You will be no better off than you were before your AIOps investment.
Thanks to an explosion of data, exponential increases in computing power and storage capacity, and better algorithms, artificial intelligence (AI) and machine learning (ML) capabilities are poised to revolutionize business processes. These intelligent capabilities will not only underpin increased automation and process optimization but also improve business results with better and faster planning, decision making, and risk forecasting.
Download a free copy of this hands-on guide, developers will learn to build production-ready serverless apps and services on Google Cloud Run
Today’s digital businesses and those moving towards digitization are rapidly embracing event-driven, in addition to historic batch-driven IT architectures. Event-enabling digital enterprises brings new capabilities, but also brings exponential growth in the streaming data volumes to be handled. This new survey offers insights on how enterprises use event-driven architectures, the volumes of streaming data they face, and the approach they are taking with respect to designing streaming applications and deploying streaming analytics.
The cloud sits at the heart of countless innovations and has transformed the way many organizations do business. It has more than proven itself for over a decade through market fluctuations, business model changes and even a pandemic by delivering the flexibility, performance, scalability, and robustness needed to keep companies running with increasingly greater efficiency. Remote work has rapidly become widespread, and the cloud — with its anywhere, from any device, delivery approach — is a critical component of supporting this new normal.
How Dataware is leading the data revolution and solving data complexity by decoupling data from apps.
Discover how Dataware makes it possible for apps and users to collaboratively manage data without creating new silos, copies, and integrations. In this white paper, learn how Dataware blends the benefits of all the other data strategies into a unified approach for building data-centric apps.
Why other data and application integration solutions and strategies just don’t cut it
What makes Dataware different from Data Warehouses, Data Lakes, MDM, Knowledge Graphs, etc.
How Dataware solves all the pain points that make app-centricity a problem
4 quick ways to begin leveraging Dataware to solve real-world problems
o User-managed data collaboration platform
o Application augmentation (CRM, ERP, HRMS)
o Digital Integration Hub / Data Fabric
o Cross application persistence and controls as a service
How this new category simplifies data collaboration for maximized business agility.
Collaboratively manage data without creating new silos, copies, or integrations.
Data is one of the most valuable resources for an organization. Yet, it's difficult to extract intelligence from it because data management and app development are traditionally centered around applications. The result is data silos, lack of control, and numerous integrations.
Discover why Dataware is different from other data management solutions and how moving to data centricity can improve your IT capacity -- bringing agility to your business.
In this eBook, learn how Cinchy's Dataware Platform can help you:
Eliminate the need for data integration every time you buy or build applications
Reduce the time and money wasted on data integration
Manage data as a linked network allowing for real-time data collaboration
In keeping up with the demands of a digital economy, organizations struggle with availability, scalability, and security. For users of the world’s most popular enterprise database, Microsoft SQL Server, this means evolving to deliver information in a hybrid, multi-platform world. While the data platform has long been associated with the Windows Server operating system, many instances can now be found running within Linux environments. The increasing movement toward cloud, which supports greater containerization and virtualization, is opening platform independence in ways not previously seen, and enterprises are benefitting with greater flexibility and lower costs. Download this special white paper today to learn about the era of the amplified SQL Server environment supported by the capabilities of Linux and the cloud.
From the rise of hybrid and multi-cloud architectures to the impact of machine learning, automation, and containerization, database management today is rife with new opportunities and challenges. Download this special report today for the top 9 strategies to overcome performance issues.
PostgreSQL is an incredibly reliable open-source database technology that continues to grow in popularity with its users whether its supporting enterprise-grade workloads and commercial databases. It’s flexible, you can use it for SQL and NoSQL workloads, and has high availability. On-premises PostgreSQL deployments make it difficult to harness the true potential of these databases.
Migrating PostgreSQL to a cloud platform like Amazon Aurora can deliver a host of benefits including increased flexibility, greater capacity, improved security and automation to start. It also requires a thorough understanding of your current on-premises databases, your application requirements, your database migration goals, and your technical resources.
