Tag: AWS Glue

AWS has invested in native service integration with Apache Hudi and published technical contents to enable you to use Apache Hudi with AWS Glue (for example, refer to Introducing native support for Apache Hudi, Delta Lake, and Apache Iceberg on AWS Glue for Apache Spark, Part 1: Getting Started). In AWS ProServe-led customer engagements, the use cases we work on usually come with technical complexity and scalability requirements. In this post, we discuss a common use case in relation to operational data processing and the solution we built using Apache Hudi and AWS Glue.

The post Archive and Purge Data for Amazon RDS for PostgreSQL and Amazon Aurora with PostgreSQL Compatibility using pg_partman and Amazon S3 proposes data archival as a critical part of data management and shows how to efficiently use PostgreSQL’s native range partition to partition current (hot) data with pg_partman and archive historical (cold) data in […]

In the world of software engineering and development, organizations use project management tools like Atlassian Jira Cloud. Managing projects with Jira leads to rich datasets, which can provide historical and predictive insights about project and development efforts. Although Jira Cloud provides reporting capability, loading this data into a data lake will facilitate enrichment with other […]

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 […]

To build a data-driven business, it is important to democratize enterprise data assets in a data catalog. With a unified data catalog, you can quickly search datasets and figure out data schema, data format, and location. The AWS Glue Data Catalog provides a uniform repository where disparate systems can store and find metadata to keep […]

AWS Glue is a serverless data integration service that makes it easy to discover, prepare, and combine data for analytics, machine learning (ML), and application development. It’s serverless, so there’s no infrastructure to set up or manage. This post provides a step-by-step guide to build a continuous integration and continuous delivery (CI/CD) pipeline using AWS […]

If you are currently running Amazon EMR 5.X clusters, consider moving to Amazon EMR 6.X as  it includes new features that helps you improve performance and optimize on cost. For instance, Apache Hive is two times faster with LLAP on Amazon EMR 6.X, and Spark 3 reduces costs by 40%. Additionally, Amazon EMR 6.x releases […]

Enterprise customers are modernizing their data warehouses and data lakes to provide real-time insights, because having the right insights at the right time is crucial for good business outcomes. To enable near-real-time decision-making, data pipelines need to process real-time or near-real-time data. This data is sourced from IoT devices, change data capture (CDC) services like […]

Amazon Athena is an interactive query service that makes it easy to analyze data in Amazon Simple Storage Service (Amazon S3) using standard SQL. Athena is serverless, so there is no infrastructure to manage, and you pay only for the queries that you run. In 2019, Athena added support for federated queries to run SQL […]

Data engineers use various Python packages to meet their data processing requirements while building data pipelines with AWS Glue PySpark Jobs. Languages like Python and Scala are commonly used in data pipeline development. Developers can take advantage of their open-source packages or even customize their own to make it easier and faster to perform use […]