AWS Big Data Blog
Category: Amazon Simple Storage Service (S3)
How to migrate a Hue database from an existing Amazon EMR cluster
This post describes the step-by-step process for migrating the Hue database from an existing EMR cluster.
Power from wind: Open data on AWS
Data that describe processes in a spatial context are everywhere in our day-to-day lives and they dominate big data problems. Map data, for instance, whether describing networks of roads or remote sensing data from satellites, get us where we need to go. Atmospheric data from simulations and sensors underlie our weather forecasts and climate models. […]
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.
Best Practices for Running Apache Cassandra on Amazon EC2
In this post, we outline three Cassandra deployment options, as well as provide guidance about determining the best practices for your use case.
Build a Multi-Tenant Amazon EMR Cluster with Kerberos, Microsoft Active Directory Integration and IAM Roles for EMRFS
In this post, we will discuss what EMRFS authorization is (Amazon S3 storage-level access control) and show how to configure the role mappings with detailed examples.
Dynamically Create Friendly URLs for Your Amazon EMR Web Interfaces
This solution provides a serverless approach to automatically assigning a friendly name for your EMR cluster for easy access to popular notebooks and other web interfaces.
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.
Building a Real World Evidence Platform on AWS
Deriving insights from large datasets is central to nearly every industry, and life sciences is no exception. To combat the rising cost of bringing drugs to market, pharmaceutical companies are looking for ways to optimize their drug development processes. They are turning to big data analytics to better quantify the effect that their drug compounds […]
Setting up Read Replica Clusters with HBase on Amazon S3
Many customers have taken advantage of the numerous benefits of running Apache HBase on Amazon S3 for data storage, including lower costs, data durability, and easier scalability. Customers such as FINRA have lowered their costs by 60% by moving to an HBase on S3 architecture along with the numerous operational benefits that come with decoupling […]
Analyze OpenFDA Data in R with Amazon S3 and Amazon Athena
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 […]