Download Datavail’s white paper to discover why your PostgreSQL database should live on Amazon Aurora and learn:A brief overview of AWS & Amazon Aurora
A brief overview of AWS & Amazon Aurora
15 benefits of an Amazon Aurora migration
In a hybrid, multi-cloud world, data management must evolve from traditional, singular approaches to more adaptable approaches. This applies to the tools and platforms that are being employed to support data management initiatives – particularly the infrastructure-as-a-service, platform-as-a-service, and SaaS offerings that now dominate the data management landscape. Download this special report today for new solutions and strategies to survive and thrive in the evolving hybrid, multi-cloud world.
Object storage, often referred to as object-based storage, is a data storage architecture for handling large amounts of unstructured data. Unstructured data is data that does not conform to, or cannot be organized easily into a traditional relational database with rows and columns. Today’s Internet communications data—email, videos, photos, web pages, audio files, sensor data, and other types of media and web content (textual or non-textual)—is largely unstructured.
Microsoft Office 365 is used widely and gaining new customers every day. While Microsoft Office 365 does offer a variety of system availability and data protection capabilities, it doesn’t really satisfy all of today’s stringent data protection, disaster recovery, legal and compliance requirements. Data can be damaged or lost based on several unforeseen situations, including accidental changes and deletions, as well as malicious activities like viruses and ransomware. And there are rare situations where Office 365 applications and data are just not available, impacting business performance, customer service and staff productivity.
Exponential data growth is something every IT team faces. It’s natural to worry about exceeding capacity, wondering how a solution will scale or perform over time. The best approach is to use a software solution that’s not limited by hardware, allowing you to turn generic hardware into a robust, infinitely-scalable storage pool.
To minimize cloud complexities and costs, there are three best practices organizations can follow when storing their backup data in the cloud.
The decision to adopt the cloud has been made. Your company exercised caution by electing to first adopt the foundational compute and storage services that AWS and Azure offer to provide the fastest journey to the cloud. It also took the appropriate next steps of managing user access to mitigate cost overruns in the cloud. Now it’s time to start moving your company’s applications and backup data to the cloud.
Ransomware has become a ‘cyber-pandemic’ that shows no signs of diminishing anytime soon. Your ransomware recovery capabilities must be up to the challenge, as these attacks have gone well beyond just another cybercrime to becoming a threat to our society as well.
Just recently, we have seen some high-profile ransomware attacks lead to serious shortages in oil, gas and meat supplies as well as interruptions to transportation systems and countrywide health systems. And those are only a small part of the ransomware iceberg that is visible as many organizations avoid publicly disclosing that they have been compromised.
Multiple backup solutions, clouds, and cloud storage tiers can all lead to escalating cloud storage costs and complexity. These, in turn, hamper an organization’s ability to perform a DR in the cloud. Three new best practices exist that account for these new variables. By adopting them, organizations better position themselves to more quickly perform DR in the cloud while incurring lower costs.
Protecting enterprise data in a complex IT environment is challenging, time-consuming, cumbersome and often incomplete. First of all, it’s not uncommon for backup and recovery technologies to be complicated to install, often requiring help from the vendor’s
professional services staff, which adds to your total cost of ownership (TCO). Then learning to manage and maintain them for your
ongoing backup and recovery needs can take a lot of time, effort and patience — especially if your environment has multiple operating systems and applications and is heavily virtualized.
Inside the report, organizations share their:
Number and types of Kubernetes workloads deployed
Concerns and challenges around adopting Kubernetes
Multi-cloud adoption timelines
Serverless adoption strategies
Analytic vs. transactional database needs & usage
Production environment preferences
This new book from O’Reilly will teach developers, architects, and devops teams how to build, optimize, and manage applications that run on CockroachDB. It includes specific guidance for anyone transitioning from a monolithic database (e.g., MySQL or PostgreSQL) to a distributed architecture, as well as practical examples for anyone more familiar with NoSQL systems.
Since 2016, the State of AI and Machine Learning Report has given insight into the adoption, implementation and scale of AI across industries. This large-organization study of senior business leaders and technologists details where organizations are within their AI journey — from the types of data they leverage to the tools they use and budgets they have.
Overall, this report will help you understand the broader context of your AI work, what your peers are experiencing, and what dials to turn for AI success.
