AWS Big Data Blog

Category: AWS Glue

How Pagely implemented a serverless data lake in AWS to facilitate customer support analytics

In this post, we discuss how Pagely worked with Beyondsoft, an AWS Advanced Consulting Partner, to use ConvergDB, an open-source tool developed by Beyondsoft, to build a DevOps-centric data pipeline. This pipeline uses AWS Glue to transform application logs into optimized tables that can be queried quickly and cost effectively using Amazon Athena.

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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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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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How to retain system tables’ data spanning multiple Amazon Redshift clusters and run cross-cluster diagnostic queries

In this blog post, I present a solution that exports system tables from multiple Amazon Redshift clusters into an Amazon S3 bucket. This solution is serverless, and you can schedule it as frequently as every five minutes. The AWS CloudFormation deployment template that I provide automates the solution setup in your environment. The system tables’ data in the Amazon S3 bucket is partitioned by cluster name and query execution date to enable efficient joins in cross-cluster diagnostic queries.

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