AWS Big Data Blog

Simplify Querying Nested JSON with the AWS Glue Relationalize Transform

AWS Glue has a transform called Relationalize that simplifies the extract, transform, load (ETL) process by converting nested JSON into columns that you can easily import into relational databases. Relationalize transforms the nested JSON into key-value pairs at the outermost level of the JSON document. The transformed data maintains a list of the original keys from the nested JSON separated by periods. Let’s look at how Relationalize can help you with a sample use case.

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Genomic Analysis with Hail on Amazon EMR and Amazon Athena

For this task, we use Hail, an open source framework for exploring and analyzing genomic data that uses the Apache Spark framework. In this post, we use Amazon EMR to run Hail. We walk through the setup, configuration, and data processing. Finally, we generate an Apache Parquet–formatted variant dataset and explore it using Amazon Athena.

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Collect Data Statistics Up to 5x Faster by Analyzing Only Predicate Columns with Amazon Redshift

After loading new data into an Amazon Redshift cluster, statistics need to be re-computed to guarantee performant query plans. By learning which column statistics are actually being used by the customer’s workload and collecting statistics only on those columns, Amazon Redshift is able to significantly reduce the amount of time needed for table maintenance during data loading workflows.

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Using Amazon Redshift Spectrum, Amazon Athena, and AWS Glue with Node.js in Production

This is a guest post by Rafi Ton, founder and CEO of NUVIAD. The ability to provide fresh, up-to-the-minute data to our customers and partners was always a main goal with our platform. We saw other solutions provide data that was a few hours old, but this was not good enough for us. We insisted on providing the freshest data possible. For us, that meant loading Amazon Redshift in frequent micro batches and allowing our customers to query Amazon Redshift directly to get results in near real time. The benefits were immediately evident. Our customers could see how their campaigns performed faster than with other solutions, and react sooner to the ever-changing media supply pricing and availability. They were very happy.

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AWS Big Data & Analytics Sessions at Re:Invent 2017

We can’t believe that there are just few days left before re:Invent 2017. If you are attending this year, you’ll want to check out our Big Data sessions! The Big Data and Machine Learning categories are bigger than ever. This post highlights the sessions that will be presented as part of the Analytics & Big Data track, as well as relevant sessions from other tracks like Architecture, Artificial Intelligence & Machine Learning, and IoT.

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Create an Amazon Redshift Data Warehouse That Can Be Securely Accessed Across Accounts

Data security is paramount in many industries. Organizations that shift their IT infrastructure to the cloud must ensure that their data is protected and that the attack surface is minimized. This post focuses on a method of securely loading a subset of data from one Amazon Redshift cluster to another Amazon Redshift cluster that is located in a different AWS account.

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Visualize AWS Cloudtrail Logs using AWS Glue and Amazon Quicksight

In this post, I walk through using AWS Glue and AWS Lambda to convert AWS CloudTrail logs from JSON to a query-optimized format dataset in Amazon S3. I then use Amazon Athena and Amazon QuickSight to query and visualize the data.

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Tableau 10.4 Supports Amazon Redshift Spectrum with External Amazon S3 Tables

by Robin Cottiss, Russell Christopher, and Vaidy Krishnan | on | in Amazon Redshift* | Permalink | Comments |  Share

We’re excited to announce today an update to our Amazon Redshift connector with support for Amazon Redshift Spectrum to analyze data in external Amazon S3 tables. With this update, you can quickly and directly connect Tableau to data in Amazon Redshift and analyze it in conjunction with data in Amazon S3—all with drag-and-drop ease.

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Build a Data Lake Foundation with AWS Glue and Amazon S3

A data lake is an increasingly popular way to store and analyze data that addresses the challenges of dealing with massive volumes of heterogeneous data. A data lake allows organizations to store all their data—structured and unstructured—in one centralized repository. Because data can be stored as-is, there is no need to convert it to a predefined schema. This post walks you through the process of using AWS Glue to crawl your data on Amazon S3 and build a metadata store that can be used with other AWS offerings.

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