AWS Big Data Blog

Query your data created on-premises using Amazon Athena and AWS Storage Gateway

In this blog post, I use this architecture to demonstrate the combined capabilities of Storage Gateway and Athena. AWS Storage Gateway is a hybrid storage service that enables your on-premises applications to seamlessly use AWS cloud storage. The File Gateway configuration of the AWS Storage Gateway offers you a seamless way to connect to the cloud in order to store application data files and backup images as durable objects on Amazon S3 cloud storage.

Separate queries and managing costs using Amazon Athena workgroups

Amazon Athena is a serverless query engine for data on Amazon S3. Many customers use Athena to query application and service logs, schedule automated reports, and integrate with their applications, enabling new analytics-based capabilities. Different types of users rely on Athena, including business analysts, data scientists, security, and operations engineers. In this post, I show you how to use workgroups to separate workloads, control user access, and manage query usage and costs.

Orchestrate an ETL process using AWS Step Functions for Amazon Redshift

Modern data lakes depend on extract, transform, and load (ETL) operations to convert bulk information into usable data. This post walks through implementing an ETL orchestration process that is loosely coupled using AWS Step Functions, AWS Lambda, and AWS Batch to target an Amazon Redshift cluster.

Set alerts in Amazon Elasticsearch Service

On April 8, Amazon ES launched support for event monitoring and alerting. To use this feature, you work with monitors—scheduled jobs—that have triggers, which are specific conditions that you set, telling the monitor when it should send an alert. An alert is a notification that the triggering condition occurred. When a trigger fires, the monitor takes action, sending a message to your destination.

This post uses a simulated IoT device farm to generate and send data to Amazon ES.

Modify your cluster on the fly with Amazon EMR reconfiguration

If you are a developer or data scientist using long-running Amazon EMR clusters, you face fast-changing workloads. These changes often require different application configurations to run optimally on your cluster. With the reconfiguration feature, you can now change configurations on running EMR clusters. Starting with EMR release emr-5.21.0, this feature allows you to modify configurations […]

Load ongoing data lake changes with AWS DMS and AWS Glue

July 2022: This blog post was reviewed and updated with an additional AWS CloudFormation stack to deploy MySQL database. Building a data lake on Amazon S3 provides an organization with countless benefits. It allows you to access diverse data sources, determine unique relationships, build AI/ML models to provide customized customer experiences, and accelerate the curation […]

Detect fraudulent calls using Amazon QuickSight ML insights

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 […]

Performance updates to Apache Spark in Amazon EMR 5.24 – Up to 13x better performance compared to Amazon EMR 5.16

Amazon EMR release 5.24.0 includes several optimizations in Spark that improve query performance. To evaluate the performance improvements, we used TPC-DS benchmark queries with 3-TB scale and ran them on a 6-node c4.8xlarge EMR cluster with data in Amazon S3. We observed up to 13X better query performance on EMR 5.24 compared to EMR 5.16 when operating with a similar configuration.