This white paper from our friends over at Vertica discusses how the race for a unified analytics warehouse is on. The data warehouse has been around for almost three decades. Shortly after big data platforms were introduced in the late 2000s, there was talk that the data warehouse was dead—but it never went away. When big data platform vendors realized that the data warehouse was here to stay, they started building databases on top of their file system and conceptualizing a data lake that would replace the data warehouse. It never did.
Data lake platforms quickly demonstrated the value of semistructured data. As data lakes grew in popularity, data warehouse vendors saw them encroaching on their territory, so they began to shift their databases to run on file systems and started finding ways to process semi-structured data.
Within a few years, nearly every organization that ran a data warehouse also stood up a data lake. The two existed side by side. Initially, there was some data sharing between the two platforms, but not much more. Pressured by customer demands to run analytics across both the data lake and the data warehouse, vendors on both sides began working toward a more complete integration of a warehouse and lake.The race began.
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