AWS Big Data Blog

Tag: Spark

Using Python 3.4 on EMR Spark Applications

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 […]

Sharpen your Skill Set with Apache Spark on the AWS Big Data Blog

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 […]

Crunching Statistics at Scale with SparkR on Amazon EMR

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 […]

Anomaly Detection Using PySpark, Hive, and Hue on Amazon EMR

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 […]

Analyze Your Data on Amazon DynamoDB with Apache Spark

Manjeet Chayel is a Solutions Architect with AWS Every day, tons of customer data is generated, such as website logs, gaming data, advertising data, and streaming videos. Many companies capture this information as it’s generated and process it in real time to understand their customers. Amazon DynamoDB is a fast and flexible NoSQL database service […]

Optimize Spark-Streaming to Efficiently Process Amazon Kinesis Streams

Rahul Bhartia is a Solutions Architect with AWS Martin Schade, a Solutions Architect with AWS, also contributed to this post. Do you use real-time analytics on AWS to quickly extract value from large volumes of data streams? For example, have you built a recommendation engine on clickstream data to personalize content suggestions in real time […]

Submitting User Applications with spark-submit

Francisco Oliveira is a consultant with AWS Professional Services Customers starting their big data journey often ask for guidelines on how to submit user applications to Spark running on Amazon EMR. For example, customers ask for guidelines on how to size memory and compute resources available to their applications and the best resource allocation model […]

Large-Scale Machine Learning with Spark on Amazon EMR

This is a guest post by Jeff Smith, Data Engineer at Intent Media. Intent Media, in their own words: “Intent Media operates a platform for advertising on commerce sites.  We help online travel companies optimize revenue on their websites and apps through sophisticated data science capabilities. On the data team at Intent Media, we are […]