| | | | | | | | |  | | | | Top Story |  |  | | |  | | | | 16 Trends Reshaping the Enterprise Data Landscape in 2016 | | | | It is often said that the only constant is change. For data executives and professionals, the coming year will only bring a lot more of it. Developments as diverse as cloud, big data, real time, NoSQL, analytics, and the Internet of Things (IoT) will continue to reshape enterprise data operations and opportunities as we know them. Here are 16 trends that will shape the enterprise data landscape in 2016. | | | | Read Full Story | | | | | | | | MUST HAVE DOWNLOADS | | | | | | FROM MAINFRAME TO NOSQL WITH MARKLOGIC AND INTEL
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IBM ANALYTICS FOR APACHE SPARK | | | | | | | News, Columns & Features | |  | | | | | | | Dynamic Data Masking and Row-Level Security Features on the Way in SQL Server 2016 | | | | SQL Server 2016, the next major release of Microsoft’s flagship database and analytics platform (which is available as a Community Technology Preview [CTP]), includes exciting features such as StretchDB and AlwaysEncrypted. Here are two more in the works to think about: dynamic data masking and row-level security. In the case of these two features, they’ll be released first to the cloud platform (Azure SQL Database) and, later, to the on-premises version of SQL Server. | | | | Read Full Story | | | | | | | Big Data Predictions for 2016 | | | What’s ahead for 2016 in terms of cloud, IoT, big data, analytics, and open source technologies? IT executives gaze into their crystal balls, and weigh in on the upcoming challenges and opportunities ahead in 2016.
| | | | Read Full Story | | | | | | | Leveraging All Data Assets for a Modern Data Architecture | | | | The modern business landscape is a fast-moving, ever-changing, highly competitive environment. For companies to outpace the competition and build upon innovation, they must embrace a modern data architecture. It is necessary that this new architecture support today’s new requirements such as mobile integration and advanced digital marketing. | | | | Read Full Story | | | | | | | Key Considerations When Data Modeling for Big Data | | | | The cardinal rule has been to model data first and load it later. But with new technologies and repositories such as Hadoop, NoSQL, and data lakes, and big data itself, the rule is being flipped to load first and model later. And, with SQL remaining an effective and widely embraced query language, companies have to balance working with traditional methods against the need for some of the newer methods as well. | | | | Read Full Story | | | | |
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