Download the report to learn:
How AI budgets have increased YoY
Why many are accelerating their AI strategy as a result of COVID-19 in 2020 and into 2021
Why companies are reporting a higher commitment to data security and privacy
Many companies ingest more than a petabyte of data but struggle to turn it into high-quality training data for machine learning algorithms that fuel core business. When coupling AIOps with professional services to guide the entire AI-creation journey, AI and ML will increase efficiencies and innovation around volume, quality, and speed requirements for training data.
Download the AIOps for Business Leaders white paper to learn about:
How AIOps with training data can power your tech stack, which translates to scalable AI
Why machine learning and the big data flywheel will create momentum that takes programs from experiment, to program, to core product
Key data-labeling resources for AIOps
There's a saying of garbage in, garbage out. It’s common knowledge that every machine learning solution needs a good algorithm powering it, but what gets far less press is what actually goes into these algorithms: the training data itself. Your model is only as good as the data it's trained on.
Pulling from over 20 years of experience, we break down how to approach training data, starting with raw data and annotating and labeling it so that it can be used to power the most ambitious projects. It's how we help some of the most innovative companies in the world. This guide will give you a few of the lessons we’ve learned along the way.
In the Essential Guide to Training Data we’ll cover everything you need to know about creating the training data necessary to drive successful machine learning projects, including:
Why having a lot of big data isn’t the same as having labeled data
How to determine which labels to use to evaluate your success
Where to find some great open datas
In an increasingly competitive and noisy business landscape, Managed Service Providers (MSPs) need to stand out – and not just for the expertise of their IT consultants. Security, compliance, and time-to-productivity issues are very much on every customer’s mind, and the MSPs that come out on top in today’s marketplace will be those that deliver on those issues.
Even as employees return to the office, most organizations say they’ll continue to facilitate remote work through a hybrid workforce. This is an unprecedented period – one ripe with opportunities to harness global talent, but also serious IT risks. That’s why CISOs everywhere need a clear, effective plan for scaling and securing a blended workforce of both in-office and remote employees.
Data management is changing. It’s no longer about standing up databases and populating data warehouses; it’s about making data the constant fuel of the enterprise, accessible to all who need it. As a result, organizations need to be able to ensure their data is viable and available. Download this special report for the key approaches to developing and supporting modern data governance approaches to align data to today’s business requirements.
Today’s emerging architecture is built to change, not to last. Flexible, swappable systems, designed to be deployed everywhere and anywhere, and quickly dispensed at the end of their tenure, are shifting the dynamics of application and data infrastructures. The combination of containers and microservices is delivering a powerful one-two punch for IT productivity. At the same time, the increasing complexity of these environments brings a new set of challenges, including security and governance, and orchestration and monitoring. Download this special report for guidance on how to successfully leverage the flexibility and scalability that containers and microservices offer while addressing potential challenges such as complexity and cultural roadblocks.
Database architecture is an important consideration in any MarTech and AdTech strategy. If you’re an application developer or technical executive, learn why Aerospike’s innovative Hybrid Memory Architecture is the defacto standard among the world’s leading advertising and marketing technology organizations.
Adform replaces Cassandra with the Aerospike database to achieve predictable low latency at scale for its multi-screen marketing platform.
The Advertising industry historically has been built upon cookie technology whereby advertisers can glean very detailed user profile information in order to segmentation and target advertisements, profitably. However, with Google’s proposal to eliminate 3rd party cookies and compliance mandates notably in the US and EMEA, the need to create a new, better alternative to cookie technology is upon us.
The Trade Desk, the world's largest independent programmatic advertising DSP, needed to migrate from cloud back to on-prem for one of its largest Aerospike clusters. There were multiple catalysts for the change including a new business requirement, a new, tailor-built site, as well as risk and capacity challenges. This session will uncover the findings and methods used to gain confidence in the move.
Leading Ad Tech companies use the Aerospike non-relational NoSQL database to improve customer engagement, campaign effectiveness and top-line results.
The database is no longer just a database. It has evolved into the vital core of all business activity; the key to market advancement; the essence of superior customer experience. In short, the database has become the business. What role does the new data environment—let’s call it “the era of data races”—play in moving enterprises forward? Download this special report to learn about the ways emerging technologies and best practices can support enterprise initiatives today.
