Are you considering a move from Snowflake to Databricks? You're part of a growing trend! Many data teams are making this transition to leverage Databricks' unified platform, which brings together data engineering, analytics, and machine learning capabilities. While Snowflake is a powerful data warehouse, Databricks offers greater flexibility, especially when dealing with big data, real-time analytics, or building AI models. It’s particularly appealing if you want to centralize your data, analytics, and machine learning workflows. Plus, recent benchmarks highlight that ETL workloads can be up to nine times more costly on Snowflake than on Databricks Lakehouse, and ETL often constitutes 50% or more of total data platform expenses.
Migrating effectively means more than just shifting data; it means ensuring your systems operate as well, if not better, afterward. To kick things off smoothly, begin by auditing your current Snowflake setup. List all your tables, views, stored procedures, and access roles so you know exactly what you're working with. Identify your key workloads—those high-priority tables or jobs that stand to gain the most from Databricks' processing power don't need to move everything at once. Also, assess any dependencies, such as BI dashboards or data pipelines connected to Snowflake, as they'll need to be reconnected to Databricks later. A phased migration, moving data in manageable chunks, is generally the safer route, allowing for thorough testing along the way.
When you're ready for the actual migration, the first step is to extract your data from Snowflake. You can use Snowflake's UNLOAD command to export data to cloud storage like S3 or Azure Blob, or employ ETL tools such as Fivetran or Matillion. Store your data in open formats like Parquet (which is excellent for speed and performance) or CSV. Before loading into Databricks, transform your data as needed—checking for data type compatibility, adjusting column names, or reapplying business rules. Databricks allows you to do this directly using PySpark, SQL, or Delta Live Tables. The final step is loading into Databricks: set up your workspace and a cluster, mount your storage, and write your data to Delta Lake, which offers versioning, ACID transactions, and improved performance.
Of course, migrations aren't without their bumps. Common issues include data type mismatches (use casting functions ), handling very large volumes of data (partitioning helps significantly ), and recreating security permissions (Unity Catalog is invaluable here ). Remember to test everything in a staging environment to prevent problems with reports or dashboards. Ultimately, moving from Snowflake to Databricks isn't just a technical task; it's a strategic shift that can result in a faster, smarter system better equipped for modern data workflows.
Explore our step-by-step guide to migrating from Snowflake to Databricks.
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