Cyberattack—How to Prepare and What to Do If It Happens
Posted November 28, 2017
Moving Beyond Relational With Data Integration
Posted November 28, 2017
No longer the stuff of science fiction, the business uses for cognitive computing, artificial intelligence, and machine learning today include fields as diverse as medicine, marketing, defense, energy, and agriculture. Enabling these applications is the vast amount of data that companies are collecting from machine sensors, instruments, and websites and the ability to support smarter solutions with faster data processing.
Posted November 13, 2017
AtScale is releasing a universal semantic platform for business intelligence (BI) with Microsoft Azure HDInsight, providing enterprises with faster time to insight. AtScale's new offer helps enhance Azure HDInsight customers' ability to quickly turn their Big Data lake into a highly available and performant analytical database.
Posted November 02, 2017
There's a surprising trick for greatly increasing the chances of real impact, true success with many types of machine learning systems, and that is "do the logistics correctly and efficiently." That sounds like simple advice - it is - but the impact can be enormous. If the logistics are not handled well, machine learning projects generally fail to deliver practical value. In fact, they may fail to deliver at all. But carrying out this advice may not seem simple at all.
Posted October 26, 2017
After years of ambiguous expectations about data governance, organizations now have a better handle on how these programs can help them manage the exponential growth of the data they generate. More importantly, there is an understanding about how the integration, organization, and alignment of that data can help meet or exceed business and technology goals.
Posted October 18, 2017
As companies grow increasingly data-centric in their decision making, product and services development, and their overall understanding of the world they work in, speed and agility are becoming critical capabilities. A common theme in big data and analytics today is "Industry 4.0," representing a new wave of technology that enables the automation necessary for scaling. There's compelling justification for this as companies seek to unlock business value from big data with two broad approaches: the democratization of data with greater access by more users, and the enablement of automation everywhere possible.
Posted September 20, 2017
Big Data and Analytics Becomes Smarter and More Connected
Posted September 20, 2017
Over the last few years, organizations have shifted from using virtual data centers to creating private or hybrid IaaS clouds that allow authorized users to perform self-service provisioning of virtual machines. These environments have reduced administrative workloads, improved the user experience, and discouraged shadow IT, but they have also brought their own challenges. As virtualized environments increase in scale, management techniques have often become far less effective, making it difficult to keep track of virtual machines, their owners, and why the virtual machines were created in the first place.
Posted September 20, 2017
Companies today are spreading their applications across multiple clouds in a hybrid fashion. According to a recent IDC CloudView study among 6,000 IT and line-of-business executives whose organizations have adopted cloud technologies, 73% are implementing a hybrid strategy, which most defined as utilizing more than one public cloud in addition to dedicated assets.
Posted September 20, 2017
Many people are unsure of the differences between deep learning, machine learning, and artificial intelligence. Generally speaking, and with minimal debate, it is reasonably well-accepted that artificial intelligence can most easily be categorized as that which we have not yet figured out how to solve, while machine learning is a practical application with the know-how to solve problems, such as with anomaly detectio
Posted September 20, 2017
When it comes to visualizing data, there is no shortage of charts and graphs to choose from. From traditional graphs to innovative hand-coded visualizations, there is a continuum of visualizations ready to translate data from numbers into meaning using shapes, colors, and other visual cues. However, each visualization type is intended to show different types of data in specific ways to best represent its insight. Let's look at five of the most common visualization types to help you choose the right chart for your da
Posted September 20, 2017
Businesses of all sizes across all industries are rapidly adopting digital transformation models that put data at the center of driving the business forward—as they should. However, putting data at the center of everything the business does can be risky without proper planning and rigorous management. Many companies have been wise to introduce data governance programs to protect corporate data assets and establish a framework for operational excellence when it comes to data management and use. Data governance emphasizes the enforcement of defined standards or policies and provides mechanisms for consistency and repeatable processes, but it is not enough to protect businesses in today's world of data.
