Move your data from Impact with your free account
Capture data from Impact and deliver it to your destinations using Estuary's pre-built connectors. Build pipelines for real-time, incremental, or batch data movement based on connector capabilities.
- <100ms to batch
- 200+ connectors
- Managed data pipelines
Impact connector details
- Log-based CDC for high-performance, low-impact data capture
- Automatic schema evolution to handle changes in source structure without manual intervention
- Unified streaming and batch ingestion in the same pipeline
- Hybrid deployment and BYOC support for security and control
- Fault-tolerant pipelines that resume automatically from the last checkpoint
- Kafka API connectivity for direct integration into streaming ecosystems
How to connect Impact to your destination in 3 easy steps
- 1
Connect Impact as your data source
Securely connect Impact and choose the objects, tables, or collections you need to sync.
- 2
Prepare and transform your data
Apply transformations and schema mapping as data moves whether you are streaming in real time or loading in batches.
- 3
Deliver to your destination
Continuously or periodically deliver your data to the destination you choose, based on the capabilities and configuration of your pipeline.
Learn more with some related videos
Dive deeper into Impact with tutorials and walkthroughs from our YouTube channel.
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PostgreSQL CDC: What, Why, and How
Set up PostgreSQL change data capture (CDC) without worrying about your WAL or replication slot loss. This step-by-step Postgres CDC guide covers WAL configuration, replication user setup, publication creation, and building your first real-time pipeline to Snowflake, Iceberg, or ClickHouse, no Kafka or Debezium required.
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Don’t know what to do with your data? Capture first, decide later
Estuary documents and collections always have an associated schema that defines the structure, representation, and constraints of your documents. Collections must have one schema, but may have two distinct schemas: one for when documents are added to the collection, and one for when documents are read from that collection.
![What are Schema Inference, Write and Read Schemas? video thumbnail]()
What are Schema Inference, Write and Read Schemas?
Flow documents and collections always have an associated schema that defines the structure, representation, and constraints of your documents. Collections must have one schema, but may have two distinct schemas: one for when documents are added to the collection, and one for when documents are read from that collection.
Trusted by data teams worldwide
All data connections are fully encrypted in transit and at rest. Estuary also supports private cloud and BYOC deployments for maximum security and compliance.
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HIGH THROUGHPUT
Distributed, event-driven architecture scales for demanding data workloads.
DURABLE COLLECTIONS
Store data as it moves so you can transform, replay, and deliver it downstream.
FLEXIBLE LATENCY
From <100ms CDC and real-time streaming to scheduled batch, depending on connector capabilities.
From <100ms to batch
Estuary supports CDC, real-time streaming, incremental syncs, and scheduled batch across its connector ecosystem. Capture data from Impact using the cadence supported by the connector, then transform and deliver it to the destinations your team uses.
- Connect Impact to warehouses, databases, data lakes, search platforms, and event systems through Estuary-supported destinations.
- Capture once and reuse Impact data across transformations and multiple downstream destinations without rebuilding source extraction.
See where you can send Impact data:
Don't see a connector?Request and our team will get back to you in 24 hours
Pipelines as fast as Kafka, easy as managed ELT/ETL, cheaper than building it.
Feature Comparison
| Estuary | Batch ELT/ETL | DIY Python | Kafka | |
|---|---|---|---|---|
| Price | $ | $$-$$$$ | $-$$$$ | $-$$$$ |
| Latency | <100ms to scheduled | 5min+ | Varies | <100ms |
| Ease | Analysts can manage | Analysts can manage | Data Engineer | Senior Data Engineer |
| Scale | ||||
| Maintenance Effort | Low | Medium | High | High |
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