AWS Big Data Blog

One of the great benefits of Amazon S3 is the ability to host, share, or consume public data sets. This provides transparency into data to which an external data scientist or developer might not normally have access. By exposing the data to the public, you can glean many insights that would have been difficult with […]

Nowadays, streaming data is seen and used everywhere—from social networks, to mobile and web applications, IoT devices, instrumentation in data centers, and many other sources. As the speed and volume of this type of data increases, the need to perform data analysis in real time with machine learning algorithms and extract a deeper understanding from […]

When Amazon S3 analytics was released in November 2016, it gave you the ability to analyze storage access patterns and transition the right data to the right storage class. You could also manually export the data to an S3 bucket to analyze, using the business intelligence tool of your choice, and gather deeper insights on usage and growth patterns. This helped you reduce storage costs while optimizing performance based on usage patterns. With today’s update, you can quickly and easily gain those deeper insights and benefits by analyzing and visualizing S3 analytics data in Amazon QuickSight. It takes just a single click from the S3 console, without the need for manual exports or additional data preparation.

Customers are using Amazon Kinesis Streams to ingest, process, and deliver data in real time from millions of devices or applications. Use cases for Kinesis Streams vary, but a few common ones include IoT data ingestion and analytics, log processing, clickstream analytics, and enterprise data bus architectures. Within milliseconds of data arrival, applications (KCL, Apache […]

If you run an operation that continuously generates a large amount of data, you may want to know what kind of data is being inserted by your application. The ability to analyze data intake quickly can be very valuable for business units, such as operations and marketing. For many operations, it’s important to see what […]

Amazon Redshift Spectrum enables you to run Amazon Redshift SQL queries on data that is stored in Amazon S3. With Redshift Spectrum, you can extend the analytic power of Amazon Redshift beyond the data that is stored natively in Amazon Redshift. Redshift Spectrum offers several capabilities that widen your possible implementation strategies. For example, it expands […]

Monitoring your AWS environment is important for security, performance, and cost control purposes. For example, by monitoring and analyzing API calls made to your Amazon EC2 instances, you can trace security incidents and gain insights into administrative behaviors and access patterns. The kinds of events you might monitor include console logins, Amazon EBS snapshot creation/deletion/modification, […]

In this post, we’ll demonstrate querying the Amazon Redshift audit data logged in S3 to provide answers to common use cases described preceding.

Although it’s common for Amazon EMR customers to process data directly in Amazon S3, there are occasions where you might want to copy data from S3 to the Hadoop Distributed File System (HDFS) on your Amazon EMR cluster. Additionally, you might have a use case that requires moving large amounts of data between buckets or regions. In these use cases, large datasets are too big for a simple copy operation.

In April, we announced Amazon Redshift Spectrum in the AWS Blog. Redshift Spectrum is a new feature of Amazon Redshift that allows you to run complex SQL queries against exabytes of data in Amazon without having to load and transform any data. We’re happy to announce that Amazon QuickSight now supports Redshift Spectrum. Starting today, […]