Category: Amazon EMR

Amazon EMR provides a managed service to easily run analytics applications using open-source frameworks such as Apache Spark, Hive, Presto, Trino, HBase, and Flink. The Amazon EMR runtime for Spark and Presto includes optimizations that provide over two times performance improvements over open-source Apache Spark and Presto, so that your applications run faster and at […]

Apache Hudi is an open-source transactional data lake framework that greatly simplifies incremental data processing and data pipeline development. It does this by bringing core warehouse and database functionality directly to a data lake on Amazon Simple Storage Service (Amazon S3) or Apache HDFS. Hudi provides table management, instantaneous views, efficient upserts/deletes, advanced indexes, streaming […]

AWS Controllers for Kubernetes (ACK) was announced in August, 2020, and now supports 14 AWS service controllers as generally available with an additional 12 in preview. The vision behind this initiative was simple: allow Kubernetes users to use the Kubernetes API to manage the lifecycle of AWS resources such as Amazon Simple Storage Service (Amazon […]

Amazon EMR on Amazon EKS is a deployment option for Amazon EMR that allows organizations to run Apache Spark on Amazon Elastic Kubernetes Service (Amazon EKS). With EMR on EKS, the Spark jobs run on the Amazon EMR runtime for Apache Spark. This increases the performance of your Spark jobs so that they run faster […]

Data engineers and data scientists are dependent on distributed data processing infrastructure like Amazon EMR to perform data processing and advanced analytics jobs on large volumes of data. In most mid-size and enterprise organizations, cloud operations teams own procuring, provisioning, and maintaining the IT infrastructures, and their objectives and best practices differ from the data […]

This post was co-written with Daniel Gu and Yolanda Wang, from Kyligence. Today, more than ever, organizations realize that modern business runs on data—almost all our interactions with business are based on data, and organizations must use analytics to understand, plan, and improve their operations. That is where Online Analytical Processing (OLAP) comes in. OLAP […]

You can use the Amazon EMR Steps API to submit Apache Hive, Apache Spark, and others types of applications to an EMR cluster. You can invoke the Steps API using Apache Airflow, AWS Steps Functions, the AWS Command Line Interface (AWS CLI), all the AWS SDKs, and the AWS Management Console. Jobs submitted with the […]

Data lakes on Amazon Simple Storage Service (Amazon S3) have become the default repository for all enterprise data and serve as a common choice for a large number of users querying from a variety of analytics and machine learning (ML) tools. Oftentimes you want to ingest data continuously into the data lake from multiple sources […]

A data lake is a centralized repository that allows you to store all your structured and unstructured data at any scale. You can keep your data as is in your object store or file-based storage without having to first structure the data. Additionally, you can run different types of analytics against your loosely formatted data […]

Update Feb 2023: AWS Step Functions adds direct integration for 35 services including Amazon EMR Serverless. In the current version of this blog, we are able to submit an EMR Serverless job by invoking the APIs directly from a Step Functions workflow. We are using the Lambda only for polling the status of the job […]