Category: Amazon EMR

Ben Snively is a Solutions Architect with AWS With big data, you deal with many different formats and large volumes of data. SQL-style queries have been around for nearly four decades. Many systems support SQL-style syntax on top of the data layers, and the Hadoop/Spark ecosystem is no exception. This allows companies to try new […]

Bruno Faria is a Big Data Support Engineer for Amazon Web Services Many data scientists choose Python when developing on Spark. With last month’s Amazon EMR release 4.6, we’ve made it even easier to use Python: Python 3.4 is installed on your EMR cluster by default. You’ll still find Python 2.6 and 2.7 on your […]

Russell Nash is a Solutions Architect with AWS. Amo Abeyaratne, a Big Data consultant with AWS, also contributed to this post. One of the most powerful features of Amazon EMR is the close integration with Amazon S3 through EMRFS. This allows you to take advantage of many S3 features, including support for S3 client-side and […]

The AWS Big Data Blog has a large community of authors who are passionate about Apache Spark and who regularly publish content that helps customers use Spark to build real-world solutions. You’ll see content on a variety of topics, including deep-dives on Spark’s internals, building Spark Streaming applications, creating machine learning pipelines using MLlib, and ways […]

Ben Snively is a Solutions Architect with AWS Jon Fritz, a Senior Product Manager for Amazon EMR, co-authored this post With today’s launch of Amazon EMR release 4.6, you can now quickly and easily provision a cluster with Apache HBase 1.2. Apache HBase is a massively scalable, distributed big data store in the Apache Hadoop ecosystem. It is […]

James Norvell is a Big Data Cloud Support Engineer for AWS Many Amazon EMR customers have architectures that track events and streams and store data in S3. This frequently leads to many small files. It’s now well known that Hadoop doesn’t deal well with small files. This issue can be amplified when migrating from Hadoop […]

Gopal Wunnava is a Senior Consultant with AWS Professional Services The number of data sources that use location, such as smartphones and sensory devices used in IoT (Internet of things), is expanding rapidly. This explosion has increased demand for analyzing spatial data. Geospatial intelligence (GEOINT) allows you to analyze data that has geographical or spatial […]

Christopher Crosbie is a Healthcare and Life Science Solutions Architect with Amazon Web Services. This post was co-authored by Ujjwal Ratan, a Solutions Architect with Amazon Web Services. ——————————— “And that’s the promise of precision medicine — delivering the right treatments, at the right time, every time to the right person.“ (President Obama, 2015 State […]

Christopher Crosbie is a Healthcare and Life Science Solutions Architect with Amazon Web Services. This post is co-authored by Gopal Wunnava, a Senior Consultant with AWS Professional Services. SparkR is an R package that allows you to integrate complex statistical analysis with large datasets. In this blog post, we introduce you running R with the […]

Veronika Megler, Ph.D., is a Senior Consultant with AWS Professional Services We are surrounded by more and more sensors – some of which we’re not even consciously aware. As sensors become cheaper and easier to connect, they create an increasing flood of data that’s getting cheaper and easier to store and process. However, sensor readings […]