AWS Big Data Blog
Category: AWS Glue
Integrating Datadog data with AWS using Amazon AppFlow for intelligent monitoring
Infrastructure and operation teams are often challenged with getting a full view into their IT environments to do monitoring and troubleshooting. New monitoring technologies are needed to provide an integrated view of all components of an IT infrastructure and application system. Datadog provides intelligent application and service monitoring by bringing together data from servers, databases, […]
Performing data transformations using Snowflake and AWS Glue
May 2022: This post was reviewed for accuracy. In the connected world, data is getting generated from many different sources in a wide variety of data formats. Enterprises are looking for tools to ingest from these evolving data sources as well as programmatically customize the ingested data to meet their data analytics needs. You also need […]
Building AWS Glue Spark ETL jobs by bringing your own JDBC drivers for Amazon RDS
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 either on AWS or elsewhere using JDBC drivers. Additionally, AWS Glue now enables you to bring your own JDBC drivers […]
Developing, testing, and deploying custom connectors for your data stores with 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 […]
Migrating data from Google BigQuery to Amazon S3 using AWS Glue custom connectors
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. In today’s connected world, it’s common to have data sitting in various data sources in a variety of formats. Even though data is a critical component of decision […]
Building AWS Glue Spark ETL jobs using Amazon DocumentDB (with MongoDB compatibility) and MongoDB
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 […]
Writing to Apache Hudi tables using AWS Glue Custom Connector
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 […]
Building fast ETL using SingleStore and AWS Glue
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 […]
Validate, evolve, and control schemas in Amazon MSK and Amazon Kinesis Data Streams with AWS Glue Schema Registry
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 serve as a highly available transport layer that decouples the data-producing applications from data processors. However, the sheer number of applications producing, processing, routing, and consuming data can […]
Building complex workflows with Amazon MWAA, AWS Step Functions, AWS Glue, and Amazon EMR
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 […]