Posted September 20, 2017
Nowadays, many firms are already using big data and analytics to manage and optimize their customer relationships. Both technologies can also prove beneficial to leverage a firm's other key assets: its employees! Various HR analytics (also called workforce analytics) examples can be thought of.
Posted September 20, 2017
While Vic Damone and Jane Powell wanted their eggs with a kiss in the 1950s musical Rich, Young and Pretty, in the near future, your kitchen might well know exactly how you want them thanks to the Internet of Things (IoT).
Posted September 20, 2017
Tic toc, tic toc—back and forth swings the privacy pendulum. While we in the U.S. continue to regress on issues of data privacy, the European Union (EU) is proceeding with bold steps to protect the privacy of its citizens. On May 25, 2018, the General Data Protection Regulation (GDPR) becomes the law of the land in the EU. It applies to any company that processes or holds data on EU residents, regardless of where it is located in the world. Popular applications such as Facebook, Twitter, and Airbnb are among the companies that will be directly impacted by this law. If you do business with EU residents, regardless of geographic locality, this law directly applies to you.
Posted September 20, 2017
Qubole Data Service provides a single platform for ETL, reporting, ad hoc analysis, stream processing and machine learning. It runs on AWS, Microsoft Azure and Oracle Bare Metal Cloud, taking advantage of the elasticity and scale of the cloud, and also supports leading open source engines, including Apache Spark, Hadoop, Presto, and Hive.
Posted September 12, 2017
Columnar Data, Analytics, and the Evolving Role of Hardware
Posted September 12, 2017
New multi-cloud capabilities scale discovery and dependency mapping of all assets to go beyond on-prem data centers to public and private cloud.
Posted September 12, 2017
As organizations increasingly move their data and applications from on-premise deployments to the cloud, the role of the DBA is also shifting. According to Penny Avril, vice president of product management, Oracle Database, the transition means that DBAs have the opportunity to move from being data custodians and keepers to taking on a more strategic role in their organizations. But, she says,the time to prepare for the new cloud reality is now.
Posted September 07, 2017
Big Data 50 - Companies Driving Innovation in 2017
Posted September 07, 2017
InfluxData provides an open source platform built for metrics, events, and other time-based data. Recently, Evan Kaplan, CEO of InfluxData, reflected on the future of databases and why time-series represents the next wave of databases for data—from humans, sensors, and machines.
Posted August 30, 2017
8 Rules of the Road for Fast Data Management and Analytics
Posted August 16, 2017
Barracuda Networks, a provider of cloud-enabled security and data protection solutions, has added the ability to replicate data from either an on-premises physical or virtual backup appliance to AWS. The new feature provides customers, resellers, and MSPs with greater flexibility and choice to protect their data from data loss and potential disasters, including security threats like ransomware. This adds an additional option for customers, in addition to the ability to replicate to the Barracuda Cloud.
Posted August 15, 2017
How to Solve Big Data Integration Challenges
Posted August 08, 2017
Dremio has announced its launch in the data analytics market with the availability of the Dremio Self-Service Data Platform. According to Dremio, its platform allows users to be independent and self-directed in their use of data, while accessing data from a variety of sources at scale.
Posted July 19, 2017
Pricchaa has released a free solution for detecting, encrypting, and monitoring sensitive data housed in the Amazon Web Services (AWS) Cloud.
Posted July 13, 2017
Tips and Tricks for Migrating to NoSQL
Posted June 15, 2017
The Growing Power of the Internet of Things
Posted June 15, 2017
The Apache Arrow project is a standard for representing data for in-memory processing.Hardware evolves rapidly. Because Apache Arrow was designed to benefit many different types of software in a wide range of hardware environments, the project team focused on making the work "future-proof," which meant anticipating changes to hardware over the next decade.
Posted June 14, 2017
Looker, provider of a cloud data platform, has announced Instant Insight, a new feature that allows users to analyze their data without waiting for help from an analyst.
Posted June 14, 2017
Attunity Ltd., a provider of data integration and big data management software solutions, is launching a new solution, Attunity Compose for Hive, which automates the process of creation and continuous loading of operational and historical data stores in a data lake.