Companies embarking on significant Hadoop migrations have the opportunity to advance their data management capabilities while modernizing their data architectures with cloud platforms and services. Consequently, having a comprehensive approach reduces the risk of business disruption and/or the potential for data loss and corruption. Download this special eGuide today to learn the do’s and don’ts for migrating Hadoop data to the cloud safely, securely, and without surprises, and key architecture strategies to follow.
To successfully make the journey to a data-driven enterprise, businesses are under pressure to extract more value from their data in order to be more competitive, own more market share and drive growth. This means they have to make their data work harder by getting insights faster while improving data integrity and resiliency, leverage automaton to short cycle times and reduce human error, and adhere to data privacy regulations. DataOps opens the path to delivering data through the enterprise as its needed, while maintaining its quality and viability. In this thought leadership paper, we will provide perspectives on the advantages DataOps gives to stakeholders across the enterprise, including database administrators, data analysts, data scientists, and c-level executives.
With more data than ever flowing into organizations and stored in multiple cloud and hybrid scenarios, there is greater awareness of the need to take a proactive approach to data security. Download this special report for the top considerations for improving data security and governance from IT and security leaders today.
There are many types of disruption affecting the data management space, but nowhere will the impact be more substantial than at the edge. Leading operations moving to the edge include smart sensors, document and data management, cloud data processing, system backup and recovery, and data warehouses. Download this special report for the key transformational efforts IT leaders need to focus on to unlock the power of IoT and the edge.
For organizations with growing data warehouses and lakes, the cloud offers almost unlimited capacity and processing power. However, transitioning existing data environments from on-premises systems to cloud platforms can be challenging. Download this special report for key considerations, evolving success factors and new solutions for enabling a modern analytics ecosystem.
Bringing knowledge graph and machine learning technology together can improve the accuracy of the outcomes and augment the potential of machine learning approaches. With knowledge graphs, AI language models are able to represent the relationships and accurate meaning of data instead of simply generating words based on patterns. Download this special report to dive into key uses cases, best practices for getting started, and technology solutions every organization should know about.
The adoption of hybrid and multicloud environments is accelerating; boosted by a mounting urgency for enterprises to digitally transform into more efficient and agile operators. At the same time, the challenges of managing, governing, security and integrating data are growing in step. Download this special DBTA report to navigate the key data management solutions and strategies for surviving and thriving in the growing hybrid, multi-cloud world.
The popularity of the Oracle Unlimited Licensing Agreement (ULA) has grown over the last decade and doesn’t seem likely to decrease. Whether your organization simply needs more licenses (more seats) or you’ve been audited and a ULA is being recommended to avoid this happening again in the future, it’s absolutely critical to understand when a ULA is a smart investment, when it’s a terrible idea, and how to best navigate the pitfalls of the complex world of licensing.
From the rise of hybrid and multi-cloud architectures, to the impact of machine learning and automation, the business of data management is constantly evolving with new technologies, strategies, challenges, and opportunities. The demand for fast, wide-ranging access to information is growing. At the same time, the need to effectively integrate, govern, protect, and analyze data is also intensifying. Download this special report for the top trends in data management to keep on your radar for 2021.
DataOps is now considered to be one of the best ways to work toward a data-driven culture and is gaining ground at enterprises hungry for fast, dependable insights. Download this special report to learn about the key technologies and practices of a successful DataOps strategy.
Los data warehouses en la nube son un elemento fundamental de la transformación digital porque no requieren ningún tipo de hardware, son infinitamente escalables y solo se pagan los recursos de datos que se consumen. Sin embargo, eso no es todo, hay más, y la cosa se complica.
Azure Synapse, Amazon Redshift, Google Big Query y Snowflake, todos ellos, requieren integración de datos en tiempo real y automatización del ciclo de vida para alcanzar su máximo potencial. El problema es que estas dos capacidades no están incluidas, por lo que es necesario codificar a mano las secuencias de comandos de ETL para compensarlo. En consecuencia, los desarrolladores se ven limitados y las transferencias de datos, restringidas; lo que afecta negativamente a su retorno de inversión inicial.
Les data warehouses dans le cloud sont au cœur de la transformation digitale, car ils ne nécessitent aucun hardware, sont infiniment redimensionnables, et parce que vous payez uniquement pour les ressources de données que vous consommez. Mais ce n'est pas tout.
