Data warehouses are intended to perform queries and analysis and often contain large amounts of historical data. The data within a data warehouse is usually derived from a wide range of sources such as application log files and transaction applications.
Historically, data warehouses have been painful to manage. The legacy, on-premises systems that worked well for the past 40 years have proved to be expensive and had many challenges around data freshness, scaling and costs.
In addition, they cannot easily provide artificial intelligence (AI) or real-time capabilities that are needed by modern businesses.
A data lake is a storage repository that holds a vast amount of raw data in its native format until it is needed for analytics applications.
Over the years, cloud data lake and warehousing architectures have helped enterprises scale their data management efforts while lowering costs. Conventionally, the steps in the data management architecture typically include enterprise data extraction from operational data repositories and storing them in a raw data lake.
Data warehouse users tend to be closer to the business, and have ideas about how to improve analysis, often without the ability to explore the business to drive a deeper understanding.
On the contrary, data lake users are closer to the raw data and have the tools and capabilities to explore the data. Since they spend so much time doing this, they are focused on the data itself, and less focused on the business. This disconnect deprives the business of the opportunity to find insights that would drive the business forward to higher revenues, lower costs, lower risk and new opportunities.
There are many benefits achieved by the convergence of data warehouses and data lakes. Going forward, as cloud data warehouse and data lake architectures converge, companies may soon find vendors that combine all the capabilities of all the data lakehouse tools. This could open up endless opportunities when it comes to building and managing data pipelines.
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Melody K. Smith
Sponsored by Data Harmony, harmonizing knowledge for a better search experience.