Awesome GitHub RepositoriesDistributed SQL Engines

Apache Spark is a unified distributed data processing engine designed for large-scale data analysis and computation graphs. It functions as a distributed machine learning framework, a graph processing system, a real-time stream processor, and a SQL analytics engine. The system enables the execution of distributed SQL querying, large-scale graph analysis, and real-time stream analytics across clusters of machines. It also provides a scalable environment for implementing machine learning algorithms and predictive model development on massive datasets. The engine incorporates relational query e

Cockroach is a distributed SQL database designed to scale horizontally across multiple nodes while maintaining strict ACID compliance and global data consistency. It functions as a relational database engine that automatically partitions data into ranges, rebalancing them across a cluster to accommodate growing storage and throughput requirements. By utilizing a distributed consensus protocol, the system ensures that all nodes agree on the order of operations, providing fault tolerance and continuous availability even in the event of hardware failures. The system distinguishes itself through

Presto is a distributed SQL query engine designed for high-performance analytical processing across heterogeneous data sources. It functions as a data federation platform and massively parallel processing engine, allowing users to execute interactive queries against diverse storage systems without requiring data migration. By mapping remote metadata and structures to a unified relational namespace, it enables seamless cross-platform analysis through a standard SQL interface. The engine distinguishes itself through a pluggable connector architecture and a shared-nothing distributed processing

Trino is a distributed SQL query engine designed for large-scale data analytics. It functions as a data federation platform, providing a unified interface that allows users to execute complex analytical queries across multiple heterogeneous data sources simultaneously without requiring data movement or transformation. The engine utilizes a massively parallel processing architecture to scale compute resources across clusters for high-speed data retrieval. It distinguishes itself through a cost-based query optimizer that analyzes metadata to determine efficient execution plans, alongside dynami

Citus is a PostgreSQL extension that transforms a standard database into a distributed system. It functions as a sharding framework and distributed SQL engine, enabling horizontal scaling by partitioning tables across a cluster of nodes. By utilizing a coordinator-worker topology, the system manages metadata and routes queries to the appropriate nodes, allowing for parallel execution of complex operations across distributed data shards. The platform distinguishes itself through its specialized support for multi-tenant architectures and real-time analytical processing. It enables tenant-based