AWS Big Data Blog

Category: Amazon EMR

Best practices to optimize data access performance from Amazon EMR and AWS Glue to Amazon S3

June 2023: This post was reviewed and updated for accuracy. Customers are increasingly building data lakes to store data at massive scale in the cloud. It’s common to use distributed computing engines, cloud-native databases, and data warehouses when you want to process and analyze your data in data lakes. Amazon EMR and AWS Glue are […]

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New features from Apache Hudi 0.9.0 on Amazon EMR

Apache Hudi is an open-source transactional data lake framework that greatly simplifies incremental data processing and data pipeline development. It does this by providing transaction support and record-level insert, update, and delete capabilities on data lakes on Amazon Simple Storage Service (Amazon S3) or Apache HDFS. Apache Hudi is integrated with open-source big data analytics […]

Up to 15 times improvement in Hive write performance with the Amazon EMR Hive zero-rename feature

Our customers use Apache Hive on Amazon EMR for large-scale data analytics and extract, transform, and load (ETL) jobs. Amazon EMR Hive uses Apache Tez as the default job execution engine, which creates Directed Acyclic Graphs (DAGs) to process data. Each DAG can contain multiple vertices from which tasks are created to run the application […]

Create a low-latency source-to-data lake pipeline using Amazon MSK Connect, Apache Flink, and Apache Hudi

August 30, 2023: Amazon Kinesis Data Analytics has been renamed to Amazon Managed Service for Apache Flink. Read the announcement in the AWS News Blog and learn more. During the recent years, there has been a shift from monolithic to the microservices architecture. The microservices architecture makes applications easier to scale and quicker to develop, […]

How Cynamics built a high-scale, near-real-time, streaming AI inference system using AWS

This post is co-authored by Dr. Yehezkel Aviv, Co-Founder and CTO of Cynamics and Sapir Kraus, Head of Engineering at Cynamics. Cynamics provides a new paradigm of cybersecurity — predicting attacks long before they hit by collecting small network samples (less than 1%), inferring from them how the full network (100%) behaves, and predicting threats […]

Featured Stateful Architecture

Doing more with less: Moving from transactional to stateful batch processing

Amazon processes hundreds of millions of financial transactions each day, including accounts receivable, accounts payable, royalties, amortizations, and remittances, from over a hundred different business entities. All of this data is sent to the eCommerce Financial Integration (eCFI) systems, where they are recorded in the subledger. Ensuring complete financial reconciliation at this scale is critical […]

How Belcorp decreased cost and improved reliability in its big data processing framework using Amazon EMR managed scaling

This is a guest post by Diego Benavides and Luis Bendezú, Senior Data Architects, Data Architecture Direction at Belcorp. Belcorp is one of the main consumer packaged goods (CPG) companies providing cosmetics products in the region for more than 50 years, allocated to around 13 countries in North, Central, and South America (AMER). Born in Peru […]

New features from Apache Hudi 0.7.0 and 0.8.0 available on Amazon EMR

Apache Hudi is an open-source transactional data lake framework that greatly simplifies incremental data processing and data pipeline development by providing record-level insert, update, and delete capabilities. This record-level capability is helpful if you’re building your data lakes on Amazon Simple Storage Service (Amazon S3) or Hadoop Distributed File System (HDFS). You can use it […]

How Goldman Sachs built persona tagging using Apache Flink on Amazon EMR

The Global Investment Research (GIR) division at Goldman Sachs is responsible for providing research and insights to the firm’s clients in the equity, fixed income, currency, and commodities markets. One of the long-standing goals of the GIR team is to deliver a personalized experience and relevant research content to their research users. Previously, in order to customize […]

Announcing Amazon EMR Serverless (Preview): Run big data applications without managing servers

Today we’re happy to announce Amazon EMR Serverless, a new option in Amazon EMR that makes it easy and cost-effective for data engineers and analysts to run petabyte-scale data analytics in the cloud. With EMR Serverless, you can run applications built using open-source frameworks such as Apache Spark and Hive without having to configure, manage, […]