Category: AWS Glue

Organizations across all industries have complex data processing requirements for their analytical use cases across different analytics systems, such as data lakes on AWS, data warehouses (Amazon Redshift), search (Amazon OpenSearch Service), NoSQL (Amazon DynamoDB), machine learning (Amazon SageMaker), and more. Analytics professionals are tasked with deriving value from data stored in these distributed systems […]

Jul 2023: This post was reviewed and updated with Glue 4.0 support in AWS Glue Studio notebook and interactive sessions. Deriving insight from data is hard. It’s even harder when your organization is dealing with silos that impede data access across different data stores. Seamless data integration is a key requirement in a modern data […]

AWS Glue Studio is a graphical interface that makes it easy to create, run, and monitor extract, transform, and load (ETL) jobs in AWS Glue. It allows you to visually compose data transformation workflows using nodes that represent different data handling steps, which later are converted automatically into code to run. AWS Glue Studio recently […]

Hundreds of thousands of customers use AWS Glue, a serverless data integration service, to discover, prepare, and combine data for analytics, machine learning (ML), and application development. AWS Glue for Apache Spark jobs work with your code and configuration of the number of data processing units (DPU). Each DPU provides 4 vCPU, 16 GB memory, […]

The Transparency in Coverage rule is a federal regulation in the United States that was finalized by the Center for Medicare and Medicaid Services (CMS) in October 2020. The rule requires health insurers to provide clear and concise information to consumers about their health plan benefits, including costs and coverage details. Under the rule, health […]

In today’s data-driven business environment, organizations face the challenge of efficiently preparing and transforming large amounts of data for analytics and data science purposes. Businesses need to build data warehouses and data lakes based on operational data. This is driven by the need to centralize and integrate data coming from disparate sources. At the same […]

AWS Glue is a serverless data integration service that makes it simple to discover, prepare, and combine data for analytics, machine learning (ML), and application development. You can use AWS Glue to create, run, and monitor data integration and ETL (extract, transform, and load) pipelines and catalog your assets across multiple data stores. One of […]

This is a guest post co-written by Maik Leuthold and Nick Harmening from BMW Group. The BMW Group is headquartered in Munich, Germany, where the company oversees 149,000 employees and manufactures cars and motorcycles in over 30 production sites across 15 countries. This multinational production strategy follows an even more international and extensive supplier network. Like many automobile companies across the world, the […]

Implementing an effective data sharing strategy that satisfies compliance and regulatory requirements is complex. Customers often need to share data between disparate software as a service (SaaS) platforms within their organization or across organizations. On many occasions, they need to apply business logic to the data received from the source SaaS platform before pushing it […]

Building a data lake on Amazon Simple Storage Service (Amazon S3) provides numerous benefits for an organization. It allows you to access diverse data sources, build business intelligence dashboards, build AI and machine learning (ML) models to provide customized customer experiences, and accelerate the curation of new datasets for consumption by adopting a modern data […]