If you’re an engineer exploring a streaming platform like Kafka, chances are you’ve spent some time trying to work out what’s going on with the data in there. But if you’re introducing Kafka to a team of data scientists or developers unfamiliar with its idiosyncrasies, you might have spent days, weeks, months trying to tack on self-service capabilities. We’ve been there.
From data stagnating in warehouses to a growing number of real-time applications, in this article we explain why we need a new class of Data Catalogs: this time for real-time data. The 2010s brought us organizations “doing big data”. Teams were encouraged to dump it into a data lake and leave it for others to harvest. But data lakes soon became data swamps.
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Strong investments in an organization’s data pipeline result in greater business outcomes. Few would dispute this claim, reflected in the massive growth in the big data and analytics market which continues to fuel many organizations’ ambition to become data-driven.
Apache Hive supports transactional tables which provide ACID guarantees. There has been a significant amount of work that has gone into hive to make these transactional tables highly performant. Apache Spark provides some capabilities to access hive external tables but it cannot access hive managed tables. To access hive managed tables from spark Hive Warehouse Connector needs to be used.
Being data-driven helps businesses to cut costs and produce higher returns on investments, increasing their financial viability in the fight for a piece of the market pie. But *becoming* data-driven is a more labor-intensive process. In the same way that companies must align themselves around business objectives, data professionals must align their data around data models. In other words: if you want to run a successful data-driven operation, you need to model your data first.
