Often kept separate, streaming and batch systems are typically complex to manage and costly to scale. But Snowflake keeps things simple by handling both streaming and batch data ingestion and transformation in a single system.
Stream row-set data in near real time with single-digit latency using Snowpipe Streaming, or auto-ingest files with Snowpipe. Both options are serverless for better scalability and cost-efficiency.
With Dynamic Tables (in public preview), you can use SQL or Python to declaratively define data transformations. Snowflake will manage the dependencies and automatically materialize results based on your freshness targets. Dynamic Tables only operate on data that has changed since the last refresh to make high data volumes and complex pipelines simpler and more cost-efficient.
As business needs change, you can easily adapt by making a batch pipeline into a streaming pipeline with a single latency parameter change.
Bring your workloads to the data to streamline pipeline architecture and eliminate the need for separate infrastructure.
Bring your code to the data to fuel a variety of business needs—from accelerating analytics to building apps to unleashing the power of generative AI and LLMs. Thanks to Snowpark, this code can be in whichever language you prefer, whether that’s SQL, Python, Java or Scala.
Code with Python, Java or Scala using Snowpark’s set of libraries, such as DataFrame API, and runtimes, including UDFs and stored procedures. Then securely deploy and process your code where your data is—all with consistent governance in Snowflake.
With Snowpark, customers see a median of 3.5x faster performance and 34% lower cost compared to managed Spark solutions.1
With the Data Cloud, you’ll have a vast network of data and applications at your fingertips.
Easily access and distribute data and applications with direct access to live data sets from Snowflake Marketplace, which reduces the costs and burden associated with traditional extract, transform and load (ETL) pipelines and API-based integrations. Or, simply use native connectors to bring data in.
By migrating to Snowpark for their data engineering workload, Openstore now processes 20x more data while reducing operational burden and achieving 100% PySpark code parity.
Decrease in pipeline runtime
Reduction in engineering maintenance hours required