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Today, data is critical to every organization and every department within every organization. Yet, all the disparate systems for handling it are creating new challenges. Joe Caserta, founder and president of Caserta, a technology consulting and implementation firm focused on data and analytics strategies and solutions, recently discussed the current state of data integration and what is needed to overcome today's problems.

Posted October 31, 2019

Semantically enabled machine reasoning is an efficient form of AI that can help with basic tenets like data quality and completeness, and that can scale to provide automated pattern recognition for decision support in mission-critical applications. AI - delivered by semantic technologies - opens a wealth of opportunity to improve efficiency in all types of enterprise business applications. In this very powerful new era, errors are reduced, data insights are more sophisticated and quickly gleaned, and staff is freed to focus on excellent service, new product development, and overall business growth.

Posted October 31, 2019

What if the reason the BI implementation was failing was not the users or their willingness to work together, but that they were using the wrong analytics platform

Posted October 31, 2019

On June 11, 2019, the National Institute of Standards & Technology (NIST) released an updated white paper, detailing several action plans (https://csrc.nist.gov) for reducing software vulnerabilities and cyber-risk. In the paper, titled "Mitigating the Risk of Software Vulnerabilities by Adopting a Secure Software Development Framework (SSDF)," NIST provided organizations with solid guidelines to avoid the nasty—not to mention expensive—consequences of a data breach.

Posted October 31, 2019

The data warehouse and data lake each solve different business problems and impose their own unique challenges.Organizations shouldn't write off data warehouses—as they evolve, they are taking on new roles in digital enterprises. Data lakes may add a great deal of flexibility to an enterprise data strategy, but they are supported by fast-breaking technologies that require constant vigilance.

Posted October 31, 2019

There is a sea change underway in enterprise architecture. Just a few years ago, enterprise administrators were fearful of the security implications of trusting an outside provider to protect their data assets. Although security is still a cloud concern—one which predominates at the time of cloud migration, and even grows stronger post-implementation—the use of cloud platforms has gained widespread acceptance.

Posted October 31, 2019

As more companies continue to rely on interconnected networks, virtualized cloud services, and IoT technologies, the potential for downtime and its costs will only rise. By achieving true network resilience, companies can focus on maintaining their services, removing single points of failure and having a plan to bring the network back up to continue normal operations—before it costs them.

Posted October 01, 2019

Data continues to grow in volume, variety, and velocity, resulting in new data management technologies. Recently, Deepti Srivastava, product manager for Cloud Spanner at Google Cloud, discussed how database requirements are evolving and how Google's Cloud Spanner is advancing a relational-NoSQL-convergence approach by giving customers the combined benefits of relational database structure with non-relational horizontal scale.

Posted October 01, 2019

Digital transformation. Infrastructure modernization. Global data center demands. All these forces and more are driving enterprises around the world to seek out next generation cloud-based technologies for a wide range of applications—even those most critical to their business. In reality, however, migrating to the cloud or any other modern architecture is not as easy as it sounds.  

Posted October 01, 2019

DevOps is now widely accepted in application development because, by introducing a culture of collaboration and cooperation between development and operations teams, it enables features to be released faster to end users. As DevOps grows, there is a corresponding need to ensure the database is included so that the entire development process is seamless and free of bottlenecks.

Posted October 01, 2019

Oracle has identified a need for "augmented" analytics, leveraging machine learning and AI throughout the analytics process to help drive up the impact and value of data, and enable knowledge workers to uncover more insights. Recently, Bruno Aziza, group VP, Oracle Analytics, described this new phase in analytics, the role that cloud plays in making it possible, and what the capabilities will enable for customers.

Posted October 01, 2019

Best Streaming Solution

Posted August 14, 2019

A relational database is a set of formally described tables from which data can be accessed or reassembled in many different ways without having to reorganize the database tables. The standard user and application programming interface (API) of a relational database is the Structured Query Language (SQL). SQL statements are used both for interactive queries for information from a relational database and for gathering data for reports.

