Reactor + Databricks

The ultimate data pipeline for Databricks.

Automate ELT workflows with low-code pipelines. Enable real-time data ingestion for AI, analytics, and predictive modeling.

Why Reactor + Databricks

Built for Databricks-native teams.

Low-code ELT automation

Refocus your engineering team away from redundant data plumbing. Build, deploy, and adapt pipelines without writing custom code.

Real-time data ingestion

Stream data for AI, analytics, and predictive modeling. No batch lag, no stale insights.

COPY-INTO from 500+ sources

Connect to APIs, databases, and SaaS apps. Reactor lands data into your defined Databricks tables and fields in real-time.

Delta Lake ready

Output to Delta tables natively. Optimized for time-travel, ACID transactions, and unified analytics workloads.

Delta ingest

Two-step process for Databricks.

Connect your sources, define your target Delta tables, Reactor handles everything in between. Your data lands in Databricks in the exact structure your ML, analytics, and BI tools expect.

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Connect sources + define schema

500+ connectors. Define your Delta table structure visually.

Reactor maps and lands data

Real-time COPY-INTO, with semantic mapping applied at ingest.

FAQ

Got questions?

Common questions about Reactor + Databricks.

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01How does Reactor work with Delta Lake?

Reactor lands directly into Delta tables with full schema enforcement. Your Databricks notebooks and ML workflows get clean, business-ready data without additional transformation steps.

02Can I use Reactor with Databricks Unity Catalog?

Yes. Reactor respects your Unity Catalog governance and writes data with the lineage and metadata your catalog requires.

03What's the latency from source to Databricks?

P50 latency is around 250ms for streaming sources. Batch sources land on whatever cadence you configure.

Ready when you are

Power your Databricks workloads.

Get started with Reactor and accelerate your Databricks data initiatives today.