Category: Amazon EMR

Presto is a popular distributed SQL query engine for interactive data analytics. With its massively parallel processing (MPP) architecture, it’s capable of directly querying large datasets without the need of time-consuming and costly ETL processes. With a properly tuned Presto cluster you can run fast queries against big data with response times ranging from subsecond […]

Tens of thousands of customers use Amazon EMR to run big data analytics applications on Apache Spark, Apache Hive, Apache HBase, Apache Flink, Apache Hudi, and Presto at scale. Amazon EMR automates the provisioning and scaling of these frameworks, and delivers high performance at low cost with optimized runtimes and support for a wide range […]

May 2024: This post was reviewed and updated with a new dataset. In a previous post, we introduced the Amazon EMR notebook APIs, which allow you to programmatically run a notebook on Amazon EMR Studio (preview) without accessing the AWS web console. With the APIs, you can schedule running EMR notebooks with cron scripts, chain multiple notebooks, […]

With Amazon EMR release 6.1.0, Amazon EMR runtime for Apache Spark is now available for Spark 3.0.0. EMR runtime for Apache Spark is a performance-optimized runtime for Apache Spark that is 100% API compatible with open-source Apache Spark. In our benchmark performance tests using TPC-DS benchmark queries at 3 TB scale, we found EMR runtime […]

Amazon Managed Workflows for Apache Airflow (Amazon MWAA) is a fully managed service that makes it easy to run open-source versions of Apache Airflow on AWS and build workflows to run your extract, transform, and load (ETL) jobs and data pipelines. You can use AWS Step Functions as a serverless function orchestrator to build scalable […]

This post was last updated July 2022. Data security is an important pillar in data governance. It includes authentication, authorization , encryption and audit. Amazon EMR enables you to set up and run clusters of Amazon Elastic Compute Cloud (Amazon EC2) instances with open-source big data applications like Apache Spark, Apache Hive, Apache Flink, and Presto. You may […]

June 2024: This post was reviewed and updated to add instructions for using PyDeequ with Amazon SageMaker Notebook, SageMaker Studio, EMR, and updated the examples against a new dataset. March 2023: You can now use AWS Glue Data Quality to measure and manage the quality of your data. AWS Glue Data Quality is built on Deequ […]

This is a guest post co-authored by Pradip Thoke of Dream11. In their own words, “Dream11, the flagship brand of Dream Sports, is India’s biggest fantasy sports platform, with more than 100 million users. We have infused the latest technologies of analytics, machine learning, social networks, and media technologies to enhance our users’ experience. Dream11 […]

We’re happy to announce Amazon EMR Studio (Preview), an integrated development environment (IDE) that makes it easy for data scientists and data engineers to develop, visualize, and debug applications written in R, Python, Scala, and PySpark. EMR Studio provides fully managed Jupyter notebooks and tools like Spark UI and YARN Timeline Service to simplify debugging. […]

This is a guest post by Gautham Acharya, Software Engineer III at the Allen Institute for Brain Science, in partnership with AWS Data Lab Solutions Architect Ranjit Rajan, and AWS Sr. Enterprise Account Executive Arif Khan. The human brain is one of the most complex structures in the universe. Billions of neurons and trillions of […]