![]() We work through a simple scenario where you might need to incrementally load data from Amazon Simple Storage Service (Amazon S3) into Amazon Redshift or transform and enrich your data before loading into Amazon Redshift. If you haven’t tried AWS Glue interactive sessions before, this post is highly recommended. When the code is ready, you can configure, schedule, and monitor job notebooks as AWS Glue jobs. ![]() You can also start a notebook through AWS Glue Studio all the configuration steps are done for you so that you can explore your data and start developing your job script after only a few seconds. This enables you to author code in your local environment and run it seamlessly on the interactive session backend. Interactive sessions provide a Jupyter kernel that integrates almost anywhere that Jupyter does, including integrating with IDEs such as P圜harm, IntelliJ, and Visual Studio Code. You can also use Jupyter-compatible notebooks to visually author and test your notebook scripts. You can create and work with interactive sessions through the AWS Command Line Interface (AWS CLI) and API. There are different options to use interactive sessions. Interactive sessions is a recently launched AWS Glue feature that allows you to interactively develop AWS Glue processes, run and test each step, and view the results. If you prefer a code-based experience and want to interactively author data integration jobs, we recommend interactive sessions. AWS Glue provides both visual and code-based interfaces to make data integration simple and accessible for everyone. If you’re looking to simplify data integration, and don’t want the hassle of spinning up servers, managing resources, or setting up Spark clusters, we have the solution for you.ĪWS Glue is a serverless data integration service that makes it easy to discover, prepare, and combine data for analytics, ML, and application development. ![]() Most organizations use Spark for their big data processing needs. This is one of the key reasons why organizations are constantly looking for easy-to-use and low maintenance data integration solutions to move data from one location to another or to consolidate their business data from several sources into a centralized location to make strategic business decisions. Data integration becomes challenging when processing data at scale and the inherent heavy lifting associated with infrastructure required to manage it. Data is growing exponentially and is generated by increasingly diverse data sources. Organizations are placing a high priority on data integration, especially to support analytics, machine learning (ML), business intelligence (BI), and application development initiatives. ![]()
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