Category: Serverless

Customers with specific needs to run big data compute jobs on an on-premises infrastructure often require a scalable orchestration solution. For large-scale distributed compute clusters, the orchestration of jobs must be scalable to maximize their utilization, while at the same time remain resilient to any failures to prevent blocking the ever-growing influx of data and […]

Data lakes, business intelligence, operational analytics, and data warehousing share a common core characteristic—the ability to extract, transform, and load (ETL) data for analytics. Since its launch in 2017, AWS Glue has provided serverless data integration service that makes it easy to discover, prepare, and combine data for analytics, machine learning, and application development. AWS […]

dbt has established itself as one of the most popular tools in the modern data stack, and is aiming to bring analytics engineering to everyone. The dbt tool makes it easy to develop and implement complex data processing pipelines, with mostly SQL, and it provides developers with a simple interface to create, test, document, evolve, […]

October 2024: This post has been updated along with Interactive Sessions support for AWS Glue Auto scaling. June 2023: This post was reviewed and updated for accuracy. Data created in the cloud is growing fast in recent days, so scalability is a key factor in distributed data processing. Many customers benefit from the scalability of […]

In today’s market, business success often lies in the ability to glean accurate insights and predictions from data. However, data scientists and analysts often find that the data they have at their disposal isn’t enough to help them make accurate predictions for their use cases. A variety of factors might alter an outcome and should […]

Mar 2025: This post was written for AWS Glue 3.0 and 4.0. For AWS Glue 5.0, visit Develop and test AWS Glue 5.0 jobs locally using a Docker container. Apr 2023: This post was reviewed and updated with enhanced support for Glue 4.0 Streaming jobs. Jan 2023: This post was reviewed and updated with enhanced […]

June 2024: This post was reviewed for accuracy and updated to cover Apache Iceberg. June 2023: This post was reviewed and updated for accuracy. Customers are increasingly building data lakes to store data at massive scale in the cloud. It’s common to use distributed computing engines, cloud-native databases, and data warehouses when you want to […]

AWS Glue Schema Registry now supports Protocol buffers (protobuf) schemas in addition to JSON and Avro schemas. This allows application teams to use protobuf schemas to govern the evolution of streaming data and centrally control data quality from data streams to data lake. AWS Glue Schema Registry provides an open-source library that includes Apache-licensed serializers […]

The AWS Well-Architected Data Analytics Lens provides a set of guiding principles for analytics applications on AWS. One of the best practices it talks about is build a central Data Catalog to store, share, and track metadata changes. AWS Glue provides a Data Catalog to fulfill this requirement. AWS Glue also provides crawlers that automatically […]

Data lineage is one of the most critical components of a data governance strategy for data lakes. Data lineage helps ensure that accurate, complete and trustworthy data is being used to drive business decisions. While a data catalog provides metadata management features and search capabilities, data lineage shows the full context of your data by […]