AWS Big Data Blog
Use Sqoop to Transfer Data from Amazon EMR to Amazon RDS
In this post, I will show you how to transfer data using Apache Sqoop, which is a tool designed to transfer data between Hadoop and relational databases. Support for Apache Sqoop is available in Amazon EMR releases 4.4.0 and later.
Read MoreAnalyze Realtime Data from Amazon Kinesis Streams Using Zeppelin and Spark Streaming
This post shows you how you can use Spark Streaming to process data coming from Amazon Kinesis streams, build some graphs using Zeppelin, and then store the Zeppelin notebook in Amazon S3.
Read MoreApache 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.
Read MoreProcessing 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 […]
Read MoreUse 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 […]
Read MoreJOIN 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 […]
Read MoreSupercharge 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 […]
Read MoreUsing 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 […]
Read MoreUsing 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 […]
Read MoreReal-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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