Enterprises exploring big data architectures have turned to data lakes and data warehouses for different types of analytic and AI workloads. These are complementary technologies — data warehouses shine when you know what you want to analyze, while data lakes excel at combining data for unforeseen applications.
As these architectures migrate to the cloud, their core ideas evolve to take advantage of the various services of the cloud infrastructure. With data lakes, for example, raw data once housed in dedicated platforms such as Hadoop move to cloud object stores such as S3 that operate as a broad storage mechanism. Amazon takes advantage of this separation with architectural tiers that blend the best of AWS data lake and data warehouse capabilities with tools like Amazon Redshift Spectrum.
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