Hybrid Distributed Data Store and RDBMS

As organizations transition their analytical ecosystems to the cloud, they are increasingly adopting hybrid data storage solutions that combine traditional relational database management systems (RDBMS) with distributed data stores like S3, Azure's ADLS, and HDFS. This approach leverages the strengths of both systems, with RDBMS managing mutable data that requires frequent updates, and distributed stores handling "write-once, read-many" data such as events and IoT transactions. Modern tools and federated query engines, like Starburst's Trino, facilitate seamless data queries across these systems, offering flexibility and scalability without the data lock-in typically associated with on-premise solutions. This hybrid architecture supports a variety of emerging data patterns, including data lakes, data meshes, and multi-location data setups, enabling companies to efficiently manage and analyze vast amounts of data while mitigating downtime and quality issues during migrations.

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