{"id":1851,"date":"2016-11-04T15:41:57","date_gmt":"2016-11-04T19:41:57","guid":{"rendered":"https:\/\/solutionsreview.com\/data-integration\/?p=1851"},"modified":"2016-11-04T16:26:22","modified_gmt":"2016-11-04T20:26:22","slug":"the-future-of-etl-and-the-argument-for-spark-augmentation","status":"publish","type":"post","link":"https:\/\/solutionsreview.com\/data-integration\/the-future-of-etl-and-the-argument-for-spark-augmentation\/","title":{"rendered":"The Future of ETL and the Argument for Spark Augmentation"},"content":{"rendered":"

In managing databases, extract, transform, load (ETL) refers to three separate functions combined into a single programming tool.\u00a0Fragmentation in the legacy Data Integration market made us wonder whether or not traditional integration tools were becoming obsolete<\/a> before our very eyes. With this in mind, we asked the crowd<\/a> whether or not they believed Data Integration as we\u2019ve known it was dying. To our surprise, the answer was a resounding no, and it appears that legacy tools are still being used in many verticals as enterprises prepare for the next wave in data tools.<\/p>\n

In a recent presentation at Spark Summit EU, ING’s Chapter Lead in Analytics Bas Geerdink spoke to this very topic, recommending a migration from ETL to Apache Spark for data processing and movement. Geerdink, who is also a certified Spark developer argues that ETL has seen no real technological or market evolution like BI and the data warehouse have in recent years.\u00a0ETL tools don’t seem to have a major role in the future outside of niche use cases, with this slideshow even referring to these solutions as “ETL Hell<\/a>.” Make your own conclusion, and click through the presentation to learn more.<\/p>\n

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