AWS Big Data Blog

Amazon EMR Serverless is a serverless option in Amazon EMR that makes it simple to run applications using open-source analytics frameworks such as Apache Spark and Hive without configuring, managing, or scaling clusters. At AWS re:Invent 2022, we announced support for running serverless Spark and Hive workloads with AWS Graviton2 (Arm64) on Amazon EMR Serverless. […]

Amazon EMR Serverless allows you to run open-source big data frameworks such as Apache Spark and Apache Hive without managing clusters and servers. With EMR Serverless, you can run analytics workloads at any scale with automatic scaling that resizes resources in seconds to meet changing data volumes and processing requirements. EMR Serverless automatically scales resources up […]

This is a guest post co-written with Frank Contrepois from Strategic Blue. For over 10 years, Strategic Blue has helped organizations unlock the most value from the cloud by enabling their customers to purchase non-standard commitments. By taking a commodity trading approach to purchasing from AWS, Strategic Blue helps customers purchase commitments for varying lengths […]

Organizations have chosen to build data lakes on top of Amazon Simple Storage Service (Amazon S3) for many years. A data lake is the most popular choice for organizations to store all their organizational data generated by different teams, across business domains, from all different formats, and even over history. According to a study, the […]

Amazon EMR provides a managed Apache Hadoop framework that makes it straightforward, fast, and cost-effective to run Apache HBase. Apache HBase is a massively scalable, distributed big data store in the Apache Hadoop ecosystem. It is an open-source, non-relational, versioned database that runs on top of the Apache Hadoop Distributed File System (HDFS). It’s built […]

This is a guest post by Suresh Dakshina and Damodharan Sampathkumar from Chargeback Gurus. Chargeback Gurus, a global financial technology company helps businesses fight, prevent, and win chargebacks. To date, we have helped businesses worldwide recover over $2 billion in lost revenue. As trusted advisors to card networks and Fortune 500 companies, we are known […]

This is a guest post by Miguel Chin, Data Engineering Manager at OLX Group and David Greenshtein, Specialist Solutions Architect for Analytics, AWS. OLX Group is one of the world’s fastest-growing networks of online marketplaces, operating in over 30 countries around the world. We help people buy and sell cars, find housing, get jobs, buy […]

This post was co-written with Amit Shah, Principal Consultant at Atos. Customers across industries seek meaningful insights from the data captured in their Customer Relationship Management (CRM) systems. To achieve this, they combine their CRM data with a wealth of information already available in their data warehouse, enterprise systems, or other software as a service […]

It’s common to ingest multiple data sources into Amazon Redshift to perform analytics. Often, each data source will have its own processes of creating and maintaining data, which can lead to data quality challenges within and across sources. One challenge you may face when performing analytics is the presence of imperfect duplicate records within the source data. This post presents one possible approach to addressing this challenge in an Amazon Redshift data warehouse using fuzzy matching.

This is a guest post by Siva Manickam and Prahalathan M from Vyaire Medical Inc. Vyaire Medical Inc. is a global company, headquartered in suburban Chicago, focused exclusively on supporting breathing through every stage of life. Established from legacy brands with a 65-year history of pioneering breathing technology, the company’s portfolio of integrated solutions is […]