The exponential growth of data and the need to ingest, transform, and analyze it at scale presents new challenges for organizations that use traditional technologies such as Hadoop, Hive, and Apache Spark. Those challenges include complex infrastructure, scalability issues, security, siloed data, and more.
By migrating certain Spark workloads to Snowflake, organizations can take advantage of the Snowflake platform’s near-zero maintenance, data sharing capabilities, and built-in governance, as well as the ability to use their programming language of choice via Snowpark. With Snowpark, data engineers and developers can use Python, Java or Scala with familiar DataFrame and custom function support to build powerful and efficient pipelines, machine learning (ML) workflows, and data applications.
Find out if your organization may benefit from migrating your Spark workloads to Snowflake in our Spark to Snowflake Migration Guide.
We cover:
- Snowflake features and capabilities to consider in your decision, including data sharing and Snowpark
- Four questions to help you determine which workloads may be the right fit for migration
- What challenges you may be able to address by migrating those workloads to Snowflake
- A checklist of information to gather before you start the migration
- A four-step migration plan