Posted August 14, 2019

Look to NoSQL for fast, highly scalable access to free-form data. This comes with a few costs, like consistency of reads and other safeguards common to SQL databases. But for many applications, those safeguards may well be worth trading for what NoSQL offers.

Posted August 14, 2019

Best MultiValue Database

Posted August 14, 2019

The Internet of things (IoT) is the inter-networking of physical devices, vehicles (also referred to as "connected devices" and "smart devices"), buildings, and other items embedded with electronics, software, sensors, actuators, and network connectivity which enable these objects to collect and exchange data.

Posted August 14, 2019

In-memory databases and technologies enable decision makers to get to the information they are seeking rapidly and more readily. While in-memory technology has been on the market for many years, today, the demand for intelligent, interactive experiences requires back-end systems and applications operating at high performance, and incorporating movement and delivery of data faster than ever before.

Posted August 14, 2019

After more than 15 years, there is still probably no technology more aligned with advent of big data than Hadoop. The Apache Hadoop framework allows for the distributed processing of large datasets across compute clusters, enabling scale up from single commodity servers to thousands of machines for local computing and storage. Designed to detect and handle failures at the application layer, the framework supports high availability.

Posted August 14, 2019

Best DBA Solution

Posted August 14, 2019

Best Database Performance Solution

Posted August 14, 2019

Social media, the Internet of Things, demands for mobile access, and real-time insights are just some of the factors that have increased the pressure on organizations to change how data is managed. And as a result there have never been so many data management choices to deal with it all.

Posted August 14, 2019

Best Database Development Solution

Posted August 14, 2019

With long downtimes simply unacceptable today, organizations seek solutions with capabilities such as the ability to manage backups seamlessly, manage and monitor backups, ensure data integrity, scale efficiently, restore quickly to any point in time, and provide security features to stay in compliance with local geographic and industry mandates.

Posted August 14, 2019

Data visualization tools have evolved beyond the standard charts and graphs used in Excel spreadsheets, displaying data in more sophisticated ways such as infographics, dials and gauges, geographic maps, sparklines, heat maps, and detailed bar, pie and fever charts. The images may include interactive capabilities, enabling users to manipulate them or drill into the data for querying and analysis.

Posted August 14, 2019

Best Data Virtualization Solution

Posted August 14, 2019

According to an IDC report, the Global Datasphere will grow from 33 Zettabytes (ZB) in 2018 to 175ZB by 2025. "To keep up with the storage demands stemming from all this data creation, IDC forecasts that over 22ZB of storage capacity must ship across all media types from 2018 to 2025."

Posted August 14, 2019

Increasingly stringent data privacy regulations along with a generally lower tolerance for data mishandling, are making companies even more concerned about improving their data security postures and thwarting cyber risk.

Posted August 14, 2019

Best Data Replication Solution

Posted August 14, 2019

Best Data Quality Solution

Posted August 14, 2019

Best Data Modeling Solution

Posted August 14, 2019

In the era of big data, volume, variety, and velocity are often mentioned as critical issues. However, at Data Summit 2019, data integration was identified as one of the most critical and disruptive problems facing organizations today.

Posted August 14, 2019

The pressure is on. Today, organizations are under tremendous pressure from business partners, consumers, and regulators to exercise care about how they handle sensitive data of all kinds. Data governance is becoming more challenging due to a confluence of factors, including data volumes exploding, data being collected from more disparate sources, and data no longer being stored centrally, but instead in a combination of on-premise, cloud, and hybrid scenarios.

Posted August 14, 2019

One in four enterprises now regards real-time data as critical to their ongoing operations—and another one in four is actively preparing to introduce real-time data capabilities into their infrastructures. Data-driven innovation is being embraced by almost every department in the enterprise, led by outward-facing departments, with line-of-business owners and marketing departments demanding the most innovation from data.

Posted August 14, 2019

Best Cognitive Solution

Posted August 14, 2019

Cloud is now mainstream, a critical part of data environments,and this trend is only increasing. Gartner estimates that $206 billion will spent on public cloud services in 2019, up 17% from 2018, while IDC estimates that nearly half of IT spending was cloud-based in 2018, "reaching 60% of all IT infrastructure and 60%-70% of all software, services and technology spending by 2020."  

