01
Streaming ingestion
Ingest from Kafka, webhooks, SDKs and CDC streams with exactly-once delivery and automatic schema detection.
Platform
One platform for real-time data, start to finish
Catalyst combines ingestion, modeling, querying and visualization into one fast, dependable workspace.
02
Columnar query engine
A vectorized engine with smart caching returns most queries in under 100 milliseconds, even on billions of rows.
03
Visual pipeline builder
Drag-and-drop transforms with branching, retries and dead-letter queues. Export any pipeline as SQL.
04
Dashboards & reports
Interactive charts, filters and drill-downs. Schedule PDF or CSV reports to any inbox.
05
Anomaly detection
Catalyst learns normal patterns in your metrics and flags unusual spikes or drops automatically.
06
Governance
Row-level security, column masking, SSO (SAML/OIDC), SCIM provisioning and a full audit trail.
07
API & embedding
Query anything over a REST API or embed dashboards inside your own product with signed URLs.
08
Version control
Every query, model and dashboard keeps a full history with diff, restore and Git sync.
Performance
Fast by design, not by accident
Catalyst stores data in a compressed columnar format and pushes filters down to the storage layer. Hot results are cached at the edge, and heavy queries are split across workers automatically.
- Median query latency of 42ms on production workloads
- Automatic partitioning and clustering
- Concurrency scaling for busy Monday mornings
- Predictable, flat per-seat pricing — no compute surprises
Integrations
Connect your stack in minutes
Native connectors for the sources and destinations teams use most. Need something else? Use our open API.
PostgreSQLMySQLSQL ServerBigQuerySnowflakeRedshiftDatabricksAmazon S3Google Cloud StorageAzure BlobKafkaKinesisMongoDBStripeShopifyHubSpotSalesforceGoogle Analytics 4SegmentZendeskIntercomJiraSlackMicrosoft Teams
Compare
Catalyst vs. a traditional warehouse setup
| Catalyst | Legacy stack | |
|---|---|---|
| Time to first dashboard | Same day | 2–6 weeks |
| Data freshness | Seconds | Hours (batch) |
| Tools to manage | 1 | 4–6 |
| Pricing model | Flat per seat | Compute + storage + seats |
| Support | U.S.-based humans | Ticket queue |