Category: Amazon EMR on EKS

This post demonstrates how to build a centralized observability platform using SHS for Spark applications running on EMR on EKS. We showcase how to enhance SHS with performance monitoring tools, with a pattern applicable to many monitoring solutions such as SparkMeasure and DataFlint.

This post shows how to enhance the multi-cluster solution by integrating Amazon Managed Workflows for Apache Airflow (Amazon MWAA) with BPG. By using Amazon MWAA, we add job scheduling and orchestration capabilities, enabling you to build a comprehensive end-to-end Spark-based data processing pipeline.

In this post, we explore the design patterns for implementing the Hive Metastore (HMS) with EMR on EKS with Spark Operator, each offering distinct advantages depending on your requirements. Whether you choose to deploy HMS as a sidecar container within the Apache Spark Driver pod, or as a Kubernetes deployment in the data processing EKS cluster, or as an external HMS service in a separate EKS cluster, the key considerations revolve around communication efficiency, scalability, resource isolation, high availability, and security.

In our previous post Backtesting index rebalancing arbitrage with Amazon EMR and Apache Iceberg, we showed how to use Apache Iceberg in the context of strategy backtesting. In this post, we focus on data management implementation options such as accessing data directly in Amazon Simple Storage Service (Amazon S3), using popular data formats like Parquet, or using open table formats like Iceberg. Our experiments are based on real-world historical full order book data, provided by our partner CryptoStruct, and compare the trade-offs between these choices, focusing on performance, cost, and quant developer productivity.

AWS customers often process petabytes of data using Amazon EMR on EKS. In enterprise environments with diverse workloads or varying operational requirements, customers frequently choose a multi-cluster setup due to the following advantages: Better resiliency and no single point of failure – If one cluster fails, other clusters can continue processing critical workloads, maintaining business […]

AWS recently announced that Apache Flink is generally available for Amazon EMR on Amazon Elastic Kubernetes Service (EKS). Apache Flink is a scalable, reliable, and efficient data processing framework that handles real-time streaming and batch workloads (but is most commonly used for real-time streaming). Amazon EMR on EKS is a deployment option for Amazon EMR […]

The construction of big data applications based on open source software has become increasingly uncomplicated since the advent of projects like Data on EKS, an open source project from AWS to provide blueprints for building data and machine learning (ML) applications on Amazon Elastic Kubernetes Service (Amazon EKS). In the realm of big data, securing […]

Apache Hive is a distributed, fault-tolerant data warehouse system that enables analytics at a massive scale. Using Spark SQL to run Hive workloads provides not only the simplicity of SQL-like queries but also taps into the exceptional speed and performance provided by Spark. Spark SQL is an Apache Spark module for structured data processing. One […]

Backtesting is a process used in quantitative finance to evaluate trading strategies using historical data. This helps traders determine the potential profitability of a strategy and identify any risks associated with it, enabling them to optimize it for better performance. Index rebalancing arbitrage takes advantage of short-term price discrepancies resulting from ETF managers’ efforts to […]

Amazon EMR is the industry-leading cloud big data solution, providing a collection of open-source frameworks such as Spark, Hive, Hudi, and Presto, fully managed and with per-second billing. Amazon EMR on Amazon EKS is a deployment option allowing you to deploy Amazon EMR on the same Amazon Elastic Kubernetes Service (Amazon EKS) clusters that is […]