The idea is to create a single data platform that combines the easy and structured querying capability of data warehouses with the flexibility, openness and cost effectiveness of data lakes. The Data Lakehouse concept seeks to address this If for example you didn’t know where all your PII data was stored you risked non compliance with data protection laws such as GDPR. Data governance features typically weren’t prioritised and data was often poorly catalogued. For a full lakehouse experience, the user can access the corresponding SQL Endpoint and default dataset.Data lakes didn’t properly support transactions and incremental data updates.This article explains data lakehouses, including how they emerged, how they shape up versus data. The features of a data lakehouse make it ideal for a range of data analytics use cases. It is popular among many organizations that incorporate the features of both data lakes and data warehouses. BI tool support was often limited so querying the data was more difficult. A data lakehouse is a modern data architecture.Data sets weren’t really modelled (by design) which meant they were more difficult to understand and join up.The “store anything” approach offered by data lakes meant data was often badly curated and suffered from poor quality (hence the growth of the term “Data Swamp”).Many people predicted the death of the data warehouse as a result, but that didn’t happen. The theory was you could now just throw your raw data files into a big and cheap data storage platform and you had yourself a Data Lake that your analysts and data scientists could be let loose on with a plethora of new query tools. Open source developments in the early 2000s such as Hadoop and Hive meant it was now a lot easier and cheaper to access and analyse data in its raw form.
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