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What is Spark ETL?

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Business demands across multiple industries has impacted the ETL landscape for data engineering, data science and machine learning. The ETL (Extract, Transform, Load) process can be lengthy and laborious. To generate usable data quickly, ETL pipelines must be constantly continuous data, churning, and loading data.

Apache Spark provides the framework to up the ETL game. Data pipelines enable organizations to make faster data-driven decisions through automation. They are an integral piece of an effective ETL process because they allow for effective and accurate aggregating of data from multiple sources.

Spark was known for innately supporting multiple data sources and programming languages. Whether relational data or semi-structured data, such as JSON, Spark ETL delivers clean data. 

Spark data pipelines have been designed to handle enormous amounts of data.

Snowflake and Spark ETL

Snowflake’s Snowpark delivers the benefits of Spark ETL with none of the complexities.

Snowflake’s Snowpark framework brings integrated, DataFrame-style programming to the languages developers like to use and performs large-scale data processing, all executed inside of Snowflake for ETL jobs. Here are just a few of the things that organizations are accomplishing using Snowpark.

  • Improve collaboration: Bring all teams to collaborate on the same data in a single platform that natively supports everyone’s programming language and constructs of choice, including Spark DataFrames.

  • Accelerate time to market: Enable technical talent to increase the pace of innovation on top of existing data investments with native support for cutting-edge open-source software and APIs. 

  • Lower total cost of ownership: Streamline architecture to reduce infrastructure and operational costs from unnecessary data pipelines and Spark-based environments.

  • Reduce security risks: Exert full control over libraries being used. Provide teams with a single and governed source of truth to access and process data to simplify data security and compliance risk management across projects.

Thanks to Snowflake’s Snowpark, organizations can achieve lightning-fast data processing for ETL jobs and data pipelines via their developer’s favorite programming languages and coding constructs. At the same time, they can enjoy all the advantages that the Snowflake Data Cloud offers.

In addition, Snowflake's platform can also connect with Spark. The Snowflake Connector for Spark keeps Snowflake open to connect to some complex Spark workloads. 

To learn more about Snowpark, watch an on-demand session on What’s New with Snowpark. 

Download the eBook: Moving from On-Premise ETL to ELT.