AWS Big Data Blog

Category: Analytics

Automate deployment of an Amazon QuickSight analysis connecting to an Amazon Redshift data warehouse with an AWS CloudFormation template

Amazon Redshift is the most widely used data warehouse in the cloud, best suited for analyzing exabytes of data and running complex analytical queries. Amazon QuickSight is a fast business analytics service to build visualizations, perform ad hoc analysis, and quickly get business insights from your data. QuickSight provides easy integration with Amazon Redshift, providing […]

Achieve up to 27% better price-performance for Spark workloads with AWS Graviton2 on Amazon EMR Serverless

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 supports larger worker sizes to run more compute and memory-intensive workloads

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 […]

How Strategic Blue uses Amazon QuickSight and AWS Cost and Usage Reports to help their customers save millions

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 […]

Automate replication of relational sources into a transactional data lake with Apache Iceberg and AWS Glue

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 […]

Monitor Apache HBase on Amazon EMR using Amazon Managed Service for Prometheus and Amazon Managed Grafana

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 […]

Chargeback Gurus empowers eCommerce merchants with advanced chargeback intelligence to recover millions using Amazon QuickSight

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 […]

How OLX Group migrated to Amazon Redshift RA3 for simpler, faster, and more cost-effective analytics

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 […]

Synchronize your Salesforce and Snowflake data to speed up your time to insight with Amazon AppFlow

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 […]

­­Use fuzzy string matching to approximate duplicate records in Amazon Redshift

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.