How Apache Iceberg Is Changing the Face of Data Lakes

Data storage has been evolving, from databases to data warehouses and expansive data lakes, with each architecture responding to different business and data needs. Traditional databases excelled at structured data and transactional workloads but struggled with performance at scale as data volumes grew. The data warehouse solved for performance and scale but, much like the databases that preceded it, relied on proprietary formats to build vertically integrated systems. Data lake systems moved to more open formats but lacked the functional benefits that warehouses provide, such as ACID-compliant transactions, comprehensive governance and more. Ultimately, users found themselves stuck between two options: either a fully integrated platform with only proprietary solutions available or a resource-intensive, build-it-yourself, vendor-neutral data lake in a constant state of migration, in hopes of finally capturing promised value...

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