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Snowflake for Data Engineering

Build powerful streaming and batch data pipelines in SQL or Python.

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Top Streaming Data Analytics Reference Architectures

Simplify Complex Data Engineering Requirements

Build streaming and batch data pipelines on a single platform with the power of declarative pipelines and cost-efficient incremental refresh.

Eliminate Unnecessary Pipelines With Data Sharing

Access live, ready-to-use data directly from thousands of data sets and apps via Snowflake Marketplace—all without having to build pipelines.

Code With Your Language of Choice in One Optimized Engine

Program in Python, SQL and more, then execute with Snowflake’s multi-cluster compute. No separate infrastructure required.

How It Works

Stream Data With <10-Second Latency

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.

Stream data with less than 10 second latency

Adjust latency with single parameter change

Adjust Latency With a Single Parameter Change

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.

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Power Data Engineering for Analytics, AI/ML and Applications

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.

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See 3.5x Faster Performance and 34% Cost Savings—Without Compromising Governance

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

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Build Fewer Data Pipelines With Easy Data Sharing

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.

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USE CASES

Break the Streaming and Batch Silos

Ingest and transform streaming and batch data in a single system.

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OUR CUSTOMERS

leaders use snowflakefor data engineering

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.

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