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Data lakes have been built with a desire to democratize data — to allow more and more people, tools, and applications to make use of more and more data. A key capability needed to achieve this is hiding the complexity of underlying data structures and physical data storage from users. The de facto standard to achieve this has been the Hive table format, released by Facebook in 2009 that addresses
The data landscape is a constantly changing one. We started with relational databases to store and process transactional data that was generated by a business’s day-to-day operations (e.g., customer, order details) to serve OLTP use cases. Then, with the rising need to derive analytical insights from a business’s stored data (e.g., average sales over time), we moved onto OLAP-based systems with da
Apache Iceberg: The Definitive Guide Everything you need to know about Apache Iceberg table architecture, and how to structure and optimize Iceberg tables for maximum performance WHY DREMIO Unified analytics on the lakehouse for high-performance, self-service access anywhere, on-premises, hybrid, or cloud Shift left analytics means bringing your users closer to your data, delivering seamless enter
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