AWS Big Data Blog

Category: AWS Glue

Enabling self-service data publication to your data lake using AWS Glue DataBrew

Data lakes have been providing a level of flexibility to organizations unparalleled to anything before them. Having the ability to load and query data in place—and in its natural form—has led to an explosion of data lake deployments that have allowed organizations to accelerate against their data strategy faster than ever before. Most organizations have […]

Data monetization and customer experience optimization using telco data assets: Part 1

The landscape of the telecommunications industry is changing rapidly. For telecom service providers (TSPs), revenue from core voice and data services continues to shrink due to regulatory pressure and emerging OTT players that offer an attractive alternative. Despite increasing demand from customers for bandwidth, speed, and efficiency, TSPs are finding that ROI from implementing new […]

Keeping your data lake clean and compliant with Amazon Athena

With the introduction of CTAS support for Amazon Athena (see Use CTAS statements with Amazon Athena to reduce cost and improve performance), you can not only query but also create tables using Athena with the associated data objects stored in Amazon Simple Storage Service (Amazon S3). These tables are often temporary in nature and used […]

Optimizing Spark applications with workload partitioning in AWS Glue

AWS Glue provides a serverless environment to prepare (extract and transform) and load large amounts of datasets from a variety of sources for analytics and data processing with Apache Spark ETL jobs. This posts discusses a new AWS Glue Spark runtime optimization that helps developers of Apache Spark applications and ETL jobs, big data architects, […]

Data preprocessing for machine learning on Amazon EMR made easy with AWS Glue DataBrew

The machine learning (ML) lifecycle consists of several key phases: data collection, data preparation, feature engineering, model training, model evaluation, and model deployment. The data preparation and feature engineering phases ensure an ML model is given high-quality data that is relevant to the model’s purpose. Because most raw datasets require multiple cleaning steps (such as […]

Managing COVID-19 exposure with crowd tracing

This is a guest blog post by AWS partner Aspire Ventures As we enter winter, with fewer options to be outdoors, our personal choices can impact our risk of contracting the COVID-19 virus even more. The New England Journal of Medicine publication showed real-world examples of the effectiveness of masks and social distancing in mitigating […]

Building Python modules from a wheel for Spark ETL workloads using AWS Glue 2.0

AWS Glue is a fully managed extract, transform, and load (ETL) service that makes it easy to prepare and load your data for analytics. AWS Glue 2.0 features an upgraded infrastructure for running Apache Spark ETL jobs in AWS Glue with reduced startup times. With reduced startup delay time and lower minimum billing duration, overall […]

Creating a source to Lakehouse data replication pipe using Apache Hudi, AWS Glue, AWS DMS, and Amazon Redshift

February 2021 update – Please refer to the post Writing to Apache Hudi tables using AWS Glue Custom Connector to learn about an easier mechanism to write to Hudi tables using AWS Glue Custom Connector. In this post, we include the modified Apache Hudi JARs as an external dependency. The AWS Glue Custom Connector feature […]

Handling data erasure requests in your data lake with Amazon S3 Find and Forget

Data lakes are a popular choice for organizations to store data around their business activities. Best practice design of data lakes impose that data is immutable once stored, but new regulations such as the European General Data Protection Regulation (GDPR), California Consumer Privacy Act (CCPA), and others have created new obligations that operators now need […]

AWS serverless data analytics pipeline reference architecture

May 2022: This post was reviewed and updated to include additional resources for predictive analysis section. 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 […]