Posted August 14, 2019

DBAs and their organizations are moving to the cloud. A recent survey conducted by Unisphere Research, a division of Information Today, Inc., in partnership with Amazon Web Services, found that, on average, 25% of organizations' critical enterprise data is now managed in public clouds. The survey also found that for 60% of data managers the use of public cloud-based data resources and platforms has increased over the past year.

Posted August 14, 2019

Speed to insight matters more today than ever before. But dealing with the increasing volume of data, and the speed at which this data changes, can be a hindrance to data integration, timely analytics, and rapid decision making. Change data capture supports real-time analytics with less overhead to advance a range of initiatives such as data warehousing, real-time dashboards, data quality, and more.

Posted August 14, 2019

To leverage the immense power of their data, organizations need a solid strategy that incorporates everything from security to data governance to the right big data technologies. Enabling both on-prem and cloud deployments—or a hybrid strategy—big data platforms today support data warehouses, data lakes, data science, engineering, machine learning, myriad database management systems, and much more.

Posted August 14, 2019

The ability to quickly act on information to solve problems or create value has long been the goal of many businesses. But with the combination of greater volume, variety, and velocity, the situation is becoming more vexing. Fortunately, there are business intelligence solutions today with capabilities to support strong data strategies.

Posted August 14, 2019

While IT has evolved significantly in the past decade as hardware and software innovations accelerate and customers seek solutions that increase efficiencies and lower costs, one aspect has remained the same: backup. And as the dependence on data grows for business insights and analytics, backup will only become more important in IT.

Posted August 07, 2019

As access to data and data sources continues to explode, businesses are being forced to rethink their data strategies to consider more information and power real-time intelligent decisions. That is, however, easier said than done. With the amount of data available that is pouring in from a myriad of sources, it can be difficult to identify what provides value, and what is just noise. Increased data and cloud growth has led to data integration challenges.

Posted August 07, 2019

We've reached the point where hybrid cloud arrangements have become commonplace in enterprises, and with this trend come implications for databases and data management. The rise of both hybrid and multi-cloud platforms means data needs to be managed in new ways, industry experts point out. And, there are lingering questions about which data should go into the cloud, and which should stay on-premise.

Posted August 07, 2019

With the emergence of data-intensive activities such AI and the Internet of Things, workloads are getting heavier for data managers. Data managers have seen increases in data volume over the last 3 years and expect this trend to continue. They are also finding it difficult to keep up with this growth. Many DBAs manage more than 10 databases, with some handling hundreds.

Posted August 07, 2019

Protecting Against Cryptomining Malware in 2019: A Layered Approach to Device Management and Security

Posted August 07, 2019

Earlier this year, Google became the first major tech giant to be hit with a General Data Protection Regulation (GDPR) fine—approximately $56.8 million. The stated reason: not giving users enough information about consent policies and sufficient control over how their personal data was being used. However, according to a recent report, 86% of businesses use live customer data for application testing because testers believe this provides the most realistic assessment of how an application will perform "in the wild" for real people. This poses significant risk to organizations.

Posted July 18, 2019

The Right and Left Brain of DevOps and Security: How to Gain a Meeting of the Minds Instead of a Battle of Wills

Posted July 18, 2019

The Perils of Ignoring Employee ‘Leaver' Data in Regulated Industries

Posted July 18, 2019

DBTA 100 2019 - The Companies That Matter Most in Data

Posted June 12, 2019

AI, machine learning, and predictive analytics are used synonymously by even the most data-intensive organizations, but there are subtle, yet important, differences between them. Machine learning is a type of AI that enables machines to process data and learn on their own, without constant human supervision. Predictive analytics uses collected data to predict future outcomes based on historical data.

Posted June 10, 2019

With deep learning so much praised, you've probably read a lot about its positive side. Let's skip the positives and instead concentrate on the things that can go wrong with deep learning-based demand forecasts as well as offer some ways to overcome these problems. 

Posted June 10, 2019

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