Tag: AWS Glue

In this post, I show how to use AWS Step Functions and AWS Glue Python Shell to orchestrate tasks for those Amazon Redshift-based ETL workflows in a completely serverless fashion. AWS Glue Python Shell is a Python runtime environment for running small to medium-sized ETL tasks, such as submitting SQL queries and waiting for a response. Step Functions lets you coordinate multiple AWS services into workflows so you can easily run and monitor a series of ETL tasks. Both AWS Glue Python Shell and Step Functions are serverless, allowing you to automatically run and scale them in response to events you define, rather than requiring you to provision, scale, and manage servers.

This post describes how to make the MIMIC-III dataset available in Athena and provide automated access to an analysis environment for MIMIC-III on AWS. We also compare a MIMIC-III reference bioinformatics study using a traditional database to that same study using Athena.

A data lake is a centralized store of a variety of data types for analysis by multiple analytics approaches and groups. Many organizations are moving their data into a data lake. In this post, we explore how you can use AWS Lake Formation to build, secure, and manage data lakes.

In this post, I show you how to use AWS Glue to extract data from a Salesforce.com account object and save it to Amazon S3. You then use Amazon Athena to generate a report by joining the account object data from Salesforce.com with the orders data from a separate order management system.

The financial impact of fraud in any industry is massive. According to the Financial Times article Fraud Costs Telecoms Industry $17bn a Year (paid subscription required), fraud costs the telecommunications industry $17 billion in lost revenues every year. Fraudsters constantly look for new technologies and devise new techniques. This changes fraud patterns and makes detection […]

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 […]