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
While 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 new practices and integration with existing workflows.
New native capabilities
Openflow and dbt Projects on Snowflake 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.
Automate development
Simplify the development life cycle with emphasis on CI/CD, deployment automation and infrastructure management.
Benefits
Building and Orchestrating with SQL and Python in Snowflake
Empower teams via SQL Pipelines
Ease the load on data engineers with accessible data pipelines in SQL
- SQL pipelines’ modularity enables users with varied SQL skills to execute numerous pipelines at scale reliably, creating an adaptable data workflow foundation.
- Focus on writing SQL code with Snowflake virtual warehouses, fully managed compute.
- Simplify pipeline configuration with automatic orchestration and continuous, incremental data processing with Dynamic Tables.
- Build, deploy and govern dbt Projects with native support on Snowflake.
Build and scale with Python pipelines
Enable enterprise-grade Python development
- Using familiar Python syntax, complex transformations execute seamlessly within Snowflake’s elastic engine, eliminating data movement for efficient, large-scale data processing.
- Handle growing data volumes and processing demands without infrastructure overhead, offering a powerful and scalable Python solution with Snowpark.
- Use pandas on Snowflake to simplify and scale development using this familiar syntax for flexible data transformations.
- Improve performance and lower cost on complex data transformations in Apache Spark.
Add Automation
Orchestrate data pipelines
- Schedule and automate dbt projects on Snowflake with native orchestration or use a third-party orchestrator.
- 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.
