AWS Big Data Blog

Category: AWS Glue

Introducing native support for Apache Hudi, Delta Lake, and Apache Iceberg on AWS Glue for Apache Spark, Part 2: AWS Glue Studio Visual Editor

In the first post of this series, we described how AWS Glue for Apache Spark works with Apache Hudi, Linux Foundation Delta Lake, and Apache Iceberg datasets tables using the native support of those data lake formats. This native support simplifies reading and writing your data for these data lake frameworks so you can more […]

How Infomedia built a serverless data pipeline with change data capture using AWS Glue and Apache Hudi

This is a guest post co-written with Gowtham Dandu from Infomedia. Infomedia Ltd (ASX:IFM) is a leading global provider of DaaS and SaaS solutions that empowers the data-driven automotive ecosystem. Infomedia’s solutions help OEMs, NSCs, dealerships and 3rd party partners manage the vehicle and customer lifecycle. They are used by over 250,000 industry professionals, across […]

Simplify data loading into Type 2 slowly changing dimensions in Amazon Redshift

Thousands of customers rely on Amazon Redshift to build data warehouses to accelerate time to insights with fast, simple, and secure analytics at scale and analyze data from terabytes to petabytes by running complex analytical queries. Organizations create data marts, which are subsets of the data warehouse and usually oriented for gaining analytical insights specific […]

Build an end-to-end change data capture with Amazon MSK Connect and AWS Glue Schema Registry

The value of data is time sensitive. Real-time processing makes data-driven decisions accurate and actionable in seconds or minutes instead of hours or days. Change data capture (CDC) refers to the process of identifying and capturing changes made to data in a database and then delivering those changes in real time to a downstream system. […]

Use Apache Iceberg in a data lake to support incremental data processing

Apache Iceberg is an open table format for very large analytic datasets, which captures metadata information on the state of datasets as they evolve and change over time. It adds tables to compute engines including Spark, Trino, PrestoDB, Flink, and Hive using a high-performance table format that works just like a SQL table. Iceberg has […]

Build a semantic search engine for tabular columns with Transformers and Amazon OpenSearch Service

Finding similar columns in a data lake has important applications in data cleaning and annotation, schema matching, data discovery, and analytics across multiple data sources. The inability to accurately find and analyze data from disparate sources represents a potential efficiency killer for everyone from data scientists, medical researchers, academics, to financial and government analysts. Conventional […]

Build a real-time GDPR-aligned Apache Iceberg data lake

Data lakes are a popular choice for today’s organizations to store their data around their business activities. As a best practice of a data lake design, data should be immutable once stored. But regulations such as the General Data Protection Regulation (GDPR) have created obligations for data operators who must be able to erase or […]

Introducing AWS Glue crawlers using AWS Lake Formation permission management

Data lakes provide a centralized repository that consolidates your data at scale and makes it available for different kinds of analytics. AWS Glue crawlers are a popular way to scan data in a data lake, classify it, extract schema information from it, and store the metadata automatically in the AWS Glue Data Catalog. AWS Lake […]

How Ruparupa gained updated insights with an Amazon S3 data lake, AWS Glue, Apache Hudi, and Amazon QuickSight

This post is co-written with Olivia Michele and Dariswan Janweri P. at Ruparupa. Ruparupa was built by PT. Omni Digitama Internusa with the vision to cultivate synergy and create a seamless digital ecosystem within Kawan Lama Group that touches and enhances the lives of many. Ruparupa is the first digital platform built by Kawan Lama […]

Automate replication of relational sources into a transactional data lake with Apache Iceberg and AWS Glue

Organizations have chosen to build data lakes on top of Amazon Simple Storage Service (Amazon S3) for many years. A data lake is the most popular choice for organizations to store all their organizational data generated by different teams, across business domains, from all different formats, and even over history. According to a study, the […]