Tag Archives: ETL

Large-Scale Health Data Analytics with OHDSI

Categories: CDH Data Science

Data analytics is increasingly being brought to bear to treat human disease, but as more and more health data is stored in computer databases, one significant challenge is how to perform analyses across these disparate databases. In this post I take a look at the Observational Health Data Sciences and Informatics (or OHDSI, pronounced “Odyssey”) program that was formed to address this challenge, and which today accounts for 1.26 billion patient records collectively stored across 64 databases in 17 countries.

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How To Predict ICU Mortality with Digital Health Data, DL4J, Apache Spark and Cloudera

Categories: CDH Data Science Spark

Modeling EHR Data in Healthcare

In this case study, we take a look at modeling electronic health record (EHR) data with deep learning and Deeplearning4j (DL4J). We draw inspiration from recent research showing that carefully designed neural network architectures can learn effectively from the complex, messy data collected in EHRs. Specifically, we describe how to train an  long short-term memory recurrent neural network (LSTM RNN) to predict in-hospital mortality among patients hospitalized in the intensive care unit (ICU).

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Announcing Support for Spot Instances in Cloudera Altus

Categories: Cloud

A month ago, we publicly announced Cloudera Altus, our new platform–as–a–service offering, and today, we are expanding the Altus data engineering service to support AWS EC2 Spot instances. Cloud infrastructure is the most costly component of running Altus data engineering workloads in the cloud.  Altus EC2 Spot instance support makes it easy to significantly reduce the cost of cloud infrastructure by allowing users to provision Altus data engineering clusters backed by excess EC2 compute capacity at reduced prices.

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How To Set Up a Shared Amazon RDS as Your Hive Metastore

Categories: Cloud Hadoop Hive How-to Impala Spark Use Case

Before CDH 5.10, every CDH cluster had to have its own Apache Hive Metastore (HMS) backend database. This model is ideal for clusters where each cluster contains the data locally along with the metadata. In the cloud, however, many CDH clusters run directly on a shared object store (like Amazon S3), making it possible for the data to live across multiple clusters and beyond any cluster’s lifespan. In this scenario clusters need to regenerate and coordinate metadata for the underlying shared data individually.

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