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Where SynxDB fits.

SynxDB is general-purpose — but general-purpose only matters if it lands cleanly in your specific shape of work. Here's where teams are putting it, including a dedicated migration path for Greenplum users.

Greenplum migration

Move off an aging Greenplum deployment without rewriting your SQL. Same lineage, same ops model, same tooling — on a modern, actively developed engine. Cloud or self-managed target.

Good fit when

  • Greenplum 6.x hitting end-of-cadence
  • Stable SQL + ops playbooks you want to keep
  • Need a Cloud or self-managed target under your control

Migration guide →

Enterprise data warehouse

Replace the aging on-prem MPP warehouse with something cloud-native. Keep the SQL, lose the forklift upgrades. Stars, snowflakes, slowly-changing dimensions — all the classic shapes, at warehouse scale.

Good fit when

  • Multi-tenant BI and dashboarding
  • Monthly-close reporting with deterministic runtimes
  • dbt + your favorite orchestrator

Unified lakehouse analytics

Your Parquet/Iceberg data lake and your warehouse, one SQL surface. Query external tables in place, materialize hot slices to native storage, keep cold data on S3 at S3 prices.

Good fit when

  • Federated queries across S3 + native tables
  • Iceberg, Hudi, and Delta as first-class external sources
  • Transform-on-read or transform-on-write — your choice

Real-time analytics & HTAP

Ingest continuously, query immediately. Hybrid row/column storage (PAX) keeps fresh writes fast to read without waiting for a batch rebuild. Good for operational dashboards and event-shape queries.

Good fit when

  • Sub-minute freshness on operational data
  • Kafka-driven ingest with SQL consumers
  • Mixed read/write workloads on the same table

AI feature store & embeddings

Native vector storage and search means your embeddings live next to the features that generated them. Train, retrieve, and serve from the same database your analysts already query.

Good fit when

  • Vector similarity search over embeddings
  • Feature engineering in SQL, close to the data
  • Retrieval for RAG without a separate vector DB

Spin up a warehouse. Run a query. See for yourself.

Early access is open. A starter cluster is free while we're in preview — no credit card, no sales call.

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