Bringing together the best of data warehouses and data lakes

In today’s highly digitized world, data about customers, products, operations and the supply chain can come from many sources, and can have a diverse set of structures. To gain deeper and more complete data driven insights into a business topic (such as customer journey, customer retention, product performance, etc.), organizations need to analyze all topic relevant data, of all structures, from all sources, together. A data lake is well suited to storing all these different types of data inexpensively, and provides a wide variety of tools to work with and consume the data. This includes the ability to transform data with frameworks such as Apache Spark, to train machine learning models on the data using tools such as Amazon Sagemaker, and to query the data using SQL with tools such as Amazon Athena, Presto or Trino. However, there are some limitations with traditional data lakes. For example, traditional implementations...