Category: Analytics

Welcome to this introductory series on Amazon OpenSearch Service. In this and future blog posts, we provide the basic information that you need to get started with Amazon OpenSearch Service. Introduction When you’re spinning up your first Amazon OpenSearch Service domain, you need to configure the instance types and count, decide whether to use dedicated […]

Many enterprises have highly regulated policies around cloud security. Those policies might be even more restrictive for Amazon EMR where sensitive data is processed. EMR provides security configurations that allow you to set up encryption for data at rest stored on Amazon S3 and local Amazon EBS volumes. It also allows the setup of Transport […]

In this post, I detail how EMR clusters resize, and I present some best practices for getting the maximum benefit and resulting cost savings for your own cluster through this feature.

In this post, I show you how to use AWS Step Functions and AWS Lambda for orchestrating multiple ETL jobs involving a diverse set of technologies in an arbitrarily-complex ETL workflow.

In this post, we’ll show you how to deploy an Ethereum blockchain using the AWS Blockchain Templates, deploy a smart contract, and build a serverless analytics pipeline for that contract based around AWS Lambda, Amazon Kinesis, and Amazon Athena.

In this blog post, we focus on how to get analytics out of the rich set of data published by Amazon Connect. We make use of an Amazon Connect data stream and create an end-to-end workflow to offer an analytical solution that can be customized based on need.

In this post, I’ll show you how to use AWS Step Functions to orchestrate your Spark jobs that are running on Amazon EMR.

Kinesis Data Firehose can now save data to Amazon S3 in Apache Parquet or Apache ORC format. These are optimized columnar formats that are highly recommended for best performance and cost-savings when querying data in S3. This feature directly benefits you if you use Amazon Athena, Amazon Redshift, AWS Glue, Amazon EMR, or any other big data tools that are available from the AWS Partner Network and through the open-source community.

In this post, we demonstrate how to connect to data sources that are not natively supported in AWS Glue today. We walk through connecting to and running ETL jobs against two such data sources, IBM DB2 and SAP Sybase.

I’ll describe how to read the DynamoDB backup file format in Data Pipeline, how to convert the objects in S3 to a CSV format that Amazon ML can read, and I’ll show you how to schedule regular exports and transformations using Data Pipeline.