AWS Big Data Blog

Tag: AWS Glue

Create real-time clickstream sessions and run analytics with Amazon Kinesis Data Analytics, AWS Glue, and Amazon Athena

Clickstream events are small pieces of data that are generated continuously with high speed and volume. Often, clickstream events are generated by user actions, and it is useful to analyze them. For example, you can detect user behavior in a website or application by analyzing the sequence of clicks a user makes, the amount of […]

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Our data lake story: How Woot.com built a serverless data lake on AWS

In this post, we talk about designing a cloud-native data warehouse as a replacement for our legacy data warehouse built on a relational database. At the beginning of the design process, the simplest solution appeared to be a straightforward lift-and-shift migration from one relational database to another. However, we decided to step back and focus […]

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Create cross-account and cross-region AWS Glue connections

In this blog post, we describe how to configure the networking routes and interfaces to give AWS Glue access to a data store in an AWS Region different from the one with your AWS Glue resources. In our example, we connect AWS Glue, located in Region A, to an Amazon Redshift data warehouse located in Region B.

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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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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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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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Unite Real-Time and Batch Analytics Using the Big Data Lambda Architecture, Without Servers!

In this post, I show you how you can use AWS services like AWS Glue to build a Lambda Architecture completely without servers. I use a practical demonstration to examine the tight integration between serverless services on AWS and create a robust data processing Lambda Architecture system.

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