Category: AWS Glue

AWS Glue is a serverless data integration service that makes it easy to discover, prepare, and combine data for analytics, machine learning, and application development. AWS Glue already integrates with various popular data stores such as the Amazon Redshift, RDS, MongoDB, and Amazon S3. Organizations continue to evolve and use a variety of data stores that best fit […]

July, 2022: This post was reviewed and updated to include a mew data point on the effective runtime with the latest version, explaining Glue 3,0 and autoscaling. October, 2024: In Glue 4.0 we have introduced a native and managed connector for Google BigQuery. You can follow the instruction in the blog postUnlock scalable analytics with […]

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 has native connectors to connect to supported data sources on AWS or elsewhere using JDBC drivers. Additionally, AWS Glue now supports reading and writing to Amazon DocumentDB (with MongoDB […]

December 2022: This post was reviewed for accuracy. In today’s world, most organizations have to tackle the 3 V’s of variety, volume and velocity of big data. In this blog post, we talk about dealing with the variety and volume aspects of big data. The challenge of dealing with the variety involves processing data from […]

Disparate data systems have become a norm in many companies. The reasons for this vary: different teams in the organization select data system best suited for its primary function, the responsibility for choosing these data systems may have been decentralized across different departments, a merged company may still use separate data systems from the formerly […]

August 30, 2023: Amazon Kinesis Data Analytics has been renamed to Amazon Managed Service for Apache Flink. Read the announcement in the AWS News Blog and learn more. Data streaming technologies like Apache Kafka and Amazon Kinesis Data Streams capture and distribute data generated by thousands or millions of applications, websites, or machines. These technologies […]

Amazon Managed Workflows for Apache Airflow (Amazon MWAA) is a fully managed service that makes it easy to run open-source versions of Apache Airflow on AWS and build workflows to run your extract, transform, and load (ETL) jobs and data pipelines. You can use AWS Step Functions as a serverless function orchestrator to build scalable […]

In soccer (or football outside of the US), players decide to take shots when they think they can score. But how do they make that determination vs. when to pass or dribble? In a fraction of a second, in motion, while chased from multiple directions by other professional athletes, they think about their distance from […]

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. Also, as we start building complex data engineering or data analytics pipelines, we look for a simpler orchestration mechanism with graphical user interface-based ETL (extract, transform, load) tools. Recently, AWS […]

June 2024: This post was reviewed and updated to add instructions for using PyDeequ with Amazon SageMaker Notebook, SageMaker Studio, EMR, and updated the examples against a new dataset. March 2023: You can now use AWS Glue Data Quality to measure and manage the quality of your data. AWS Glue Data Quality is built on Deequ […]