Historically, the need for cost-effective storage, performant processing, and low-latency querying required a two-tier architecture: a data lake for raw storage of unstructured data and a data warehouse on top of it for high-performance reporting and querying. To integrate these layers, ETL batch processes were used. The introduction of a Lakehouse architecture, using Delta as the underlying storage format and Spark as the querying engine, aims to solve the shortcomings of the two-tier architecture by unifying it into a single layer. This can make data more accessible, cheaper to query, and reduce the number of buggy data pipelines. However, usability and productivity still remain a challenge. Download this special eBook to learn how make your transition to a Data Lakehouse easier and get the most from your investment.