List of Amazon Redshift Customers
Amazon Web Services (AWS)
410 Terry Avenue North,
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United States
Seattle, 98109-5210, WA,
United States
1 206-266-1000
Professional Services
135000
$128.7B
Since 2010, our global team of researchers has been studying Amazon Redshift customers around the world, aggregating massive amounts of data points that form the basis of our forecast assumptions and perhaps the rise and fall of certain vendors and their products on a quarterly basis.
Each quarter our research team identifies companies that have purchased Amazon Redshift for Data Warehouse from public (Press Releases, Customer References, Testimonials, Case Studies and Success Stories) and proprietary sources, including the customer size, industry, location, implementation status, partner involvement, LOB Key Stakeholders and related IT decision-makers contact details.
Companies using Amazon Redshift for Data Warehouse include: Amazon, a United States based Retail organisation with 1578000 employees and revenues of $637.96 billion, Cigna Healthcare, a United States based Insurance organisation with 71295 employees and revenues of $244.38 billion, United States Department of Agriculture (USDA), a United States based Government organisation with 100000 employees and revenues of $230.00 billion, Cardinal Health, a United States based Healthcare organisation with 53084 employees and revenues of $222.58 billion, Comcast, a United States based Communications organisation with 182000 employees and revenues of $123.73 billion and many others.
Contact us if you need a completed and verified list of companies using Amazon Redshift, including the breakdown by industry (21 Verticals), Geography (Region, Country, State, City), Company Size (Revenue, Employees, Asset) and related IT Decision Makers, Key Stakeholders, business and technology executives responsible for the IaaS software purchases.
The Amazon Redshift customer wins are being incorporated in our Enterprise Applications Buyer Insight and Technographics Customer Database which has over 100 data fields that detail company usage of IaaS software systems and their digital transformation initiatives. Apps Run The World wants to become your No. 1 technographic data source!
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| Logo | Customer | Industry | Empl. | Revenue | Country | Vendor | Application | Category | When | SI | Insight |
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AJE Group | Consumer Packaged Goods | 10000 | $1.3B | Peru | Amazon Web Services (AWS) | Amazon Redshift | Data Warehouse | 2019 | CloudHesive |
In 2019, AJE Group deployed Amazon Redshift as the core Cloud Data Warehouse within a centralized corporate analytics platform on Amazon Web Services, engaging CloudHesive as the implementation partner. The initiative consolidated distributed on-premises infrastructure into a single analytics fabric that supports operations across AJE Group’s footprint in 22 countries and spans analytics, sales, and customer-facing teams.
The Amazon Redshift configuration was positioned as the universal repository and single source of truth, supporting SQL analysis of structured and semi-structured data. AJE Group paired Amazon Redshift with a data lake on Amazon S3 for storage and used AWS Glue for serverless data integration to discover, prepare, and move data. The company moved on-premises SQL databases to AWS using AWS Database Migration Service to populate the new platform and accelerate pipeline throughput, enabling a 35 percent reduction in ETL times.
Integrations centered on Amazon Redshift, Amazon S3, AWS Glue, and AWS Database Migration Service, creating end-to-end ingestion and analytics flows that deliver near-real-time visibility. The Cloud Data Warehouse solution was instrumented to feed country-level data models and to provide next-day metrics to business users, enabling internal teams to access data roughly 20 percent faster than before.
Governance and operational changes included centralizing data access patterns, establishing Amazon Redshift as the companywide analytics repository, and expanding data literacy to support broader consumption. Reported outcomes tied to the AWS implementation include 35 percent faster ETL, 15 percent savings in infrastructure costs through cloud consumption models, improved scalability for market expansion, and increased agility to pursue predictive analytics and AI opportunities.
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Amazon | Retail | 1578000 | $638.0B | United States | Amazon Web Services (AWS) | Amazon Redshift | Data Warehouse | 2017 | n/a |
In 2017, Amazon implemented Amazon Redshift as its Cloud Data Warehouse to centralize inbound shipment analytics and create a single source of truth for procurement and fulfillment workflows. The initial implementation concentrated on SQL driven analytics, scalable data modeling and production data pipelines to support operational reporting for fulfillment centers and procurement teams across global operations.
The implementation used Amazon Redshift for core storage and analytics, with Python based ETL, R for advanced clustering analysis, and Git for code management. Engineers redesigned data pipelines and data models inside Amazon Redshift and optimized ETL execution time to 40 percent of prior runtimes, while improving invoice to receipt matching rates from 75 percent to 92 percent by identifying and mapping new attributes into the warehouse model.
A robust ingestion architecture was established using Python, AWS Lambda, Amazon S3 and EC2 to stage and transfer data into ElasticSearch, enabling business teams to consume datasets through Kibana for real time reporting and deep dive analysis. The ingestion and migration tooling built was used to migrate almost 8000 tables and is running in production as more than 100 scheduled jobs, and the team led two migration projects moving MySQL workloads to AWS Aurora, reducing the duration of the second migration to 2 percent of the first by reusing a migration service tool.
