
Stream into Imply Polaris with your free account
Deliver data from databases, SaaS apps, APIs, files, and streaming systems into Imply Polaris using Estuary's pre-built connectors. Run pipelines from real-time streaming to scheduled batch based on connector capabilities.
- <100ms to batch
- 200+ connectors
- Managed data pipelines



Imply Polaris connector details
The Imply Polaris materialization connector in Estuary streams data from Estuary collections to Imply Polaris as Kafka-compatible messages using Dekaf. Imply Polaris can consume these streams through its Kafka ingestion interface for real-time analytics on streaming and batch data.
- Kafka-compatible streaming from Estuary collections to Imply Polaris
- No separate Kafka cluster required — Imply Polaris connects directly to Estuary’s Dekaf endpoint
- One or more Estuary collections can be exposed as Kafka-compatible topics
- Configurable topic names for individual collection bindings
- Avro message format for ingestion into Imply Polaris
- Schema Registry support, allowing Imply Polaris to detect the Avro schema automatically
- Kafka or CDC deletion modes for controlling how deletions are represented
- Optional strict Kafka-compatible topic naming
- SASL/SSL authentication using the materialization name and an authentication token
To connect Imply Polaris, configure a Kafka input using dekaf.estuary-data.com:9092 as the bootstrap server, SASL_SSL with the PLAIN mechanism, and the Estuary materialization name and auth token as the Kafka credentials. Imply Polaris can then ingest the Avro stream and detect its schema through Estuary’s Schema Registry endpoint.
How to connect your data source to Imply Polaris in 3 easy steps
- 1
Connect your data source
Select from more than 100 supported databases and SaaS platforms including PostgreSQL, MySQL, SQL Server, MongoDB, and Kafka.
- 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
Sync to Imply Polaris
Continuously or periodically deliver data into your destination with support for change data capture and reliable delivery for accurate insights.
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.
Read success storyTogether AI
How Together AI Uses Near-Real-Time Data to Understand Inference Economics
Read success storyEnvoy
Right-Time Visibility for a High-Velocity Business: Envoy’s Estuary Story
Read success storyColtene
From SAP on SQL Server to Snowflake: How Coltene Built Reliable Replication with Estuary
Read success storyGlossier
Glossier Runs Real-Time Supply Chain and Marketing Analytics with Estuary

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. Bring data from databases, SaaS apps, APIs, files, and event streams into Imply Polaris, with capture and delivery cadence based on connector capabilities and pipeline configuration.
- Connect data from operational databases, SaaS applications, APIs, files, and streaming systems to Imply Polaris through Estuary.
- Transform and route data through Estuary collections before delivering it to Imply Polaris.
See what you can connect to Imply Polaris:
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 |
One platform for real-time and batch data pipelines

Deliver real-time and batch data from DBs, SaaS, APIs, and more
















