Month in Review: August 2016
Another month of big data solutions on the Big Data Blog. Take a look at our summaries below and learn, comment, and share. Thanks for reading!
Readmission Prediction Through Patient Risk Stratification Using Amazon Machine Learning
With this post, learn how to apply advanced analytics concepts like pattern analysis and machine learning to do risk stratification for patient cohorts.
Building and Deploying Custom Applications with Apache Bigtop and Amazon EMR
When you launch a cluster, Amazon EMR lets you choose applications that will run on your cluster. But what if you want to deploy your own custom application? This post shows you how to build a custom application for EMR for Apache Bigtop-based releases 4.x and greater.
Writing SQL on Streaming Data with Amazon Kinesis Analytics – Part 1
This post introduces you to Amazon Kinesis Analytics, the fundamentals of writing ANSI-Standard SQL over streaming data, and works through a simple example application that continuously generates metrics over time windows.
Monitor Your Application for Processing DynamoDB Streams
Learn how to monitor the Amazon Kinesis Client Library (KCL) application you use to process DynamoDB Streams to quickly track and resolve issues or failures so you can avoid losing data. Dashboards, metrics, and application logs all play a part. This post may be most relevant to Java applications running on Amazon EC2 instances.
Data Lake Ingestion: Automatically Partition Hive External Tables with AWS
This post introduces a simple data ingestion and preparation framework based on AWS Lambda, Amazon DynamoDB, and Apache Hive on EMR for data from different sources landing in S3. This solution lets Hive pick up new partitions as data is loaded into S3 because Hive by itself cannot detect new partitions as data lands.
FROM THE ARCHIVE
Powering Gaming Applications with Amazon DynamoDB (July 2014)
Learn how to quickly build a reliable and scalable database tier for a mobile game. We’ll walk through a design example and show how to power a sizable game for less than the cost of a daily cup of coffee. We’ll also profile a fast-growing customer who has scaled to millions of players while saving time and money with Amazon DynamoDB.
Leave a comment below to let us know what big data topics you’d like to see next on the AWS Big Data Blog.