Operational governance centered on a single source of truth dataset for inbound shipments, with the implementer acting as subject matter expert and owning mapping rules, data quality checks and pipeline orchestration. Analytical capabilities built on Amazon Redshift included hourly fulfillment center performance pipelines and an 800 million query clustering exercise using KMeans in R to inform deep dive dashboards, and the centralized dataset uncovered cost saving opportunities totaling more than $5 MM per month.
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Ameriprise Financial | Banking and Financial Services | 13800 | $15.5B | United States | Amazon Web Services (AWS) | Amazon Redshift | Data Warehouse | 2020 | n/a |
In 2020 Ameriprise Financial implemented Amazon Redshift as its Cloud Data Warehouse to centralize analytics and reporting for enterprise business intelligence. The Amazon Redshift Cloud Data Warehouse deployment on AWS was positioned to support Tableau and SAS Visual Analytics reporting workflows and to serve as a confidential analytics store for data ingested from on premises sources and Hadoop ecosystems.
Implementation work focused on data ingestion and processing, including writing data normalization jobs for new data loaded into Amazon Redshift, developing optimized data collection and qualifying procedures, and authoring SQL scripts to validate mappings. The implementation included extensive query and environment optimization to improve query performance for Tableau and SAS Visual Analytics, and development of metrics, attributes, filters, and advanced visual calculations for downstream reporting.
The Redshift environment was integrated with the broader BI toolchain explicitly referenced in project artifacts, including Tableau Server administration and content publishing, Active Directory user and group provisioning for Tableau access control, Hive and Hadoop for large data set analysis, Informatica and PLUTORA for extract workflows, and Confluence for report documentation. Scheduled extract refreshes and Tableau Server scheduling were used to operationalize data currency, while Python and log analysis were employed to monitor event patterns and support operational troubleshooting.
Governance and quality controls included a Traceability Matrix mapping business requirements to test scripts, parallel and production testing to validate interfaces and production behavior, dashboard review sessions with end users prior to publish, and formal documentation and training for internal teams. Security and permissioning work included creation of users, groups, projects, and permission sets for Tableau Server as part of the data access governance model.
Explicit outcomes recorded in project notes include improved performance of data extracts by using context and action filters, scheduled extract refreshes to ensure up to date dashboards, and ongoing Redshift performance tuning to enable faster queries for BI consumers. Monitoring of KPIs and log analysis were instituted to identify technical issues and forecast event occurrences, supporting sustained operational reliability for the Amazon Redshift Cloud Data Warehouse.
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Amgen | Life Sciences | 28000 | $33.4B | United States | Amazon Web Services (AWS) | Amazon Redshift | Data Warehouse | 2020 | n/a |
In 2020 Amgen provisioned Amazon Redshift as a centralized Data Warehouse to consolidate analytics workloads and provide a single queryable store for downstream consumers. The Amazon Redshift deployment served as the enterprise data warehouse layer tied to an existing enterprise data lake architecture.
Implementation work centered on data engineering pipelines that extracted, transformed and loaded data from multiple sources and file formats into the enterprise data lake and then into Amazon Redshift. Data preparation and preprocessing occurred in Databricks using PySpark, with transformed datasets ingested into Amazon Redshift to support reporting and analytics. Data quality validation routines were implemented in Python and executed before and during ETL processing.
The Redshift environment was integrated with Databricks for cleansing and transformation, and with Apache Airflow to orchestrate ETL jobs and optimize scheduling and runtime. Operational ownership aligned to data engineering and analytics teams, with explicit engineering activity noted in Tampa, Florida where pipeline development and orchestration work was performed.
Governance and process changes focused on standardizing ingest and transformation workflows, embedding automated data quality checks into the ETL pipeline, and using Airflow to enforce scheduling and dependency controls. The configuration emphasized schema alignment and repeatable orchestration to enable downstream analytics consumers to access consistent, curated data in Amazon Redshift within the Data Warehouse layer.
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Astellas Pharma US | Life Sciences | 3000 | $700M | United States | Amazon Web Services (AWS) | Amazon Redshift | Data Warehouse | 2021 | n/a |
In 2021, Astellas Pharma US migrated core analytics and master data workloads to Amazon Redshift as its Data Warehouse platform. The engagement pursued two primary objectives, replacing multiple external vendor supplied datasets feeding the Customer Master in the MDM Hub and migrating ETL processes from IBM Netezza Appliance and IBM Data Stage to an AWS Redshift based architecture with subsequent conversion to Talend.
The implementation was executed in a phased architecture to reduce risk, first replatforming Netezza tables and Data Stage source and target bindings to Amazon Redshift, then stabilizing the Redshift table environment before converting ETL jobs from Data Stage to Talend. The Amazon Redshift deployment served as the central Data Warehouse repository for HCP and HCO customer information, supporting Customer Master processing and totem pole mastering logic changes required by new vendor data feeds.
