Category: AWS Glue

In this post, I show you how to use AWS Glue’s DynamoDB integration and AWS Step Functions to create a workflow to export your DynamoDB tables to S3 in Parquet. I also show how to create an Athena view for each table’s latest snapshot, giving you a consistent view of your DynamoDB table exports.

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 […]

April 2024: The content of this post is no longer relevant and deprecated. 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 […]

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 blog post, we’ll go through the steps needed to build an ETL pipeline that consumes from one source in one VPC and outputs it to another source in a different VPC. We’ll set up in multiple VPCs to reproduce a situation where your database instances are in multiple VPCs for isolation related to security, audit, or other purposes.

As organizations expand analytics practices and hire data scientists and other specialized roles, big data pipelines are growing increasingly complex. Sophisticated models are being built using the troves of data being collected every second. The bottleneck today is often not the know-how of analytical techniques. Rather, it’s the difficulty of building and maintaining ETL (extract, transform, and load) jobs using tools that might be unsuitable for the cloud. In this post, I demonstrate a solution to this challenge.

Data cataloging is an important part of many analytical systems. The AWS Glue Data Catalog provides integration with a wide number of tools. Using the Data Catalog, you also can specify a policy that grants permissions to objects in the Data Catalog. Data lakes require detailed access control at both the content level and the level of the metadata describing the content. In this post, we show how you can define the access policies for the metadata in the catalog.

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 part 1 of this series, we demonstrated how to build a data pipeline in support of a data lake. We used key AWS services such as Amazon Kinesis Data Streams, Kinesis […]