A data lakehouse is a new data management paradigm that combines the capabilities of data lakes and data warehouses, enabling BI and ML on all data.
Data warehouses have a long history in decision support and business intelligence applications, though were not suited or were expensive for handling unstructured data, semi-structured data, and data with high variety, velocity, and volume.
Data lakes then emerged to handle raw data in a variety of formats on cheap storage for data science and machine learning, though lacked critical features from the world of data warehouses: they do not support transactions, they do not enforce data quality, and their lack of consistency/isolation makes it almost impossible to mix appends and reads, and batch and streaming jobs.
Data teams consequently stitch these systems together to enable BI and ML across the data in both these systems, resulting in duplicate data, extra infrastructure cost, and security challenges.
Data lakehouses are enabled by a new system design: implementing similar data structures and data management features to those in a data warehouse, directly on the kind of low-cost storage used for data lakes. Merging them together into a single system means that data teams can move faster as they are able to use data without needing to access multiple systems. Data lakehouses also ensure that teams have the most complete and up to date data available for data science, machine learning, and business analytics projects.
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