Analyst Report: Part 8 of 9, Big Data/Data Lake Platforms: Removing Silos & Operationalizing Your Data

On Big Data

This is part 8 of a 9-part report, access all other parts here:

Part 1 – Data Connectors
Part 2 – Virtualized Data Layers
Part 3 – Data Integration
Part 4 – In-Memory Database/Grid Platforms
Part 5 – Data Warehouse Platforms
Part 6 – Business Intelligence (BI)
Part 7 – Business Intelligence on Big Data/Data
Part 9 – Data Management and Governance

We’ve spent a lot of time working our way through foundational analytics technology categories. But we haven’t yet addressed the one that is perhaps the most celebrated in the current market: big data and data lake technology.

Initially, Apache Hadoop and related technologies were representative of the big data analytics category.  Data lakes are merely an architectural approach that big data analytics enables, so the two terms are not, technically, equivalent.

But, these days in the industry, the terms tend to be used interchangeably.  One reason for this is most organizations use big data tech for the construction of data lakes.  Another reason is because many early Hadoop big data initiatives were perceived to be only minimally successful, leaving the industry in need of a new headline term, with “data lake” winning out.

Table of Contents

  1. Summary
  2. Strategy & Business Considerations
  3. Technical Considerations
  4. Technology Evolution & Landscape
  5. Key Players
  6. About Andrew Brust
  7. About GigaOm

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