“The data foundation we built with Snowflake allows us to adapt quickly while keeping client data secure and private.”
John Saunders
VP of Product, Power Digital
Product Category
Snowflake for Data Engineering
Focus on data quality rather than infrastructure tuning. Now you can harness the full potential of your data from birth to insights with ZeroOps data engineering, limitless interoperability and enterprise-grade AI.
Overview
Take your data from raw to AI-ready
Enable reliable data movement
Build, deploy and optimize data pipelines faster
- Streamline the entire pipeline lifecycle and quickly adopt new data engineering practices — or merge with existing workflows.
- Democratize data engineering with Snowflake’s end-to-end workflows, which include a growing set of native capabilities and tight integrations with open standards and data engineering-specific tools.
Centralize data storage in open format
Build or seamlessly integrate your open lakehouse
- Take advantage of flexible open source standards to build your open lakehouse faster, while still getting the reliability and security of the AI Data Cloud.
- Seamlessly integrate with existing lakehouse architectures or build with an open lakehouse format from the ground up, breaking down data silos and getting to value faster
Innovate with AI
Unlock the potential of even the most advanced AI use cases
- Access and process an array of unstructured and semi-structured data with an open, flexible architecture.
- By simplifying the complexities of data engineering across the entire pipeline, Snowflake makes it surprisingly easy to build generative AI applications, enable AI agents with near real-time data flows and more.
Developer Experience
Manage delivery of your AI data foundation
- Focus more efficiently on your work with developer productivity tools like native development environments, Git integration, and observability views and alerts.
- DevOps in Snowflake allows you to streamline and automate the software development lifecycle for all your Snowflake environments.
Benefits
Setting new standards for data engineering
Zero Ops data engineering
Code and automate pipelines with confidence
Meet data SLAs, automate repetitive tasks and deliver results that make a real impact. By focusing on outcomes instead of infrastructure, you can be free of operational overhead through native data engineering capabilities and integration with open standards with Snowflake Openflow, dbt Projects, pandas, Iceberg, and more.
Limitless Interoperability
Lower TCO, improve performance and reduce vendor lock-in
Build without borders with Snowflake’s end-to-end data engineering platform that interoperates with the technologies you know and love, both within the platform and outside it.
- Now you can free data movement between data sources and destinations with Snowflake Openflow — an open, extensible, managed, multi-modal data integration service.
- Streamline data transformation workflows with dbt Projects on Snowflake.
- Bring code to data and jumpstart development across coding languages with Snowpark.
Turbocharge AI
Deliver on the promise of AI
Enable AI agents to collaborate, share context, and make decisions at machine speed with support for structured and unstructured formats in near real-time, bi-directional data flows.
With Snowflake’s enterprise-grade features built across the platform, you can power even your most advanced business solutions with agile, efficient and reliable data architecture.
Resources
Explore more in data engineering
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 Engineering
Frequently Asked Questions
Find answers to most common questions about Snowflake’s data engineering capabilities, from pipeline creation to AI assistants
Does Snowflake support data pipeline creation?
Yes, Snowflake provides comprehensive support for creating robust and scalable data pipelines, including efficient data ingestion from various sources, data transformation capabilities and optimized storage. Snowflake also offers robust observability and governance features, ensuring your pipelines are reliable, secure and easy to manage.
What open source standards does Snowflake support?
Key open source technologies and standards supported by Snowflake include Apache Iceberg, a popular open table format for huge analytic datasets. We also offer strong integration with dbt for data transformation, support for Modin to scale pandas workflows and empower data application development with Streamlit. Snowflake also integrates with tools like Apache NiFi for data ingestion.
What storage formats does Snowflake support?
Snowflake offers different data storage capabilities, supporting a wide array of formats. You can store and analyze structured data (including Apache Parquet), semi-structured data (such as JSON, Avro, XML) and unstructured data (like images, videos, PDFs) all within a single platform.
Does Snowflake have AI assistance in their data pipelines?
Absolutely! For instance, Document AI allows you to extract valuable insights from documents. For developers, Snowflake Copilot offers coding assistance to streamline the development of data pipelines and applications. With Snowflake Cortex LLM, you gain access to powerful AI functions that enable you to perform tasks such as text completion, classification, extraction, parsing, sentiment analysis, summarization, translation and generating embeddings. You can find more details in our Snowflake Cortex LLM Functions documentation.
What are the costs for data engineering on Snowflake?
The two primary cost drivers are compute and storage. For compute resources, Snowflake employs a pay-for-what-you-use model. Storage costs are based on the amount of data (measured in terabytes per month) stored within Snowflake. To get a detailed breakdown of our pricing and see our consumption table, we encourage you to visit Snowflake Pricing Page that has the most up-to-date and comprehensive information.
