Category: AWS Glue

August 2024: This post was reviewed and updated with examples against a new dataset. Additionally, changed the architecture to use AWS Glue Studio Notebooks and added information on the appropriate Deequ/PyDeequ versions. In our previous post, we introduced PyDeequ, an open-source Python wrapper over Deequ, which enables you to write unit tests on your data […]

September 8, 2021: Amazon Elasticsearch Service has been renamed to Amazon OpenSearch Service. See details. Recently, we launched AWS Glue custom connectors for Amazon OpenSearch Service, which provides the capability to ingest data into Amazon OpenSearch Service with just a few clicks. You can now use Amazon OpenSearch Service as a data store for your […]

This is a guest post by Christa Pierson and Jon Fearer at OrthoFi. OrthoFi is an orthodontic industry leader in revenue cycle management (RCM), and has partnered with more than 550 orthodontic practices across the country, delivering an end-to-end platform that enables orthodontists to bring on more patients and run their businesses more effectively. To […]

As the industry grows with more data volume, big data analytics is becoming a common requirement in data analytics and machine learning (ML) use cases. Analysts are building complex data transformation pipelines that include multiple steps for data preparation and cleansing. However, analysts may want a simpler orchestration mechanism with a graphical user interface that […]

AWS provides many ways to optimize your workloads and save on costs. For example, services like AWS Cost Explorer and AWS Trusted Advisor provide cost savings recommendations to help you optimize your AWS environments. However, you may also want to estimate cost savings when comparing Amazon Elastic Compute Cloud (Amazon EC2) Spot to On-Demand Instances. […]

AWS Glue is fully managed service that makes it easier for you to extract, transform, and load (ETL) data for analytics. You can easily create ETL jobs to connect to backend data sources. There are several natively supported data sources, but what if you need to extract data from an unsupported data source? What if […]

This blog post was last updated July, 2022 to update the new version of the connector and details on how to push down queries to Google BigQuery. The cloud is often seen as advantageous for data lakes because of better security, faster time to deployment, better availability, more frequent feature and functionality updates, more elasticity, […]

Today, with AWS Glue DataBrew, data analysts and data scientists can easily access and visually explore any amount of data across their organization directly from their Amazon Simple Storage Service (Amazon S3) data lake, Amazon Redshift data warehouse, and Amazon Aurora and Amazon Relational Database Service (Amazon RDS) databases. Customers can choose from over 250 […]

A common challenge ETL and big data developers face is working with data files that don’t have proper name header records. They’re tasked with renaming the columns of the data files appropriately so that downstream application and mappings for data load can work seamlessly. One example use case is while working with ORC files and […]

In Part 1 of this two-part post, we looked at how we can create an AWS Glue ETL job that is agnostic enough to rename columns of a data file by mapping to column names of another file. The solution focused on using a single file that was populated in the AWS Glue Data Catalog […]