Posted June 13, 2017
Addressing the rise of hybrid deployments, Hortonworks has introduced a new software support subscription to provide seamless support to organizations as they transition from on-premise to cloud. Separately, Hortonworks also announced the general availability of Hortonworks Dataflow (HDF) 3.0, a new release of its open source data-in-motion platform, which enables customers to collect, curate, analyze and act on all data in real-time, across the data center and cloud.
Posted June 12, 2017
How to Leverage the Power of Data Lakes
Posted June 09, 2017
The demand for speed and agility are among the key drivers of the growing DevOps movement, which seeks to better align software development and IT operations. Yet, challenges still exist.
Posted June 07, 2017
Data Governance and Security Tips from Data Summit 2017
Posted May 24, 2017
The Enterprise Impact of Streaming Data and IoT
Posted May 24, 2017
9 Key Takeaways About Cloud and Analytics from Data Summit 2017
Posted May 24, 2017
Tapping into the Best Strategies for Integrating to Hadoop with Josh Klahr at Data Summit 2017
Posted May 17, 2017
Tips for Database Management and Migration in the Cloud
Posted May 17, 2017
The Importance of Information Governance in our Current Data Analytics Landscape
Posted May 17, 2017
In October of 2008, Congress enacted the Emergency Economic Stabilization Act, more commonly known as the bailout of the financial system. The understanding was that catastrophic financial consequences would be the result of the failure of these entities and that those aggregate failures could devastate the U.S. The recent major outages in the public clouds services inevitably lead to the same issue being consider with regard to this new industry.
Posted May 15, 2017
With the furor over fake news, where the truth is massaged for commercial or political gain, the focus has gone off fake data—which can have a lot more perilous consequences.
Posted May 15, 2017
When people talk about the next generation of applications or infrastructure, what is often echoed throughout the industry is the cloud. On the application side, the concept of "serverless" is becoming less of a pipe dream and more of a reality. The infrastructure side has already proven that it is possible to deliver the ability to pay for compute on an hourly or more granular basis.
Posted May 15, 2017
You will often hear experienced practitioners and consultants suggest that there is both an art and a science to effective data governance. The art is in the details of fine-tuning a data governance program to fit your culture and address specific business needs. But the fundamental principles of data governance are best understood and executed through science.
Posted May 15, 2017
The tide is changing for analytics architectures. Traditional approaches, from the data warehouse to the data lake, implicitly assume that all relevant data can be stored in a single, centralized repository. But this approach is slow and expensive, and sometimes not even feasible, because some data sources are too big to be replicated, and data is often too distributed to make a "full centralization" strategy successful.
Posted May 15, 2017
Over the next 6 years, the Internet of Things (IoT) market is expected to reach $883.55 billion, as connected devices continue to pour into just about every aspect of our lives. For enterprises, the IoT is helping to transform products into connected services, capable of creating recurring revenue streams, reducing costs, and enhancing customers' experiences.
Posted May 15, 2017
Data—now universally understood to be the lifeblood of businesses—is at risk like never before in the form of both malicious attacks and innocent indiscretions. Recently, Steve Grobman, CTO for McAfee, discussed the range of threats to data security and what companies must do to defend themselves.
Posted May 15, 2017
Organizations are embracing data visualization as more than a tool to "see" trends and patterns in data but as a pathway to a dynamic culture of visual data discovery. As with any type of cultural shift, there are going to be a few bumps along the road as innovative ways to transform data into actionable insights through the power of data visualization are sought.However, with a few considerations kept top-of-mind in the early stages of data visualization adoption, common problems can be avoided.
Posted May 15, 2017
Big data and analytics are all around these days. Most companies already have their first analytical models in production and are thinking about further boosting their performance. However, far too often, these companies focus on the analytical techniques rather than on the key ingredient: data. The best way to boost the performance and ROI of an analytical model is by investing in new sources of data which can help to further unravel complex customer behavior and improve key analytical insights.
Posted May 15, 2017