AWS Glue Studio now offers a new visual user experience for authoring data preparation transformations using its visual editor. This graphical interface allows users to create, run, and monitor data integration jobs within the AWS Glue ecosystem. The new data preparation interface in AWS Glue Studio provides a spreadsheet-style view for working with tabular data interactively, enabling users to validate recipe steps in real-time and author data preparation recipes without the need for coding.
To demonstrate this new feature, let’s consider an example e-commerce use case involving an apparel company and customer reviews. In this scenario, a data analyst is tasked with preprocessing raw customer review data for downstream analytics. By leveraging AWS Glue Studio’s visual editor, the analyst can easily create ETL jobs to transform the data, generate Python scripts, and output the results to Amazon S3. Additionally, the Data Catalog table created by AWS Glue can be queried using Amazon Athena by the analyst team for further analysis.
To begin using this new capability, users must set up prerequisites such as an S3 bucket for storing output data, a Data Catalog database, and IAM roles for the AWS Glue job and console user. These steps ensure the smooth execution of data integration tasks within AWS Glue Studio. Following the setup, users can author and run data integration jobs using the interactive data preparation experience provided by AWS Glue Studio.
Through a step-by-step process outlined in the content, users can create ETL jobs, inspect data, author data preparation recipes, transform data using a variety of prebuilt transformations, and monitor the results within AWS Glue Studio. The visual interface simplifies the process of building ETL workflows tailored to specific business needs without manual coding.
Once the ETL job is completed, users can query the transformed output data using Amazon Athena to derive insights for analysis. By executing SQL queries against the processed dataset, users can extract valuable information, such as top-reviewed items and attributes based on user-defined criteria.
In conclusion, the new AWS Glue data preparation authoring experience streamlines the process of creating ETL workflows and generating datasets for analytics purposes. By offering a low-code, no-code solution, AWS Glue Studio empowers users to quickly build scalable data transformation pipelines without the typical manual scripting overhead. This feature is now publicly available, allowing users to explore and leverage data preparation recipes to enhance their data integration workflows.
Article Source
https://aws.amazon.com/blogs/big-data/author-data-integration-jobs-with-an-interactive-data-preparation-experience-with-aws-glue-visual-etl/
