Tag: AWS Glue

In Amazon Simple Storage Service (Amazon S3), you can use cross-region replication (CRR) to copy objects automatically and asynchronously across buckets in different AWS Regions. CRR is a bucket-level configuration, and it can help you meet compliance requirements and minimize latency by keeping copies of your data in different Regions. CRR replicates all objects in […]

September 2022: This post was reviewed and updated with latest screenshots and instructions. Today, data is flowing from everywhere, whether it is unstructured data from resources like IoT sensors, application logs, and clickstreams, or structured data from transaction applications, relational databases, and spreadsheets. Data has become a crucial part of every business. This has resulted […]

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. Clickstream events are small pieces of data that are generated continuously with high speed and volume. Often, clickstream events are generated by user actions, and it is […]

February 9, 2024: Amazon Kinesis Data Firehose has been renamed to Amazon Data Firehose. Read the AWS What’s New post to learn more. In this post, we talk about designing a cloud-native data warehouse as a replacement for our legacy data warehouse built on a relational database. At the beginning of the design process, the […]

In this blog post, we describe how to configure the networking routes and interfaces to give AWS Glue access to a data store in an AWS Region different from the one with your AWS Glue resources. In our example, we connect AWS Glue, located in Region A, to an Amazon Redshift data warehouse located in Region B.

In this post, we will explore how the AWS Glue Data Catalog addresses discoverability and manageability for table metadata for Presto on Amazon EMR.

We are excited to announce AWS Glue support for running ETL (extract, transform, and load) scripts in Scala. Scala lovers can rejoice because they now have one more powerful tool in their arsenal.

AWS Glue has a transform called Relationalize that simplifies the extract, transform, load (ETL) process by converting nested JSON into columns that you can easily import into relational databases. Relationalize transforms the nested JSON into key-value pairs at the outermost level of the JSON document. The transformed data maintains a list of the original keys from the nested JSON separated by periods. Let’s look at how Relationalize can help you with a sample use case.

This is a guest post by Rafi Ton, founder and CEO of NUVIAD. The ability to provide fresh, up-to-the-minute data to our customers and partners was always a main goal with our platform. We saw other solutions provide data that was a few hours old, but this was not good enough for us. We insisted on providing the freshest data possible. For us, that meant loading Amazon Redshift in frequent micro batches and allowing our customers to query Amazon Redshift directly to get results in near real time. The benefits were immediately evident. Our customers could see how their campaigns performed faster than with other solutions, and react sooner to the ever-changing media supply pricing and availability. They were very happy.

In this post, I walk through using AWS Glue and AWS Lambda to convert AWS CloudTrail logs from JSON to a query-optimized format dataset in Amazon S3. I then use Amazon Athena and Amazon QuickSight to query and visualize the data.