AWS Big Data Blog

Category: AWS Glue

How William Hill migrated NoSQL workloads at scale to Amazon Keyspaces

Social gaming and online sports betting are competitive environments. The game must be able to handle large volumes of unpredictable traffic while simultaneously promising zero downtime. In this domain, user retention is no longer just desirable, it’s critical. William Hill is a global online gambling company based in London, England, and it is the founding […]

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Migrate from Snowflake to Amazon Redshift using AWS Glue Python shell

As the most widely used cloud data warehouse, Amazon Redshift makes it simple and cost-effective to analyze your data using standard SQL and your existing ETL (extract, transform, and load), business intelligence (BI), and reporting tools. Tens of thousands of customers use Amazon Redshift to analyze exabytes of data per day and power analytics workloads […]

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Accelerate Amazon DynamoDB data access in AWS Glue jobs using the new AWS Glue DynamoDB Export connector

Modern data architectures encourage the integration of data lakes, data warehouses, and purpose-built data stores, enabling unified governance and easy data movement. With a modern data architecture on AWS, you can store data in a data lake and use a ring of purpose-built data services around the lake, allowing you to make decisions with speed […]

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Use the AWS Glue connector to read and write Apache Iceberg tables with ACID transactions and perform time travel

Nowadays, many customers have built their data lakes as the core of their data analytic systems. In a typical use case of data lakes, many concurrent queries run to retrieve consistent snapshots of business insights by aggregating query results. A large volume of data constantly comes from different data sources into the data lakes. There […]

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Build an Apache Iceberg data lake using Amazon Athena, Amazon EMR, and AWS Glue

Most businesses store their critical data in a data lake, where you can bring data from various sources to a centralized storage. The data is processed by specialized big data compute engines, such as Amazon Athena for interactive queries, Amazon EMR for Apache Spark applications, Amazon SageMaker for machine learning, and Amazon QuickSight for data […]

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Implement a CDC-based UPSERT in a data lake using Apache Iceberg and AWS Glue

As the implementation of data lakes and modern data architecture increases, customers’ expectations around its features also increase, which include ACID transaction, UPSERT, time travel, schema evolution, auto compaction, and many more. By default, Amazon Simple Storage Service (Amazon S3) objects are immutable, which means you can’t update records in your data lake because it […]

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How GE Proficy Manufacturing Data Cloud replatformed to improve TCO, data SLA, and performance

This is post is co-authored by Jyothin Madari, Madhusudhan Muppagowni and Ayush Srivastava from GE. GE Proficy Manufacturing Data Cloud (MDC), part of the GE Digital’s Manufacturing Execution Systems (MES) suite of solutions, allows GED’s customers to increase the derived value easily and quickly from the MES by reliably bringing enterprise-wide manufacturing data into the […]

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Optimize Federated Query Performance using EXPLAIN and EXPLAIN ANALYZE in Amazon Athena

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 […]

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Solution Architecture for the blog post

Simplify and optimize Python package management for AWS Glue PySpark jobs with AWS CodeArtifact

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 […]

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A serverless operational data lake for retail with AWS Glue, Amazon Kinesis Data Streams, Amazon DynamoDB, and Amazon QuickSight

Do you want to reduce stockouts at stores? Do you want to improve order delivery timelines? Do you want to provide your customers with accurate product availability, down to the millisecond? A retail operational data lake can help you transform the customer experience by providing deeper insights into a variety of operational aspects of your […]

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