Author: Noritaka Sekiyama

After careful consideration, we have made the decision to close new customer access to AWS Data Pipeline, effective July 25, 2024. AWS Data Pipeline existing customers can continue to use the service as normal. AWS continues to invest in security, availability, and performance improvements for AWS Data Pipeline, but we do not plan to introduce […]

AWS Glue is a serverless data integration service that enables you to run extract, transform, and load (ETL) workloads on your data in a scalable and serverless manner. One of the main advantages of using a cloud platform is its flexibility; you can provision compute resources when you actually need them. However, with this ease […]

Today, we’re excited to announce general availability of Amazon Q data integration in AWS Glue. Amazon Q data integration, a new generative AI-powered capability of Amazon Q Developer, enables you to build data integration pipelines using natural language. This reduces the time and effort you need to learn, build, and run data integration jobs using […]

In Part 2 of this series, we discussed how to enable AWS Glue job observability metrics and integrate them with Grafana for real-time monitoring. Grafana provides powerful customizable dashboards to view pipeline health. However, to analyze trends over time, aggregate from different dimensions, and share insights across the organization, a purpose-built business intelligence (BI) tool […]

Monitoring data pipelines in real time is critical for catching issues early and minimizing disruptions. AWS Glue has made this more straightforward with the launch of AWS Glue job observability metrics, which provide valuable insights into your data integration pipelines built on AWS Glue. However, you might need to track key performance indicators across multiple […]

AWS Glue is a serverless data integration service that makes it easier 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 […]

dbt is an open source, SQL-first templating engine that allows you to write repeatable and extensible data transforms in Python and SQL. dbt focuses on the transform layer of extract, load, transform (ELT) or extract, transform, load (ETL) processes across data warehouses and databases through specific engine adapters to achieve extract and load functionality. It […]

Apache Hudi is an open table format that brings database and data warehouse capabilities to data lakes. Apache Hudi helps data engineers manage complex challenges, such as managing continuously evolving datasets with transactions while maintaining query performance. Data engineers use Apache Hudi for streaming workloads as well as to create efficient incremental data pipelines. Hudi provides tables, transactions, efficient […]

For any modern data-driven company, having smooth data integration pipelines is crucial. These pipelines pull data from various sources, transform it, and load it into destination systems for analytics and reporting. When running properly, it provides timely and trustworthy information. However, without vigilance, the varying data volumes, characteristics, and application behavior can cause data pipelines […]

Today, we are pleased to announce serverless Spark UI built into the AWS Glue console. You can now use Spark UI easily as it’s a built-in component of the AWS Glue console, enabling you to access it with a single click when examining the details of any given job run. There’s no infrastructure setup or teardown required. AWS Glue serverless Spark UI is a fully-managed serverless offering and generally starts up in a matter of seconds. Serverless Spark UI makes it significantly faster and easier to get jobs working in production because you have ready access to low level details for your job runs.