Les systèmes tels qu'Azure Synapse, Amazon Redshift, Google BigQuery et Snowflake exigent tous une intégration des données en temps réel et une automatisation du cycle de vie pour atteindre leur plein potentiel. Pour autant, ces deux fonctionnalités ne sont pas incluses dans ces systèmes, ce qui oblige à coder manuellement des scripts ETL pour y pallier. Par conséquent, vos développeurs sont soumis à des contraintes et vos transferts de données sont restreints, ce qui s'avère compromettant pour votre ROI initial.
Cloud Data Warehouses sind das Rückgrat der Digitalisierung, denn sie benötigen keine Hardware und sind unendlich skalierbar. Außerdem brauchen Sie nur für die Ressourcen zu zahlen, die Sie tatsächlich nutzen. Doch das ist nur die halbe Wahrheit.
Datenintegration und eine Automatisierung des gesamten Daten-Lebenszyklus, damit Sie ihre Stärken voll ausspielen können. Allerdings gehören diese beiden Möglichen bei keinem dieser Tool zum Funktionsumfang. Es bleibt Ihnen daher nichts Anderes übrig, als die Lücken mit selbstgeschriebenen ETL-Skripts zu schließen, was Ihre Entwickler und Datentransfers stark einschränkt und wodurch der anfänglich avisierte ROI in weite Ferne rückt.
The move to modern data architecture is fueled by a number of converging trends – the rise of advanced data analytics and AI, the Internet of Things, edge computing, and cloud. Both IT and business managers need to constantly ask whether their data environments are robust enough to support the increasing digitization of their organizations. Over the past year, requirements for data environments have been driven largely by cost considerations, efficiency requirements, and movement to the cloud. Download this special report for emerging best practices and key considerations today.
Now, more than ever, the ability to pivot and adapt is a key characteristic of modern companies striving to position themselves strongly for the future. Download this year’s Data Sourcebook to dive into the key issues impact enterprise data management today and gain insights from leaders in cloud, data architecture, machine learning, data science and analytics.
This study, sponsored by Quest Software, includes the views and experiences of 285 IT decision makers, representing a fairly broad sample of company types and sizes. The survey found that databases continue to expand in size and complexity, while at the same time, more enterprises are turning to cloud-based resources to keep information highly available.
Melissa has a variety of tools available to clean, validate and enhance the Contact dimension in your SQL Server data warehouse. Specifically, Melissa’s suite of SSIS Data Quality Components can be leveraged for this task. The Melissa SSIS components are plug and play; you simply drag and drop the components onto the Data Flow, configure the component properties, and you are ready to go. There is no coding required.
The critical role of data as fuel for the growing digital economy is elevating data managers, DBAs, and data analysts into key roles within their organizations. In addition, this rapid change calls for a three-pronged approach that consists of expanding the use of more flexible cloud computing strategies, growing the automation of data environments, and increasing the flow of data and collaboration through strategies such as DevOps and DataOps. Download this special report today to better understand the emerging best practices and technologies driving speed and scalability in modern database management.
There’s a wide range of reasons why many organizations are deciding to modernize their data architectures. But they all agree on one thing: by using data more effectively, more widely, and more deeply, they can improve and optimize business and decision-making processes that will help them stay competitive in the emerging digital economy.
A strong data management foundation is essential for effectively scaling AI and machine learning programs to evolve into a core competence of the business. Download this special report for the key steps to success.
Today’s enterprises rely on an assortment of platforms and environments, from on-premise systems to clouds, hybrid clouds and multi-clouds. This calls for modern data management practices that leverage emerging technologies, providing enterprise decision managers with the tools and insights they need to improve and transform their businesses. Download this special report for best practices in moving to modern data management standards to ensure the integration and governance of valuable data sources within today’s diverse environments.
Emerging agile technologies and techniques are leading to new ways of accessing and employing data. At the same time, the increasing complexity of these environments is creating additional challenges around security and governance, and orchestration and monitoring, which is particularly evident with the rise of hybrid, multi-cloud enterprise environments. Welcome to the era of the digitally enriched platform. Download this special report today to dive into emerging technologies and best practices.
