AWS Big Data Blog

Category: AWS Glue

How 1Strategy simplified their spreadsheet ETL process using AWS Glue DataBrew

This is a guest blog post by Pat Reilly and Gary Houk at 1Strategy. In their own words, “1Strategy is an APN Premier Consulting Partner focusing exclusively on AWS solutions. 1Strategy consultants help businesses architect, migrate, and optimize their workloads on AWS, creating scalable, cost-effective, secure, and reliable solutions. 1Strategy holds the AWS DevOps, Migration, […]

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Ingest Salesforce data into Amazon S3 using the CData JDBC custom connector with AWS Glue

Organizations that successfully generate business value from their data will outperform their peers. Many AWS customers require a data storage and analytics solution that combines the prospect information stored in Salesforce, a popular and widely used customer relationship management (CRM) platform, with other structured and unstructured data in their data lake to innovate and build […]

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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 […]

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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 […]

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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 […]

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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 […]

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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 […]

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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 […]

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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 […]