Ask the Expert about Data Warehouse Modernization
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Despite their ongoing evolution, data warehouses (DWs) are more relevant than ever, as they support operationalized analytics and wring business value from machine data and other new forms of big data. Hence, it’s important to modernize an existing DW environment, to keep it competitive and aligned with business goals, as well as to grow into new data-driven practices and technologies, while also keeping and improving the old ones.
April 7, 2017
Data Lakes: Purposes, Practices, Patterns, and Platforms
We’re experiencing a time of great change, as data evolves into greater diversity (more data types, sources, schema, and latencies) and as user organizations diversify the ways they use data for business value (via advanced analytics and data integrated across multiple analytic and operational applications). To capture new big data, to scale up burgeoning traditional data, and to leverage both fully, users are modernizing their portfolios of tools, platforms, best practices, and skills.
April 13, 2017
End Your Data Struggle: How to Seamlessly Analyze Disparate Data
Many organizations today are struggling to get value from their data and advanced analytics initiatives. The struggle begins with data diversity, as organizations are trying to support new apps, customer channels, sensors, and social media outlets. Each source may have its own data structure, quality, and container (in the form of files, documents, messages). The struggle is exacerbated by the exploding volume of data that must be captured, processed, stored, and delivered to the right users in a state that is fit for their own individual needs.
April 25, 2017
Between a Rock and a Hard Place: How to Modernize Legacy Middleware for an Evolving, Data-driven World
In support of daily operations, many organizations depend heavily on systems for enterprise application integration (EAI), enterprise service bus (ESB), and other approaches to middleware. Yet, these infrastructures are today legacy technologies that predate the rise of big data and unstructured data, as well as modern sources and targets for integration, such machines, devices, clouds, social media, and the Internet of Things (IoT). Furthermore, many middleware vendor tools are still optimized for the on-premises ERP-dominated applications world of twenty years ago; others are in legacy mode, with no future upgrades coming.
May 3, 2017