Category: Amazon EMR

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 […]

NOTE: Starting from emr-6.0.0 release, Hive LLAP is officially supported as a YARN service. So setting up LLAP using the instructions from this blog post (using a bootstrap action script) is not needed for releases emr-6.0.0 and onward. ——————————- Apache Hive is one of the most popular tools for analyzing large datasets stored in a Hadoop […]

In the world of data science, users must often sacrifice cluster set-up time to allow for complex usability scenarios. Amazon EMR allows data scientists to spin up complex cluster configurations easily, and to be up and running with complex queries in a matter of minutes. Data scientists often use scheduling applications such as Oozie to […]

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 […]

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 the healthcare field, data comes in all shapes and sizes. Despite efforts to standardize terminology, some concepts (e.g., blood glucose) are still often depicted in different ways. This post demonstrates how to convert an openly available dataset called MIMIC-III, which consists of de-identified medical data for about 40,000 patients, into an open source data […]

Starting with Amazon EMR 5.2.0, you have the option to run Apache HBase on Amazon S3. Running HBase on S3 gives you several added benefits, including lower costs, data durability, and easier scalability. HBase provides several options that you can use to migrate and back up HBase tables. The steps to migrate to HBase on […]

February 9, 2024: Amazon Kinesis Data Firehose has been renamed to Amazon Data Firehose. Read the AWS What’s New post to learn more. Last December, we introduced the Amazon Athena connector in Amazon QuickSight, in the Derive Insights from IoT in Minutes using AWS IoT, Amazon Kinesis Firehose, Amazon Athena, and Amazon QuickSight post. The […]

NOTE: As of November 2018, you can run Apache Flink programs with Amazon Kinesis Analytics for Java Applications in a fully managed environment. You can find further details in a new blog post on the AWS Big Data Blog and in this Github repository. ————————– September 8, 2021: Amazon Elasticsearch Service has been renamed to Amazon OpenSearch Service. See details. […]

Sometimes, data to be analyzed is spread across buckets owned by different accounts. In order to ensure data security, appropriate credentials management needs to be in place. This is especially true for large enterprises storing data in different Amazon S3 buckets for different departments. For example, a customer service department may need access to data […]