Category: AWS Big Data

February 9, 2024: Amazon Kinesis Data Firehose has been renamed to Amazon Data Firehose. Read the AWS What’s New post to learn more. AWS re:Invent 2018 is almost here! This post includes a list of Amazon Kinesis sessions, chalk talks, and workshops at AWS re:Invent 2018. You can choose the link next to each session description for the […]

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.

Understanding Windows system and service health on a global scale is challenging. You capture server log data, and then analyze and manipulate the data in real time to create actionable telemetry insights. Amazon Kinesis Agent for Microsoft Windows makes it efficient to ingest Windows server log data into your AWS ecosystem for analysis. This blog […]

re:Invent 2018 is around the corner! This year, data and analytics tracks are bigger than ever. This blog post highlights the data and analytics sessions at re:Invent 2018. If you’re attending this year, you want to check out the sessions, workshops, chalk talks, and builder sessions that we have at the conference. As in previous […]

In this blog post, we’ll go through the steps needed to build an ETL pipeline that consumes from one source in one VPC and outputs it to another source in a different VPC. We’ll set up in multiple VPCs to reproduce a situation where your database instances are in multiple VPCs for isolation related to security, audit, or other purposes.

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 […]

As organizations expand analytics practices and hire data scientists and other specialized roles, big data pipelines are growing increasingly complex. Sophisticated models are being built using the troves of data being collected every second. The bottleneck today is often not the know-how of analytical techniques. Rather, it’s the difficulty of building and maintaining ETL (extract, transform, and load) jobs using tools that might be unsuitable for the cloud. In this post, I demonstrate a solution to this challenge.

Since we launched Amazon Redshift, thousands of customers have trusted us to get uncompromising speed for their most complex analytical workloads. Over the course of 2017, our customers benefited from a 3x to 5x performance gain, resulting from short query acceleration, result caching, late materialization, and many other under-the-hood improvements. In this post, we highlight […]

February 2025: The bootstrap action script in this blog post uses IMDS v1 for accessing EC2 instance metadata. The script does not support IMDS v2 and cannot be used in an AWS account which has IMDS v2 enforced across the account. Using the script in an IMDS v2 enabled account will cause issues and unexpected […]

Clickstream analysis tools handle their data well, and some even have impressive BI interfaces. However, analyzing clickstream data in isolation comes with many limitations. For example, a customer is interested in a product or service on your website. They go to your physical store to purchase it. The clickstream analyst asks, “What happened after they […]