The data lakehouse architecture is dual-layered. It also addresses the limitations encountered when using the data warehouse and data lake as individual solutions. It is a hybrid architecture with the fundamental purpose of combining the best characteristics of both the data warehouse and data lake. This led to the development of a different type of system which could bridge the gap: the data lakehouse.Ī data lakehouse is a new data solution concept that combines elements of a data warehouse with those of a data lake. But, combining the two approaches also gives rise to a lot of operational and data governance complications. The combination of traditional data warehouses and data lakes solves many of the data processing issues that plagued the platforms when used individually. Therefore, organizations were forced to combine both data architectures to be able to fully utilize the data. This solved the problem of data storage but once the data was in the data lake, it can not be queried to perform structured operations, like those used in traditional SQL use cases. This unstructured data might make up the bulk of data being created and stored. As unstructured data became more common, data lakes solved the problem of handling large amounts of raw unstructured data. The data warehouse led to the use of analytics to enable Business Intelligence and other use cases that excel when large amounts of data are used to derive insights.Īs the technology evolved, organizations began to realize that the data warehouse is inefficient when it comes to storing streaming information that can be used for Data Science and Machine Learning. These solutions sometimes began as in-house solutions in the early years, then began to progress into a “platform” approach as companies began to build out-of-the-box data warehouse solutions. The need for organizations to invest in technologies to capture and store data, to capitalize on the valuable insights that can be derived from it, has grown to an all-time high.ĭata warehouses have served the purpose of collating structured data over the years. As technology advances to accommodate this ever-growing amount of data, each day sees the birth of a new term or the evolution of existing solutions. The more data created, captured and analyzed, the more accuracy the business can see when using the data. The benefit of this new era of massive amounts of data is that companies can rely on the data to drive critical business decisions, improve product offerings, and serve customers better. The sheer amount of data that is created and stored every second seems almost infinite. In today’s technological climate, the increasing amount of data being generated can be overwhelming.
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