Author: Leonardo Gomez

We are excited to announce the preview of API-driven, OpenLineage-compatible data lineage in Amazon DataZone to help you capture, store, and visualize lineage of data movement and transformations of data assets on Amazon DataZone. With the Amazon DataZone OpenLineage-compatible API, domain administrators and data producers can capture and store lineage events beyond what is available […]

In this post, we show you how to use the new views feature the AWS Glue Data Catalog. SQL views are a powerful object used across relational databases. You can use views to decrease the time to insights of data by tailoring the data that is queried. Additionally, you can use the power of SQL […]

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 […]

For data lake customers who need to discover petabytes of data, AWS Glue crawlers are a popular way to scan data in the background, so you can focus on using the data to make better intelligent decisions. You may also have data in data warehouses such as Snowflake and want the ability to discover the […]

AWS Glue includes crawlers, a capability that make discovering datasets simpler by scanning data in Amazon Simple Storage Service (Amazon S3) and relational databases, extracting their schema, and automatically populating the AWS Glue Data Catalog, which keeps the metadata current. This reduces the time to insight by making newly ingested data quickly available for analysis […]

AWS Glue now offers integration with Git, an open-source version control system widely used across the developer community. Thanks to this integration, you can incorporate your existing DevOps practices on AWS Glue jobs. AWS Glue is a serverless data integration service that helps you create jobs based on Apache Spark or Python to perform extract, […]

Data lakes offer the possibility of sharing diverse types of data with different teams and roles to cover numerous use cases. This is very important in order to implement a data democratization strategy and incentivize the collaboration between lines of business. When a data lake is being designed, one of the most important aspects to […]

A data lake is a centralized, curated, and secured repository that stores all your data, both in its original form and prepared for analysis. Setting up and managing data lakes today involves a lot of manual, complicated, and time-consuming tasks. AWS Glue and AWS Lake Formation make it easy to build, secure, and manage data […]

AWS Glue Studio is an easy-to-use graphical interface that speeds up the process of authoring, running, and monitoring extract, transform, and load (ETL) jobs in AWS Glue. The visual interface allows those who don’t know Apache Spark to design jobs without coding experience and accelerates the process for those who do. AWS Glue Studio was […]