Data has evolved over the years. Complex data structures, unstructured data, real-time processing, growing data volumes, and new varieties of data are all part of the evolution. Platforms have changed as well. “Schema-less,” real-time events, “schema-on-read,” and extract/load/discover/transform (ELDT) are now part of our vernacular.

Despite these changes, many businesses rely on the same data warehouse infrastructure that they’ve relied on for years. Many businesses have also turned to data lakes, through platforms such as Apache Hadoop, NoSQL databases, and Apache Kafka, or cloud storage technologies like Amazon S3, as a cost-effective way of managing large volumes of disparate data sets. Unfortunately, the success rates of these data lakes have been disappointing, as they have not been able to deliver quicker or better value to businesses.

Why isn’t the data lake living up to its promise? It turns out that the data lake architecture is a robust data management strategy, but a weak data intelligence strategy, due to its lack of focus on tools to analyze the data. While the data landscape has evolved, not all tools have kept pace. The data management platforms have changed, and it’s time to rethink the BI and analytics tools as well.

In this article, we’ll examine the advantages of an analytics and BI platform built specifically for data lakes, and how Arcadia Enterprise, the flagship product from Arcadia Data, brings self-service analytics to those environments. 

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