Introducing Snowpipe Streaming Support in StreamNative's Snowflake Streaming Sink Connector

As the landscape of data streaming and analytics continues to advance, the need for real-time, efficient data ingestion into powerful platforms like Snowflake has never been greater. StreamNative is proud to introduce the Snowflake Streaming Sink Connector, a fully managed connector available in StreamNative Cloud. This connector leverages Snowpipe Streaming ingestion, enabling enterprises to achieve sub-second data ingestion. With this cutting-edge solution, organizations can ensure seamless data flow to power real-time analytics and operational intelligence with unparalleled efficiency.

Furthermore, StreamNative is honored to be the first data ingestion partner to support Apache Iceberg with Snowflake’s Snowpipe Streaming capability. This achievement highlights our commitment to providing innovative integration capabilities, enabling Snowflake and StreamNative customers to unlock advanced data solutions.

StreamNative’s Integration with Snowflake

StreamNative has always prioritized robust data integration for customers needing quick and reliable data flow between systems. Our Snowflake Sink Connectors offer fully managed, low-latency data ingestion into Snowflake, empowering businesses to ingest real-time data streams into Snowflake within seconds. The Snowflake Sink connector supported only Snowpipe ingestion, which allowed for quick loading of data batches. However, with the Snowflake Streaming Sink connector, users can now ingest data with sub-second data ingestion and an ideal choice for real-time analytics use cases. This enhanced support aligns with StreamNative’s commitment to empowering businesses with flexible and resilient data streaming solutions.

Snowflake's Ingestion Methods: Snowpipe vs. Snowpipe Streaming

Snowflake provides two primary methods for data ingestion:

1. Snowpipe: This method leverages event-driven ingestion and is ideal for loading small batches of data frequently. Using REST API calls, Snowpipe automates the process of loading data into Snowflake as it arrives, generally within seconds. This is well-suited for scenarios where near-real-time data is sufficient.
   
   • Functionality: Facilitates continuous, automated loading of data files from cloud storage (e.g., Amazon S3, Google Cloud Storage) into Snowflake tables.
   • Process: Monitors specified cloud storage locations for new files. Upon detecting new data, it automatically triggers the loading process into the target tables.
   • Use Cases: Ideal for scenarios where data arrives in batches or micro-batches, such as periodic uploads of log files or transactional data.
   • Latency: Typically offers near real-time loading with latencies ranging from a few seconds to minutes, depending on file arrival and processing times.
2. Snowpipe Streaming: Snowflake’s newer streaming ingestion feature, Snowpipe Streaming, enables ultra-low-latency data ingestion, achieving sub-second intervals for data availability. It is designed for applications needing immediate access to data as soon as it arrives, enhancing Snowflake’s support for real-time analytics and operational dashboards.
   
   • Functionality: Enables low-latency ingestion of streaming data directly into Snowflake tables without the need for intermediary cloud storage. 
   • Process: Utilizes the Snowflake Ingest SDK to allow applications to write data rows directly into Snowflake over HTTPS. This method supports real-time data ingestion from sources like IoT devices, application logs, or Kafka topics.
   • Use Cases: Suited for applications requiring real-time analytics and immediate data availability, such as monitoring systems, real-time dashboards, or event-driven architectures.
   • Latency: Achieves lower latencies, often within seconds, due to the direct ingestion approach.

The key difference is latency; while Snowpipe is near real-time, Snowpipe Streaming is designed to handle real-time demands, ensuring that data is available in Snowflake almost instantaneously.

Support for Snowflake Ingestion Methods in StreamNative’s Snowflake Sink Connectors

With StreamNative’s Snowflake Sink Connector, users can now choose between Snowpipe and Snowpipe Streaming ingestion modes based on their specific latency requirements and use case demands. Here’s a closer look at both options:

Efficient SNOWPIPE Integration With Snowflake Sink Connector

To facilitate data ingestion from StreamNative to Snowflake using the SNOWPIPE method, users can leverage StreamNative's Snowflake Sink Connector.

This connector allows users to configure Snowflake’s traditional Snowpipe ingestion method, ideal for users looking to frequently load batches of data with a few seconds of latency. This is a fully managed connector available in StreamNative Cloud and it allows enterprises to stream data from Apache Pulsar topics stored in StreamNative Cloud to Snowflake AI Platform.

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Efficient SNOWPIPE STREAMING Integration With Snowflake Streaming Sink Connector

To facilitate data ingestion from StreamNative to Snowflake using the SNOWPIPE STREAMING method, users can leverage StreamNative's Snowflake Streaming Sink Connector. 

This connector allows users to configure Snowflake’s Snowpipe Streaming ingestion method, ideal for ultra-low-latency data ingestion, achieving sub-second intervals for data availability.

For real-time use cases, the Snowpipe Streaming ingestion method provides sub-second ingestion into Snowflake. 

Support for Apache Iceberg Format

The Snowflake Streaming Sink connector now supports the Apache Iceberg format, an open table format that simplifies data ingestion, transformation, and analytics. The Apache Iceberg format is essential for organizations that require schema evolution, version control, and partitioning—features that enhance data processing efficiency in real-time environments.

By default the Apache Iceberg format is disabled in the connector. Users can enable it by setting the icebergEnabled config to true.

Walkthrough of the Snowflake Sink Connector in StreamNative Cloud

Setting up the Snowflake Sink Connector in StreamNative Cloud is straightforward. Here’s a quick overview of how to get started with both Snowpipe and Snowpipe Streaming modes and enable Apache Iceberg format support:

1. Configuring the Connector:
   
   • From the StreamNative Cloud Console, users can access the Snowflake Streaming Sink Connector setup and configure all the required details like url, user, database, schema, role, warehouse etc.

2. Enabling Apache Iceberg Format Support:
   
   • Apache Iceberg support can be enabled within the connector configuration. This capability allows users to write data in the Iceberg format, providing advanced schema management and partitioning benefits.

3. Monitoring and Managing the Connector:
   
   • StreamNative Cloud provides a monitoring dashboard where users can track data ingestion progress, latency, and throughput. This visibility ensures users can manage their data flow and optimize performance effectively.

Conclusion

With this update, StreamNative’s Snowflake Sink Connectors become an even more powerful tool for enterprises looking to unlock real-time data analytics. Depending on the use case, enterprises can select the most suitable connector to leverage either Snowpipe or Snowpipe Streaming ingestion methods. Additionally, with support for the Apache Iceberg format, StreamNative provides a comprehensive and flexible data streaming solution designed to meet the diverse needs of modern data-driven organizations. Whether for real-time operational intelligence or large-scale data warehousing, the Snowflake Sink Connectors in StreamNative Cloud are purpose-built to support the next generation of data analytics.