AWS Big Data Blog

Category: AWS Big Data

Building a cost efficient, petabyte-scale lake house with Amazon S3 lifecycle rules and Amazon Redshift Spectrum: Part 2

In part 1 of this series, we demonstrated building an end-to-end data lifecycle management system integrated with a data lake house implemented on Amazon Simple Storage Service (Amazon S3) with Amazon Redshift and Amazon Redshift Spectrum. In this post, we address the ongoing operation of the solution we built. Data ageing process after a month […]

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Centrally tracking dashboard lineage, permissions, and more with Amazon QuickSight administrative dashboards

This post is co-written with Shawn Koupal, an Enterprise Analytics IT Architect at Best Western International, Inc. A common ask from Amazon QuickSight administrators is to understand the lineage of a given dashboard (what analysis is it built from, what datasets are used in the analysis, and what data sources do those datasets use). QuickSight […]

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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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Performing data transformations using Snowflake and AWS Glue

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 warehousing needs. You also need solutions that help you quickly meet your […]

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In the third scenario, we set up a connection where we connect to Oracle 18 and MySQL 8 using external drivers from AWS Glue ETL, extract the data, transform it, and load the transformed data to Oracle 18.

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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The following architecture diagram shows SingleStore connecting with AWS Glue for an ETL job.

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 […]

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Migrating data from Google BigQuery to Amazon S3 using AWS Glue custom connectors

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 making, for many organizations this data is spread across multiple public clouds. Organizations are looking for tools that make it easy to ingest data from these myriad data […]

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For Configure route tables, select the route table ID of the associated subnet of the database.

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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The following image shows how Amazon Redshift integrates with the data lake and other services.

Amazon Redshift 2020 year in review

Today, more data is created every hour than in an entire year just 20 years ago. Successful organizations are leveraging this data to deliver better service to their customers, improve their products, and run an efficient and effective business. As the importance of data and analytics continues to grow, the Amazon Redshift cloud data warehouse […]

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