AWS Big Data Blog

Category: Amazon Kinesis

Collect, parse, transform, and stream Windows events, logs, and metrics using Amazon Kinesis Agent for Microsoft Windows

A complete data pipeline that includes Amazon Kinesis Agent for Microsoft Windows (KA4W) can help you analyze and monitor the performance, security, and availability of Windows-based services. You can build near-real-time dashboards and alarms for your Windows services. You can also use visualization and business intelligence tools such as Amazon Athena, Kibana, Amazon QuickSight, and […]

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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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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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Getting started: Training resources for Big Data on AWS

Whether you’ve just signed up for your first AWS account or you’ve been with us for some time, there’s always something new to learn as our services evolve to meet the ever-changing needs of our customers. To help ensure you’re set up for success as you build with AWS, we put together this quick reference guide for Big Data training and resources available here on the AWS site.

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Best Practices for Running Apache Kafka on AWS

The best practices described in this post are based on our experience in running and operating large-scale Kafka clusters on AWS for more than two years. Our intent for this post is to help AWS customers who are currently running Kafka on AWS, and also customers who are considering migrating on-premises Kafka deployments to AWS.

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How I built a data warehouse using Amazon Redshift and AWS services in record time

Over the years, I have developed and created a number of data warehouses from scratch. Recently, I built a data warehouse for the iGaming industry single-handedly. To do it, I used the power and flexibility of Amazon Redshift and the wider AWS data management ecosystem. In this post, I explain how I was able to build a robust and scalable data warehouse without the large team of experts typically needed.

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