AWS Big Data Blog

Apache Tez Now Available with Amazon EMR

Amazon EMR has added Apache Tez version 0.8.3 as a supported application in release 4.7.0. Tez is an extensible framework for building batch and interactive data processing applications on top of Hadoop YARN.

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Processing Amazon DynamoDB Streams Using the Amazon Kinesis Client Library

Asmita Barve-Karandikar is an SDE with DynamoDB Customers often want to process streams on an Amazon DynamoDB table with a significant number of partitions or with a high throughput. AWS Lambda and the DynamoDB Streams Kinesis Adapter are two ways to consume DynamoDB streams in a scalable way. While Lambda lets you run your application […]

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Use Apache Oozie Workflows to Automate Apache Spark Jobs (and more!) on Amazon EMR

Mike Grimes is an SDE with Amazon EMR As a developer or data scientist, you rarely want to run a single serial job on an Apache Spark cluster. More often, to gain insight from your data you need to process it in multiple, possibly tiered steps, and then move the data into another format and […]

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JOIN Amazon Redshift AND Amazon RDS PostgreSQL WITH dblink

Tony Gibbs is a Solutions Architect with AWS (Update: This blog post has been translated into Japanese) When it comes to choosing a SQL-based database in AWS, there are many options. Sometimes it can be difficult to know which one to choose. For example, when would you use Amazon Aurora instead of Amazon RDS PostgreSQL […]

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Supercharge SQL on Your Data in Apache HBase with Apache Phoenix

With today’s launch of Amazon EMR release 4.7, you can now create clusters with Apache Phoenix 4.7.0 for low-latency SQL and OLTP workloads. Phoenix uses Apache HBase as its backing store (HBase 1.2.1 is included on Amazon EMR release 4.7.0), using HBase scan operations and coprocessors for fast performance. Additionally, you can map Phoenix tables […]

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Using Spark SQL for ETL

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 […]

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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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Real-time in-memory OLTP and Analytics with Apache Ignite on AWS

Babu Elumalai is a Solutions Architect with AWS Organizations are generating tremendous amounts of data, and they increasingly need tools and systems that help them use this data to make decisions. The data has both immediate value (for example, trying to understand how a new promotion is performing in real time) and historic value (trying […]

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From SQL to Microservices: Integrating AWS Lambda with Relational Databases

Bob Strahan is a Senior Consultant with AWS Professional Services AWS Lambda has emerged as excellent compute platform for modern microservices architecture, driving dramatic advancements in flexibility, resilience, scale and cost effectiveness. Many customers can take advantage of this transformational technology from within their existing relational database applications. In this post, we explore how to […]

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