AIOps market is set to be worth $11B by 2023 according to MarketsandMarkets. Originally started as automating the IT operations tasks, now AIOps has moved beyond the rudimentary RPA, event consolidation, noise reduction use cases into mainstream use cases such as root causes analysis, service ticket analytics, anomaly detection, demand forecasting, and capacity planning. Join this session with Andy Thurai, Chief Strategist at the Field CTO ( thefieldcto.com) to learn more about how AIOps solutions can help the digital business to run smoothly.
A challenge of ML is operationalizing the data volume, performance, and maintenance. In this session, Rashmi Gupta explains how to use tools for orchestration and version control to streamline datasets. She also discusses how to secure data to ensure that production control access is streamlined for testing.
As market conditions rapidly evolve, DataOps can help companies produce robust and accurate analytics to power the strategic decision-making needed to sustain a competitive advantage. Chris Bergh shares why, now more than ever, data teams need to focus on operations, not the next feature. He also provides practical tips on how to get your DataOps program up and running quickly today.
Traditional methodologies for handling data projects are too slow to handle the teams working with the technology. The DataOps Manifesto was created as a response, borrowing from the Agile Manifesto. This talk covers the principles of the DataOps Manifesto, the challenges that led to it, and how and where it's already being applied.
The ability to quickly act on information to solve problems or create value has long been the goal of many businesses. However, it was not until recently that new technologies emerged to address the speed and scalability requirements of real-time analytics, both technically and cost-effectively. Attend this session to learn about the latest technologies and real-world strategies for success.
Each week, 275 million people shop at Walmart, generating interaction and transaction data. Learn how the company's customer backbone team enables extraction, transformation, and storage of customer data to be served to other teams. At 5 billion events per day, the Kafka Streams cluster processes events from various channels and maintains a uniform identity of each customer.
To support ubiquitous AI, a Knowledge Graph system will have to fuse and integrate data, not just in representation, but in context (ontologies, metadata, domain knowledge, terminology systems), and time (temporal relationships between components of data). Building from ‘Entities’ (e.g. Customers, Patients, Bill of Materials) requires a new data model approach that unifies typical enterprise data with knowledge bases such as industry terms and other domain knowledge.
We are at the juncture of a major shift in how we represent and manage data in the enterprise. Conventional data management capabilities are ill equipped to handle the increasingly challenging data demands of the future. This is especially true when data elements are dispersed across multiple lines of business organizations or sourced from external sites containing unstructured content. Knowledge Graph Technology has emerged as a viable production ready capability to elevate the state of the art of data management. Knowledge Graph can remediate these challenges and open up new realms of opportunities not possible before with legacy technologies.
Knowledge Graphs are quickly being adopted because they have the advantages of linking and analyzing vast amounts of interconnected data. The promise of graph technology has been there for a decade. However, the scale, performance, and analytics capabilities of AnzoGraph DB, a graph database, is a key catalyst in Knowledge Graph adoption.
Though MongoDB is capable of incredible performance, it requires mastery of design to achieve such optimization. This presentation covers the practical approaches to optimization and configuration for the best performance. Padmesh Kankipati presents a brief overview of the new features in MongoDB, such as ACID transaction compliance, and then move on to application design best practices for indexing, aggregation, schema design, data distribution, data balancing, and query and RAID optimization. Other areas of focus include tips to implement fault-tolerant applications while managing data growth, practical recommendations for architectural considerations to achieve high performance on large volumes of data, and the best deployment configurations for MongoDB clusters on cloud platforms.
Just as in real estate, hybrid cloud performance is all about location. Data needs to be accessible from both on-premise and cloud-based applications. Since cloud vendors charge for data movement, customers need to understand and control that movement. Also, there may be performance or security implications around moving data to or from the cloud. This presentation covers these and other reasons that make it critical to consider the location of your data when using a hybrid cloud approach.
What if your business could take advantage of the most advanced AI platform without the huge upfront time and investment inherent in building an internal data scientist team? Google’s Ning looks at end-to-end solutions from ingest, process, store, analytics, and prediction with innovative cloud services. Knowing the options and criteria can really accelerate the organization's AI journey in a quicker time frame and without significant investment.
After 140+ years of acquiring, processing and managing data across multiple business units and multiple technology platforms, Prudential wanted to establish an enterprise wide data fabric architecture to allow data to be available where and when its needed. Prudential chose data virtualization technology to create the logical data fabric that spans their entire enterprise.
