AWS Big Data Blog

Category: Analytics

Analyze Amazon Connect records with Amazon Athena, AWS Glue, and Amazon QuickSight

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.

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Analyze Apache Parquet optimized data using Amazon Kinesis Data Firehose, Amazon Athena, and Amazon Redshift

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.

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Analyze data in Amazon DynamoDB using Amazon SageMaker for real-time prediction

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.

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Implement continuous integration and delivery of serverless AWS Glue ETL applications using AWS Developer Tools

In this post, I walk you through a solution that implements a CI/CD pipeline for serverless AWS Glue ETL applications supported by AWS Developer Tools (including AWS CodePipeline, AWS CodeCommit, and AWS CodeBuild) and AWS CloudFormation.

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Work with partitioned data in AWS Glue

In this post, we show you how to efficiently process partitioned datasets using AWS Glue. First, we cover how to set up a crawler to automatically scan your partitioned dataset and create a table and partitions in the AWS Glue Data Catalog. Then, we introduce some features of the AWS Glue ETL library for working with partitioned data.

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