Planning for a Scalable Enterprise Data Lake

In this webinar we will discuss a more modern view of the data lake and consider best practices for planning and implementing a scalable enterprise data lake. The flaws in early data lakes were often rooted in the expectations of data consumers who put a premium on self-service data analytics. However, with no data governance mechanisms, data lakes quickly became more of a glorified “dumping ground,” “data swamp,” or “beta lake” for organizational data.In recent years, though, some innovations have allowed the data lake to evolve into an agile yet managed environment for accumulating shared data resources that can be optimally used for competitive advantage. Data lakes have evolved beyond the original on-premises concept based solely on Hadoop and now include pretty much any distributed computing platform (Hadoop, Spark, EMR, serverless, etc.) and any storage mechanism (HDFS, S3, ADLS), either on-premises or in the cloud.
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Spotlight

OTHER ON-DEMAND WEBINARS

Strategies for Transitioning to a Cloud First Enterprise

DATAVERSITY

A great comfort with cloud deployment has emerged. Businesses are migrating databases to the cloud or building databases there as a result of scale challenges with the on-premises model, the cloud becoming the “center of gravity”, on-premises databases reaching capacity or emerging uses cases that are specific to the cloud. But not all organizations! And some struggle mightily with the move!
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What’s Ahead in Data Management in 2019?

tdwi.org

This webinar is a must attend for technical users and business managers who are facing these changes. The expert panel on this webinar will help attendees understand what’s ahead in 2019 and beyond for data management. Attendees can then apply that information to prioritize the data management changes they must address and how they will prepare via hiring, training, budgeting, making a business case, and adopting the right data platforms and tools.
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Overcoming Data Management Challenges in AI/ML

The ever-growing data landscape drives initiatives to automate many aspects of the analytics lifecycle; such as data access, enablement of semantics, BI and others. Automation has become an integral part of our daily lives in the enterprise data fabric. The AI-driven initiative to automate the data access and provide guidance to the right data assets, correlates with the initiatives of the data scientists to get access to more curated data.
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How does AXA Belgium Replicate its operational data into its Data Lake?

To preview your data, you must first have access to it. This operation can be difficult when distributed into different systems and silos which is why many companies choose to bring them together in a Data Lake. However, without governance or stewardship, these data lakes are rapidly turning into swamps, veritable brakes on innovation.
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