AWS Big Data Blog

Tag: AWS Glue

Build, Test and Deploy ETL solutions using AWS Glue and AWS CDK based CI/CD pipelines

AWS Glue is a serverless data integration service that makes it easy to discover, prepare, and combine data for analytics, machine learning (ML), and application development. It’s serverless, so there’s no infrastructure to set up or manage. This post provides a step-by-step guide to build a continuous integration and continuous delivery (CI/CD) pipeline using AWS […]

EMR Hive Metastore Upgrade

Upgrade Amazon EMR Hive Metastore from 5.X to 6.X

If you are currently running Amazon EMR 5.X clusters, consider moving to Amazon EMR 6.X as  it includes new features that helps you improve performance and optimize on cost. For instance, Apache Hive is two times faster with LLAP on Amazon EMR 6.X, and Spark 3 reduces costs by 40%. Additionally, Amazon EMR 6.x releases […]

Interactively develop your AWS Glue streaming ETL jobs using AWS Glue Studio notebooks

Enterprise customers are modernizing their data warehouses and data lakes to provide real-time insights, because having the right insights at the right time is crucial for good business outcomes. To enable near-real-time decision-making, data pipelines need to process real-time or near-real-time data. This data is sourced from IoT devices, change data capture (CDC) services like […]

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 […]

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 […]

How MEDHOST’s cardiac risk prediction successfully leveraged AWS analytic services

MEDHOST has been providing products and services to healthcare facilities of all types and sizes for over 35 years. Today, more than 1,000 healthcare facilities are partnering with MEDHOST and enhancing their patient care and operational excellence with its integrated clinical and financial EHR solutions. MEDHOST also offers a comprehensive Emergency Department Information System with […]

How Aruba Networks built a cost analysis solution using AWS Glue, Amazon Redshift, and Amazon QuickSight

This is a guest post co-written by Siddharth Thacker and Swatishree Sahu from Aruba Networks. Aruba Networks is a Silicon Valley company based in Santa Clara that was founded in 2002 by Keerti Melkote and Pankaj Manglik. Aruba is the industry leader in wired, wireless, and network security solutions. Hewlett-Packard acquired Aruba in 2015, making […]

Optimize Python ETL by extending Pandas with AWS Data Wrangler

Developing extract, transform, and load (ETL) data pipelines is one of the most time-consuming steps to keep data lakes, data warehouses, and databases up to date and ready to provide business insights. You can categorize these pipelines into distributed and non-distributed, and the choice of one or the other depends on the amount of data […]

Stream Twitter data into Amazon Redshift using Amazon MSK and AWS Glue streaming ETL

This post demonstrates how customers, system integrator (SI) partners, and developers can use the serverless streaming ETL capabilities of AWS Glue with Amazon Managed Streaming for Kafka (Amazon MSK) to stream data to a data warehouse such as Amazon Redshift. We also show you how to view Twitter streaming data on Amazon QuickSight via Amazon Redshift.

How Wind Mobility built a serverless data architecture

We parse through millions of scooter and user events generated daily (over 300 events per second) to extract actionable insight. We selected AWS Glue to perform this task. Our primary ETL job reads the newly added raw event data from Amazon S3, processes it using Apache Spark, and writes the results to our Amazon Redshift data warehouse. AWS Glue plays a critical role in our ability to scale on demand. After careful evaluation and testing, we concluded that AWS Glue ETL jobs meet all our needs and free us from procuring and managing infrastructure.