AWS Big Data Blog

Category: AWS Glue

How Fujitsu implemented a global data mesh architecture and democratized data

This is a guest post co-authored with Kanehito Miyake, Engineer at Fujitsu Japan.  Fujitsu Limited was established in Japan in 1935. Currently, we have approximately 120,000 employees worldwide (as of March 2023), including group companies. We develop business in various regions around the world, starting with Japan, and provide digital services globally. To provide a […]

Introducing Amazon Q data integration in AWS Glue

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 […]

Orchestrate an end-to-end ETL pipeline using Amazon S3, AWS Glue, and Amazon Redshift Serverless with Amazon MWAA

Amazon Managed Workflows for Apache Airflow (Amazon MWAA) is a managed orchestration service for Apache Airflow that you can use to set up and operate data pipelines in the cloud at scale. Apache Airflow is an open source tool used to programmatically author, schedule, and monitor sequences of processes and tasks, referred to as workflows. […]

Amazon DataZone announces integration with AWS Lake Formation hybrid access mode for the AWS Glue Data Catalog

Last week, we announced the general availability of the integration between Amazon DataZone and AWS Lake Formation hybrid access mode. In this post, we share how this new feature helps you simplify the way you use Amazon DataZone to enable secure and governed sharing of your data in the AWS Glue Data Catalog. We also […]

Amazon DataZone now integrates with AWS Glue Data Quality and external data quality solutions

Today, we are pleased to announce that Amazon DataZone is now able to present data quality information for data assets. This information empowers end-users to make informed decisions as to whether or not to use specific assets. In this post, we discuss the latest features of Amazon DataZone for data quality, the integration between Amazon DataZone and AWS Glue Data Quality and how you can import data quality scores produced by external systems into Amazon DataZone via API.

Use Apache Iceberg in your data lake with Amazon S3, AWS Glue, and Snowflake

Customers are using AWS and Snowflake to develop purpose-built data architectures that provide the performance required for modern analytics and artificial intelligence (AI) use cases. Implementing these solutions requires data sharing between purpose-built data stores. This is why Snowflake and AWS are delivering enhanced support for Apache Iceberg to enable and facilitate data interoperability between data services. Apache Iceberg is an open-source table format that provides reliability, simplicity, and high performance for large datasets with transactional integrity between various processing engines.

Enhance monitoring and debugging for AWS Glue jobs using new job observability metrics, Part 3: Visualization and trend analysis using Amazon QuickSight

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 […]

High level architecture

Scale AWS Glue jobs by optimizing IP address consumption and expanding network capacity using a private NAT gateway

As businesses expand, the demand for IP addresses within the corporate network often exceeds the supply. An organization’s network is often designed with some anticipation of future requirements, but as enterprises evolve, their information technology (IT) needs surpass the previously designed network. Companies may find themselves challenged to manage the limited pool of IP addresses. […]

Architecture diagram

Gain insights from historical location data using Amazon Location Service and AWS analytics services

Many organizations around the world rely on the use of physical assets, such as vehicles, to deliver a service to their end-customers. By tracking these assets in real time and storing the results, asset owners can derive valuable insights on how their assets are being used to continuously deliver business improvements and plan for future […]