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Author visual ETL flows on Amazon SageMaker Unified Studio (preview)
- Amazon SageMaker Unified Studio (preview) provides an integrated data and AI development environment within Amazon SageMaker.
- Unified Studio allows you to build faster using familiar AWS tools for model development, generative AI, data processing, and SQL analytics.
- Visual ETL is a new visual interface that makes it simple for data engineers to author, run, and monitor extract, transform, load (ETL) data integration flow.
- You can use a simple visual interface to compose flows that move and transform data and run them on serverless compute.
- Visual ETL also automatically converts your visual flow directed acyclic graph (DAG) into Spark native scripts, enabling a quick-start experience for developers who prefer to author using code.
- This post shows how you can build a low-code and no-code (LCNC) visual ETL flow that enables seamless data ingestion and transformation across multiple data sources.
- The TICKIT dataset records sales activities on the fictional TICKIT website, where users can purchase and sell tickets online for different types of events such as sports games, shows, and concerts.
- The process involves merging the allevents_pipe and venue_pipe files from the TICKIT dataset.
- The data is then aggregated to calculate the number of events by venue name.
- Generative AI can enhance your LCNC visual ETL development process, creating an intuitive and powerful workflow that streamlines the entire development experience.
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