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Do You Actually Need a Data Lake?

In this contributed article, Eran Levy, Director of Marketing at Upsolver, sets out to formally define “data lake” and then goes on to ask whether your organization needs a data lake by examining 5 key indicators. Data lakes have become the cornerstone of many big data initiatives, just as they offer easier and more flexible options to scale when working with high volumes of data that’s being generated at a high velocity – such as web, sensor or app activity data.

The Cloud Helped Expand ETL into Automated Data Infrastructure

In this contributed article, Ben Bloch, CEO of Bloch Strategy, defines Extraction, Transformation and Load (ETL) as the process of taking data from one system, changing it in a way that allows it to be integrated with other data in a new system. Since it’s automated you can work with scale in more agile ways. It became a well known acronym in business software during the 1990s, due to the focus on data warehouses. Many people in the industry have tied ETL too closely to on-premise data warehousing, and consider that it has no place in the Cloud world, but that is an incorrect assumption. ETL is even more important today.