Use Case
Build Better Data Pipelines
Empower data engineers to build, deploy and optimize data pipelines faster with end-to-end workflows — democratizing data engineering.
Overview
Streamline the entire data pipeline lifecycle with Snowflake
Building resilient pipelines with strong data integrity can be challenging. Snowflake's native capabilities and tight integrations with open standards and data engineering practices streamline the adoption of development best practices while integrating and modernizing existing workflows.
Build, test and deploy pipelines with new native workflow capabilities
Snowpark Connect, Openflow, dbt Projects on Snowflake, and Dynamic Tables provide intuitive interfaces that allow teams to collaborate across their organizations and scale data engineering directly within Snowflake.
Remove operational overhead and performance bottlenecks
Take advantage of managed compute and stop tuning infrastructure. Instead, rely on performant and highly optimized serverless transformations and orchestration options.
Put AI to work alongside you and your team
Accelerate development with a purpose-built IDE with Snowflake Workspaces that includes an integrated and knowledgeable coding assistant, Cortex Code.
Benefits
Building and Orchestrating with SQL and Python in Snowflake
Advanced declarative workflows
Build expressive pipelines that go beyond just moving data
Eliminate the need for manual orchestration and reduce resource consumption using Dynamic Tables for efficient incremental updates.
Build, test, deploy and monitor data transformations with dbt directly in Snowflake via dbt Projects.
Expedite development with a purpose-built IDE for data engineering, Workspaces.
- Augment data engineering work with an intelligent coding assistant, Cortex Code.
Accelerate Apache SparkTM and Python pipelines
Power high-performance pipelines at enterprise scale
Run your existing Apache Spark code on Snowflake’s engine using Snowpark Connect, now with support for Java, Scala and Python.
Use Snowpark’s native Python support to seamlessly access diverse data sources, newly added capabilities from external databases via DB-API to XML files with Rowtag reader.
Unlock faster performance and lower costs with zero operational overhead.
Add Automation
Orchestrate data pipelines
- Automated orchestration is embedded into transformation workflows while providing a reliable, scalable framework for consistent execution — without the operational overhead.
- Define the end state and Snowflake automatically manages refreshes with Dynamic Tables.
- Run commands on a schedule or defined triggers with Snowflake Tasks.
- Chain tasks together defining a directed acyclic graph (DAGs) to support more complex periodic processing.
- Optimize task execution with Serverless Tasks.
“Now, we aren’t so focused on how to build things. We are focused more on what to build.”
Dan Shah
Manager of Data Science
- 1 week for 130 Dynamic Tables to be in production after migration
- 65% cost savings switching from Databricks to Snowflake
Resources
Start Building and Orchestrating Pipelines on Snowflake
Get Started
Take the next stepwith Snowflake
Start your 30-day free Snowflake trial today
- $400 in free usage to start
- Immediate access to the AI Data Cloud
- Enable your most critical data workloads
Data Pipelines
Frequently Asked Questions
Learn about effectively building and managing data pipelines in Snowflake. Explore supported types, efficient data handling techniques and more.
A data pipeline is a series of processes and tools that automate the movement and transformation of data from its origin (source systems) to a destination (like a data warehouse or data lake) for storage and analysis. Essentially, it's how raw data is ingested, processed and made ready for insights, AI, apps and other downstream use cases.
Common data pipeline types include:
Batch Pipelines: Process large volumes of data at scheduled intervals.
Streaming Pipelines: Process data in real-time or near real-time as it's generated.
Microbatch Pipelines: A hybrid approach, processing data in small, frequent batches, offering a balance between batch and streaming.
Yes, Snowflake supports these approaches with an array of features depending on the data engineering persona and needs.
Snowflake offers several features that handle both transformation and data orchestration. Dynamic Tables in Snowflake can automate refresh schedules for transformations. Snowflake Tasks can be chained into task graphs (DAGs) for orchestrating SQL and Python transformations. While tools like dbt focus on transformation, they integrate with Tasks or external orchestrators (e.g., Apache Airflow) for full pipeline orchestration.
You can manage dependencies natively in Snowflake using Snowflake Tasks. By creating task graphs, you define the execution order, ensuring that subsequent steps only run after their prerequisite tasks have successfully completed. If Dynamic Tables are used, dependencies are managed automatically by Dynamic Tables.
No, you don't always need to build a custom data pipeline from scratch. There are different ways for data engineers to interact with different parts of a data pipeline. Take data loading & ingestion as an example. Depending on your needs, alternatives can include: using data integration tools (like Snowflake Openflow), accessing data shares directly via Snowflake Marketplace, or leveraging Snowflake’s secure data sharing if the data is already in another Snowflake account.
No, it's not always necessary to ingest data into Snowflake's internal managed storage before performing transformation work. Snowflake facilitates different architectures including lakehouse, so you can use Snowflake to perform transformations on data residing in your external cloud storage leveraging Apache Iceberg tables using External Tables or Apache Iceberg tables. This allows you to work with data in place without always ingesting it into Snowflake's managed storage.
