Real-time demands from business across multiple industries has impacted the ETL landscape for data scientists. The ETL (Extract, Transform, Load) process can be lengthy and laborious. To generate usable data quickly, ETL pipelines must be constantly streaming 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 innately supports multiple data sources and programming languages. Whether relational data or semi-structured data, such as JSON, Spark ETL delivers clean data. Converting SQL to JSON is easy with Spark ETL.
Spark data pipelines have been designed to handle enormous amounts of data.
SNOWFLAKE AND SPARK ETL
Snowflake's built-for-the-cloud data warehouse runs exceptionally well with Spark. Its shared data architecture can be scaled up or down instantly.
Snowflake enables the loading of semi-structured data directly into a relational table. ETL testing is no longer needed, and data ETL becomes more accurate and faster.