AWS Big Data Blog

Tag: Spark

Real-time Stream Processing Using Apache Spark Streaming and Apache Kafka on AWS

Prasad Alle is a consultant with AWS Professional Services Intuit, a creator of business and financial management solutions, is a leading enterprise customer for AWS. The Intuit Data team (IDEA) at Intuit is responsible for building platforms and products that enable a data-driven personalized experience across Intuit products and services. One dimension of this platform […]

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How SmartNews Built a Lambda Architecture on AWS to Analyze Customer Behavior and Recommend Content

This is a guest post by Takumi Sakamoto, a software engineer at SmartNews. SmartNews in their own words: “SmartNews is a machine learning-based news discovery app that delivers the very best stories on the Web for more than 18 million users worldwide.” Data processing is one of the key technologies for SmartNews. Every team’s workload […]

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Generating Recommendations at Amazon Scale with Apache Spark and Amazon DSSTNE

Kiuk Chung is a Software Development Engineer with the Amazon Personalization team In Personalization at Amazon, we use neural networks to generate personalized product recommendations for our customers. Amazon’s product catalog is huge compared to the number of products that a customer has purchased, making our datasets extremely sparse. And with hundreds of millions of […]

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Analyze Realtime Data from Amazon Kinesis Streams Using Zeppelin and Spark Streaming

Manjeet Chayel is a Solutions Architect with AWS Streaming data is everywhere. This includes clickstream data, data from sensors, data emitted from billions of IoT devices, and more. Not surprisingly, data scientists want to analyze and explore these data streams in real time. This post shows you how you can use Spark Streaming to process […]

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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 […]

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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 […]

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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 […]

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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 […]

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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 […]

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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 […]

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