Enterprise Data Hub

The Definitive Guide to BI and Analytics on a Data Lake

Data is evolving, so it's not surprising that the worlds of business intelligence (BI), self-service data exploration, and analytics are also in their own state of metamorphosis. So what happens when you pair these three disciplines with big data technology?

In their new book, The Definitive Guide to BI and Analytics on a Data Lake, authors Jim Scott and Sameer Nori examine the business potential and significant dynamics of analytics and self-service data exploration on a big data platform.

Building Blocks for Data Lakes

Read the Ovum report “Data Lakes: The Six Key Building Blocks to Success” by well-known industry analyst Tony Baer. You will come away with a firm understanding of the capabilities required in an underlying platform to enable building a successful data lake. You’ll also learn how the MapR Converged Data Platform meets the key requirements needed to support building a data lake, and much more.

The Need for Big Data Governance

Data Governance is essential to delivering maximum value from your big data environment. Without knowing what data you have, what it means, who uses it, what it is for, and how good it is, you can never create the insights and information needed to run a modern data-driven enterprise. Instead of an afterthought, data governance needs to be front and center in the organizational effort to harness the power of its data.

TDWI Checklist Report: Data Lake Principles and Economics

Without design principles, swimming in circles in a big data lake can make your arms tired. Fortunately, the data lake concept has evolved so that best practices have emerged. This Checklist Report discusses what your enterprise should consider before diving into a data lake project, no matter if it’s your first, second, or even third major data lake project. Presumably, adherence to these principles will become second nature to the data lake team and they will even improve upon them at some point.

TDWI Checklist Report: Eight Tips for Modernizing a Data Warehouse

Data warehouse modernization takes many forms. Many users are diversifying their software portfolios, while others are even decommissioning current DW platforms in order to replace them with modern ones optimized for today’s requirements in big data, analytics, real time, and cost control. No matter what modernization strategy is in play, all require significant adjustments to the logical and systems architectures of the extended data warehouse environment.

Disaster Recovery

Disaster recovery (DR) is the science of returning a system to operating status after a site-wide disaster. DR enables business continuity for significant data center failures for which high availability features cannot cover.

The MapR Converged Community Edition

MapR Converged Community Edition (MapR CE) is a free edition of the MapR Converged Data Platform, with usage restrictions specified in the MapR End User License Agreement, and with community forum support. This free version includes Apache Hadoop, Apache Spark™, MapR-DB (NoSQL database), MapR Streams (event streaming), and MapR-FS (POSIX file system). MapR CE enables distributed processing of large data sets across a cluster of servers. MapR delivers a proven platform that supports a broad set of large-scale, real-time applications.


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