The pace of technology change is continuing to accelerate and organizations have no shortage of tool and application options. But while many are modernizing tool infrastructure and ripping out legacy systems, the data that powers new tools still presents difficult and seemingly intractable problems. Seth Earley discusses approaches for bridging the gap between a modernized application infrastructure and ensuring that quality data is available for that infrastructure.
As business models become more software driven, the challenge of maintaining reliable digital services and delightful customer experiences, as well as keeping those services and customer data safe is a "continuous" practice. It’s particularly important now, when the COVID-19 global pandemic has created a discontinuity in digital transformation and many industries have been forced entirely into a digital business model due to social distancing requirements. Bruno Kurtic discusses the impact of the pandemic on industries and digital enterprises leverage continuous intelligence to transform how they build, run, and secure their digital services and use continuous intelligence to outmaneuver their competition.
In this session, Lee Rainie discusses public attitudes about data, machine learning, privacy, and the role of technology companies in society—including in the midst of COVID-19 outbreak. He covers how these issues will be factors shaping the next stages of the analytics revolution as politicians, regulators, and civic actors start to focus their sights on data and its use.
From the rise of hybrid and multi-cloud architectures, to the impact of machine learning and automation, database professionals today are flush with new challenges and opportunities. Now, more than ever, enterprises need speed, scalability and flexibility to compete in today’s business landscape. At the same time, database environments continue to increase in size and complexity; crossing over relational and non-relational, transactional and analytical, and on-premises and cloud sites. Download this report to dive into key enabling technologies and evolving best practices today.
With constantly evolving threats and an ever-increasing array of data privacy laws, understanding where your data is across the enterprise and properly safeguarding it is more important today than ever before. Download this year’s Cybersecurity Sourcebook to learn about the pitfalls to avoid and the key approaches and best practices to embrace when addressing data security, governance, and regulatory compliance.
Today’s organizations want advanced data analytics, AI, and machine learning capabilities that extend well beyond the power of existing infrastructures, so it’s no surprise that data warehouse modernization has become a top priority at many companies. Download this special report to under how to prepare for the future of data warehousing, from increasing impact of cloud and virtualization, to the rise of multi-tier data architectures and streaming data.
Improving data quality is one of the top 50 ways businesses can save money and remain successful during economic downturns. With a bumpy road ahead, now is the perfect time for developers, data architects and data stewards to review the 7 Cs of Data Quality and build a game plan to eliminate poor quality or inconsistent customer data and improve data accessibility and usability.
As organizations are more likely than ever to be audited by their software vendor, one of the top questions we are asked is, “How at risk is my organization in the event of an Oracle audit?” In this eBook, you will be able to quantify your organization’s Oracle audit risk through traditional risk calculating practices in a risk matrix.
Rapid data collection is creating a tsunami of information inside organizations, leaving data managers searching for the right tools to uncover insights. Knowledge graphs have emerged as a solution that can connect relevant data for specific business purposes. Download this special report to learn how knowledge graphs can act as the foundation of machine learning and AI analytics.
It’s no surprise then that adoption of data lakes continues to rise as data managers seek to develop ways to rapidly capture and store data from a multitude of sources in various formats. However, as the interest in data lakes continues to grow, so will the management challenges. Download this special report for guidelines to building data lakes that deliver the most value to enterprises.
While cloud is seen as the go-to environment for modernizing IT strategies and managing ever-increasing volumes of data, it also presents a bewildering array of options. Download this special report for the nine points to consider in preparing for the hybrid and multi-cloud world.
DataOps is poised to revolutionize data analytics with its eye on the entire data lifecycle, from data preparation to reporting. Download this special report to understand the key principles of a DataOps strategy, important technology, process and people considerations, and how DataOps is helping organizations improve the continuous movement of data across the enterprise to better leverage it for business outcomes.
This book will discuss the ins and outs of Oracle’s licensing web, clarifying the murky points. We’ll also go in-depth on the petrifying and dreaded “Oracle Audit,” providing clear advice on how to prepare for it; advice that includes calling in the cavalry when needed, to protect you from Oracle’s clutches.