AWS Big Data Blog

Category: Amazon Elastic Kubernetes Service

Unify log aggregation and analytics across compute platforms

February 9, 2024: Amazon Kinesis Data Firehose has been renamed to Amazon Data Firehose. Read the AWS What’s New post to learn more. Our customers want to make sure their users have the best experience running their application on AWS. To make this happen, you need to monitor and fix software problems as quickly as […]

Configure Amazon EMR Studio and Amazon EKS to run notebooks with Amazon EMR on EKS

Amazon EMR on Amazon EKS provides a deployment option for Amazon EMR that allows you to run analytics workloads on Amazon Elastic Kubernetes Service (Amazon EKS). This is an attractive option because it allows you to run applications on a common pool of resources without having to provision infrastructure. In addition, you can use Amazon […]

Reduce costs and increase resource utilization of Apache Spark jobs on Kubernetes with Amazon EMR on Amazon EKS

Amazon EMR on Amazon EKS is a deployment option for Amazon EMR that allows you to run Apache Spark on Amazon Elastic Kubernetes Service (Amazon EKS). If you run open-source Apache Spark on Amazon EKS, you can now use Amazon EMR to automate provisioning and management, and run Apache Spark up to three times faster. […]

Run and debug Apache Spark applications on AWS with Amazon EMR on Amazon EKS

Customers today want to focus more on their core business model and less on the underlying infrastructure and operational burden. As customers migrate to the AWS Cloud, they’re realizing the benefits of being able to innovate faster on their own applications by relying on AWS to handle big data platforms, operations, and automation. Many of […]

Run a Spark SQL-based ETL pipeline with Amazon EMR on Amazon EKS

Increasingly, a business’s success depends on its agility in transforming data into actionable insights, which requires efficient and automated data processes. In the previous post – Build a SQL-based ETL pipeline with Apache Spark on Amazon EKS, we described a common productivity issue in a modern data architecture. To address the challenge, we demonstrated how to utilize a declarative approach as the key enabler to improve efficiency, which resulted in a faster time to value for businesses. Generally speaking, managing applications declaratively in Kubernetes is a widely adopted best practice. You can use the same approach to build and deploy Spark applications with open-source or in-house build frameworks to achieve the same productivity goal.

Build a SQL-based ETL pipeline with Apache Spark on Amazon EKS

Today, the most successful and fastest growing companies are generally data-driven organizations. Taking advantage of data is pivotal to answering many pressing business problems; however, this can prove to be overwhelming and difficult to manage due to data’s increasing diversity, scale, and complexity. One of the most popular technologies that businesses use to overcome these […]

Manage and process your big data workflows with Amazon MWAA and Amazon EMR on Amazon EKS

Many customers are gathering large amount of data, generated from different sources such as IoT devices, clickstream events from websites, and more. To efficiently extract insights from the data, you have to perform various transformations and apply different business logic on your data. These processes require complex workflow management to schedule jobs and manage dependencies […]