Category: AWS Glue

This is a guest blog post co-written with Addison Higley and Ramzi Yassine from Hudl. Hudl Agile Sports Technologies, Inc. is a Lincoln, Nebraska based company that provides tools for coaches and athletes to review game footage and improve individual and team play. Its initial product line served college and professional American football teams. Today, […]

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. Data comes from many different sources in structured, semi-structured, and unstructured formats. For semi-structured data, one of the most common lightweight file formats is JSON. However, due to the complex […]

Many organizations build data lakes on Amazon Simple Storage Service (Amazon S3) using a modern architecture for a scalable and cost-effective solution. Open-source storage formats like Parquet and Avro are commonly used, and data is stored in these formats as immutable files. As the data lake is expanded to additional use cases, there are still […]

AWS Glue includes crawlers, a capability that make discovering datasets simpler by scanning data in Amazon Simple Storage Service (Amazon S3) and relational databases, extracting their schema, and automatically populating the AWS Glue Data Catalog, which keeps the metadata current. This reduces the time to insight by making newly ingested data quickly available for analysis […]

AWS Glue now offers integration with Git, an open-source version control system widely used across the developer community. Thanks to this integration, you can incorporate your existing DevOps practices on AWS Glue jobs. AWS Glue is a serverless data integration service that helps you create jobs based on Apache Spark or Python to perform extract, […]

To build a data lake on AWS, a common data ingestion pattern is to use AWS Glue jobs to perform extract, transform, and load (ETL) data from relational databases to Amazon Simple Storage Service (Amazon S3). A project often involves extracting hundreds of tables from source databases to the data lake raw layer. And for […]

In today’s world with technology modernization, the need for near-real-time streaming use cases has increased exponentially. Many customers are continuously consuming data from different sources, including databases, applications, IoT devices, and sensors. Organizations may need to ingest that streaming data into data lakes built on Amazon Simple Storage Service (Amazon S3). You may also need […]

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 […]

Nowadays many customers are following a multi-cloud strategy. They might choose to use various cloud-managed services, such as Amazon Relational Database Service (Amazon RDS) for SQL Server and Azure SQL Managed Instances, to perform data analytics tasks, but still use traditional extract, transform, and load (ETL) tools to integrate and process the data. However, traditional ETL tools may […]

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 […]