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Amazon Q data integration adds DataFrame support and in-prompt context-aware job creation
- Amazon Q data integration allows natural language author ETL jobs in AWS Glue. It introduces exciting new capabilities that make ETL development more efficient and intuitive such as support for DataFrame-based code generation and in-prompt context-aware development.
- The DataFrame code generation works across different data sources and destinations such as Amazon S3 data lakes, relational databases, and NoSQL databases. It can handle complex data processing requirements such as filters, projections, unions, joins, and aggregations.
- Amazon Q data integration simplifies data engineering tasks by providing users with an LCNC ETL workflow. It comes with various capabilities such as prompts, multi-turn chat capabilities, and in-prompt context awareness to incrementally update the data integration flow.
- Amazon Q data integration is available through the Amazon Q chat experience on the AWS Management Console and the Amazon SageMaker Unified Studio preview. The generative visual ETL in the SageMaker Unified Studio also allows refinement of ETL workflow with new requirements, enabling incremental development.
- Amazon Q data integration is available in the SageMaker Unified Studio notebook experience. Users can add a new cell and enter what they want to achieve, and the recommended code is shown.
- Amazon Q data integration is also available in AWS Glue Studio. Users can ask Amazon Q a question to create a Glue ETL flow, and the code with all configurations in place is generated. They can copy and paste the generated code to the script editor and run the job when ready.
- These new capabilities significantly reduce development time and complexity, making it more intuitive and time-efficient for data practitioners building data applications on AWS.
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