Integrations and interfaces documented in the program included IBM Initiate for master data management, IBM Data Stage as the incumbent ETL, Netezza as the prior database platform, Oracle sources, Talend as the target ETL runtime, Erwin for data modeling, and AWS infrastructure components. Approximately 60 vendor supplied files were audited and reengineered into new ingestion pipelines feeding the MDM Hub, with source to target mappings updated to reflect the new vendor hierarchy for Health Care Provider and Health Care Organization records.
Governance and process restructuring included an end to end data lineage and ETL job lineage capability that enabled impact analysis and traceability, a formal data profiling methodology to gate incoming files, and updated totem pole logic in the MDM Hub for Customer Master resolution. Deliverables included high level and detailed specifications, user stories, use cases, logical and physical models created in Erwin, process flow diagrams and data mapping, all socialized with business and technical stakeholders to align enterprise standards.
Operational controls applied strong data privacy safeguards for HCO and HCP information, applying HIPPA rules and other regulatory considerations across ingestion and mastering processes. The program identified gaps in prior ETL best practices, provided documented remediation approaches, and delivered a staged migration path that separated infrastructure replatforming from ETL conversion to limit disruption to business and IT operations.
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Communications | 146040 | $122.4B | United States | Amazon Web Services (AWS) | Amazon Redshift | Data Warehouse | 2020 | n/a |
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Banking and Financial Services | 1250 | $120M | United States | Amazon Web Services (AWS) | Amazon Redshift | Data Warehouse | 2020 | n/a |
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Professional Services | 2364 | $1.7B | United States | Amazon Web Services (AWS) | Amazon Redshift | Data Warehouse | 2018 | n/a |
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Professional Services | 1700 | $140M | United Kingdom | Amazon Web Services (AWS) | Amazon Redshift | Data Warehouse | 2015 | n/a |
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Professional Services | 477 | $55M | Canada | Amazon Web Services (AWS) | Amazon Redshift | Data Warehouse | 2020 | n/a |
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Buyer Intent: Companies Evaluating Amazon Redshift
ARTW Buyer Intent uncovers actionable customer signals, identifying software buyers actively evaluating Amazon Redshift. Gain ongoing access to real-time prospects and uncover hidden opportunities. Companies Actively Evaluating Amazon Redshift for Data Warehouse include:
- Nebo School District., a United States based Education organization with 2000 Employees
- Blackstone, a United States based Banking and Financial Services company with 4895 Employees
- HP, a United States based Manufacturing organization with 58000 Employees
Discover Software Buyers actively Evaluating Enterprise Applications
| Logo | Company | Industry | Employees | Revenue | Country | Evaluated |
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Nebo School District. | Education | 2000 | $335M | United States | 2026-03-04 |
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Blackstone | Banking and Financial Services | 4895 | $11.0B | United States | 2026-01-25 |
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Hecht Kugellager | Distribution | 28 | $8M | Germany | 2025-11-22 |
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Manufacturing | 58000 | $53.6B | United States | 2025-11-12 |
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Insurance | 765 | $311M | United States | 2025-11-06 |
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Education | 21489 | $6.2B | United States | 2025-10-20 |
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Insurance | 112 | $25M | United States | 2025-09-25 |
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Banking and Financial Services | 16603 | $2.0B | South Africa | 2025-09-16 |
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Education | 7000 | $350M | Mexico | 2025-09-02 |
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Healthcare | 429 | $28M | United States | 2025-08-07 |
FAQ - APPS RUN THE WORLD Amazon Redshift Coverage
Amazon Redshift is a Data Warehouse solution from Amazon Web Services (AWS).
Companies worldwide use Amazon Redshift, from small firms to large enterprises across 21+ industries.
Organizations such as Amazon, Cigna Healthcare, United States Department of Agriculture (USDA), Cardinal Health and Comcast are recorded users of
Amazon Redshift for Data Warehouse.
Companies using Amazon Redshift are most concentrated in Retail, Insurance and Government, with adoption spanning over 21 industries.
Companies using Amazon Redshift are most concentrated in United States, with adoption tracked across 195 countries worldwide. This global distribution highlights the popularity of Amazon Redshift across Americas, EMEA, and APAC.
Companies using Amazon Redshift range from small businesses with 0-100 employees - 1.25%, to mid-sized firms with 101-1,000 employees - 13.75%, large organizations with 1,001-10,000 employees - 36.25%, and global enterprises with 10,000+ employees - 48.75%.
Customers of Amazon Redshift include firms across all revenue levels — from $0-100M, to $101M-$1B, $1B-$10B, and $10B+ global corporations.
Contact APPS RUN THE WORLD to access the full verified Amazon Redshift customer database with detailed Firmographics such as industry, geography, revenue, and employee breakdowns as well as key decision makers in charge of Data Warehouse.