StreamNative Introduces Lakestream Architecture and Launches Native Kafka Service
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Unlock Real-Time Data Streaming for the Databricks Lakehouse
Stream from any Kafka source directly into Delta Lake tables — no ETL, no copies, no delays. Powered by Ursa Engine.
Stream Directly into Unity Catalog Managed Tables
StreamNative now supports streaming Kafka and Pulsar data directly into Unity Catalog Managed Tables with automated governance, lifecycle management, and performance optimization.
• OVERVIEW
The Most Cost Effective Way to Ingest Real-Time Data into Lakehouse
StreamNative partners with Databricks to enable enterprises to seamlessly stream data into the Databricks Lakehouse Catalog. With multiple integration options available, organizations can select the best approach for their needs—whether for governance, storage efficiency, or advanced analytics.
Seamless Data Streaming
Easily stream real-time data into Databricks Lakehouse.
Flexible Integration Options
Choose from native Unity Catalog integration, Delta Lake streaming, or Spark connectors.
Optimized Performance
Ensure high-performance, cost-effective data streaming at scale.
• USE CASES
Integration Options for Your Databricks Lakehouse Catalog
Native Integration with Databricks Unity Catalog
Effortlessly configure out-of-the-box integration within StreamNative Cloud to stream data directly into Databricks Unity Catalog.
Unified data governance across real-time workloads
Simplified data management for seamless analytics
Lakehouse Connector
Use the Lakehouse Connector to sink streaming data into Databricks tables with configurable batching, partitioning, and schema management.
Configurable batch size and interval
Automatic table creation
Schema registry integration
Dead letter queue support
Pulsar Spark Connector
Read and write Pulsar topics from Databricks Spark jobs using the native Pulsar Spark connector for batch and structured streaming workloads.
Structured streaming support
Batch read from Pulsar topics
Exactly-once processing
Checkpoint-based recovery
Kafka Spark Connector
Connect Databricks Spark jobs to StreamNative using the standard Kafka Spark connector. No code changes required for existing Kafka workloads.
Native Kafka protocol support
No application code changes
Databricks SQL support
Real-time dashboards and alerts
• RESOURCES
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