The Swarm64 DA extension rewrites query patterns that execute in parallel at every phase of the query. It parallelizes scanning, filtering, joining, and merging, and spins up 5x more parallel threads than standard PostgreSQL.
Swarm64 DA loads and queries data stored in a columnar-indexed format within PostgreSQL foreign data tables (FDW). The format is optimized for highly parallel access. Partitioning and range indexes further reduce I/O.
Swarm64 DA compresses data 5x to 25x depending on the data type. Besides reducing storage costs, reading compressed data, along with columnar indexing, reduces I/O by ~20x relative to standard PostgreSQL.
If an FPGA is present on the server, Swarm64 DA initiates 100+ SQL reader & writer processes on the FPGA. The processes work in parallel to accelerate queries and data insertion. It adds massive parallelism at a very low cost, to handle larger-scale data management needs.
Swarm64 DA accelerates PostgreSQL v11 and higher, on-premises or on the cloud. Run it on any hardware that PostgreSQL supports. Deploy on FPGA-equipped servers or cloud instances for even greater acceleration.
Swarm64 DA extends free, open source PostgreSQL or EnterpriseDB Postgres. Using Swarm64 DA requires no SQL or coding changes, and it works with your PostgreSQL-compatible BI, ETL, and DBA tooling.
Data warehousing and analytics
- Migrate data warehouse workloads off of expensive, proprietary legacy platforms, and on to Swarm64 DA-accelerated PostgreSQL
- Reduce IT data warehousing costs by 60% to 90%
- State of the art analytic database functionality – parallel query processing, columnar indexing, data compression and more
- Works with all PostgreSQL-compatible tooling, BI, ETL
Scaling SaaS reporting & analytics
- Accelerate PostgreSQL-based reporting and analytics dashboards
- Support 3x to 5x more concurrent users with each accelerated server = scaling with fewer servers and lower cloud costs
- Less variance in query performance as you scale
Machine learning & time series
- Sub-second query performance on data streaming into PostgreSQL
- Run machine learning algorithms at scale
- Analyze larger data sets
- Faster machine learning testing & iteration
- Data partitioning for fast SQL time series analysis
- Text search acceleration for security & log analytics