Month in Review: January 2017
Another month of big data solutions on the Big Data Blog!
Take a look at our summaries below and learn, comment, and share. Thank you for reading!
Decreasing Game Churn: How Upopa used ironSource Atom and Amazon ML to Engage Users
Ever wondered what it takes to keep a user from leaving your game or application after all the hard work you put in? Wouldn’t it be great to get a chance to interact with the users before they’re about to leave? In this post, learn how ironSource worked with gaming studio Upopa to build an efficient, cheap, and accurate way to battle churn and make data-driven decisions using ironSource Atom’s data pipeline and Amazon ML.
Create a Healthcare Data Hub with AWS and Mirth Connect
Healthcare providers record patient information across different software platforms. Each of these platforms can have varying implementations of complex healthcare data standards. Also, each system needs to communicate with a central repository called a health information exchange (HIE) to build a central, complete clinical record for each patient. In this post, learn how to consume different data types as messages, transform the information within the messages, and then use AWS services to take action depending on the message type.
Call for Papers! DEEM: 1st Workshop on Data Management for End-to-End Machine Learning
Amazon and Matroid will hold the first workshop on Data Management for End-to-End Machine Learning (DEEM) on May 14th, 2017 in conjunction with the premier systems conference SIGMOD/PODS 2017 in Raleigh, North Carolina. DEEM brings together researchers and practitioners at the intersection of applied machine learning, data management, and systems research to discuss data management issues in ML application scenarios. The workshop is soliciting research papers that describe preliminary and ongoing research results.
Converging Data Silos to Amazon Redshift Using AWS DMS
In this post, learn to use AWS Database Migration Service (AWS DMS) and other AWS services to easily converge multiple heterogonous data sources to Amazon Redshift. You can then use Amazon QuickSight, to visualize the converged dataset to gain additional business insights.
Run Mixed Workloads with Amazon Redshift Workload Management
It’s common for mixed workloads to have some processes that require higher priority than others. Sometimes, this means a certain job must complete within a given SLA. Other times, this means you only want to prevent a non-critical reporting workload from consuming too many cluster resources at any one time. Without workload management (WLM), each query is prioritized equally, which can cause a person, team, or workload to consume excessive cluster resources for a process which isn’t as valuable as other more business-critical jobs. This post provides guidelines on common WLM patterns and shows how you can use WLM query insights to optimize configuration in production workloads.
Secure Amazon EMR with Encryption
In this post, learn how to set up encryption of data at multiple levels using security configurations with EMR. You’ll walk through the step-by-step process to achieve all the encryption prerequisites, such as building the KMS keys, building SSL certificates, and launching the EMR cluster with a strong security configuration.
FROM THE ARCHIVE
Powering Amazon Redshift Analytics with Apache Spark and Amazon Machine Learning
In this post, learn to generate a predictive model for flight delays that can be used to help pick the flight least likely to add to your travel stress. To accomplish this, you’ll use Apache Spark running on Amazon EMR for extracting, transforming, and loading (ETL) the data, Amazon Redshift for analysis, and Amazon Machine Learning for creating predictive models.
Leave a comment below to let us know what big data topics you’d like to see next on the AWS Big Data Blog.