Category: AWS Glue

Onboarding new data or building new analytics pipelines in traditional analytics architectures typically requires extensive coordination across business, data engineering, and data science and analytics teams to first negotiate requirements, schema, infrastructure capacity needs, and workload management. For a large number of use cases today however, business users, data scientists, and analysts are demanding easy, […]

The AWS Marketing Data Science and Engineering team enables AWS Marketing to measure the effectiveness and impact of various marketing initiatives and campaigns. This is done through a data platform and infrastructure strategy that consists of maintaining data warehouse, data lake, and data transformation (ETL) pipelines, and designing software tools and services to run related […]

Organizations across verticals have been building streaming-based extract, transform, and load (ETL) applications to more efficiently extract meaningful insights from their datasets. Although streaming ingest and stream processing frameworks have evolved over the past few years, there is now a surge in demand for building streaming pipelines that are completely serverless. Since 2017, AWS Glue […]

Businesses are increasingly harnessing data to improve their business outcomes. To enable this transformation to a data-driven business, customers are bringing together data from structured and unstructured sources into a data lake. Then they use business intelligence (BI) tools, such as Amazon QuickSight, to unlock insights from this data. To provide fast access to datasets, […]

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 […]

If you’re new to AWS Glue and looking to understand its transformation capabilities without incurring an added expense, or if you’re simply wondering if AWS Glue ETL is the right tool for your use case and want a holistic view of AWS Glue ETL functions, then please continue reading. In this post, we walk you […]

AWS Glue is a fully managed extract, transform, and load (ETL) service that makes it easy to prepare and load your data for analytics. In the fourth post of the series, we discussed optimizing memory management. In this post, we focus on writing ETL scripts for AWS Glue jobs locally. AWS Glue is built on […]

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 […]

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 […]

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.