AWS Big Data Blog

Category: AWS Lambda

Optimize downstream data processing with Amazon Kinesis Data Firehose and Amazon EMR running Apache Spark

This blog post shows how to use Amazon Kinesis Data Firehose to merge many small messages into larger messages for delivery to Amazon S3, which results in faster processing with Amazon EMR running Spark. This post also shows how to read the compressed files using Apache Spark that are in Amazon S3, which does not have a proper file name extension and store back in Amazon S3 in parquet format.

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Optimize Amazon EMR costs with idle checks and automatic resource termination using advanced Amazon CloudWatch metrics and AWS Lambda

Many customers use Amazon EMR to run big data workloads, such as Apache Spark and Apache Hive queries, in their development environment. Data analysts and data scientists frequently use these types of clusters, known as analytics EMR clusters. Users often forget to terminate the clusters after their work is done. This leads to idle running […]

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Build and automate a serverless data lake using an AWS Glue trigger for the Data Catalog and ETL jobs

Today, data is flowing from everywhere, whether it is unstructured data from resources like IoT sensors, application logs, and clickstreams, or structured data from transaction applications, relational databases, and spreadsheets. Data has become a crucial part of every business. This has resulted in a need to maintain a single source of truth and automate the […]

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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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How to build a front-line concussion monitoring system using AWS IoT and serverless data lakes – Part 2

In part 1 of this series, we demonstrated how to build a data pipeline in support of a data lake. We used key AWS services such as Amazon Kinesis Data Streams, Kinesis Data Analytics, Kinesis Data Firehose, and AWS Lambda. In part 2, we discuss how to process and visualize the data by creating a […]

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How to build a front-line concussion monitoring system using AWS IoT and serverless data lakes – Part 1

In this two-part series, we show you how to build a data pipeline in support of a data lake. We use key AWS services such as Amazon Kinesis Data Streams, Kinesis Data Analytics, Kinesis Data Firehose, and AWS Lambda. In part 2, we focus on generating simple inferences from that data that can support RTP parameters.

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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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