Data lakes are no longer anomalies. Consolidating all of an organization’s data—unstructured, semi-structured, and structured—into a single repository for integration, access, and analytics purposes...
Recent developments in big data technologies have significantly impacted the prowess of contemporary analytics; the most profound of these involves the deployment of semantically enhanced semantic...
Many data lake projects achieve their IT objective: cheap storage of all enterprise data in raw form, but fail in their business objective to deliver value from this data. Why? Because making the...
Legacy applications that have exceeded their useful life can be expensive to maintain. They often require specialized skills and old versions of software and hardware to support. But, they can also...
Many Hadoop users, seeking higher performance and a better analytics engine, are turning to Apache Spark for data transformation (ELT) on HDFS. While Spark offers many advantages, you still need...
Mike Atkin of the EDM Council speaks eloquently about the "perfect storm" for data in Financial Services. Two converging forces, regulatory reporting requirements and the need for customer insight,...
Data integration projects can be time consuming, expensive and difficult to manage. Traditional data integration methods require point to point mapping of source and target systems. This effort...
A round up of recent industry news on the topics of Big Data and Enterprise Data Management
Driving business value from your data often requires integration across many sources. These integration projects can be time consuming, expensive and difficult to manage. Any short cuts can...
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