Data volumes have experienced explosive growth in recent years, and that data is being generated from sources that are increasingly complex and varied. Harnessing and refining value from this data requires a new approach as data extraction, transformation, and loading (ETL) becoming increasingly more costly and difficult to scale.
Organizations are looking to leverage Hadoop as an enterprise data hub—also called a “data lake” or “data reservoir”—as a key component of their data architecture to augment their data warehouse, ETL and analytical systems in order to maximize their existing investments, reduce costs, and unlock new business value from their data.
In this webinar, you will learn:
Real-world examples that illustrate why Hadoop is the best low-cost data hub, data lake, or data landing zone (staging area) option for ETL processing
Proof points that demonstrate advantages of Hadoop and its ability to scale to manage increasing data volumes and support exploratory big data analytics
Proven best practices for a cost-effective, reliable way to implement a data management platform for your entire big data analytical ecosystem
Hidden issues to be aware of in deploying your data hub/data lake
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