List of Cloudera Data Warehouse Customers
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Since 2010, our global team of researchers has been studying Cloudera Data Warehouse 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 Cloudera Data Warehouse 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 Cloudera Data Warehouse for Data Warehouse include: Woolworths Group, a Australia based Retail organisation with 201413 employees and revenues of $44.36 billion, Iqvia, a United States based Professional Services organisation with 88000 employees and revenues of $15.41 billion, Regions Bank, a United States based Banking and Financial Services organisation with 19969 employees and revenues of $7.53 billion, Safra Bank Brazil, a Brazil based Banking and Financial Services organisation with 30000 employees and revenues of $4.00 billion, Bank Danamon, a Indonesia based Banking and Financial Services organisation with 22794 employees and revenues of $1.14 billion and many others.
Contact us if you need a completed and verified list of companies using Cloudera Data Warehouse, 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 software purchases.
The Cloudera Data Warehouse 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 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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Bank Danamon | Banking and Financial Services | 22794 | $1.1B | Indonesia | Cloudera | Cloudera Data Warehouse | Data Warehouse | 2020 | n/a |
In 2020, Bank Danamon implemented Cloudera Data Warehouse as part of a broader modern data platform to consolidate customer data and enable real-time analytics. The deployment targeted Data Warehouse workloads and was positioned to support enterprise customer marketing, fraud detection, and anti-money laundering functions across the bank.
The implementation incorporated Cloudera Data Warehouse alongside core Cloudera components such as Apache HBase, Apache Impala, Apache Kafka, Apache Sentry, Apache Spark, and Cloudera Navigator to provide storage, query, streaming, security, and governance capabilities. The bank paired the platform with the Kogentix Automated Machine Learning Platform to test, train, validate, and monitor machine learning models, enabling descriptive through prescriptive analytic workflows and ongoing model performance analysis.
Operationally the platform ingests and analyzes more than one terabyte of structured and unstructured data daily, both in batch and via live streaming, integrating sources from about 50 different systems. Data sources include transactional systems, product systems, internet and mobile banking logs, credit card feeds, customer care voice, digital logs, social media, socioeconomic inputs, and third-party data, supporting real-time recommendation engines, business intelligence, fraud detection, and AML applications.
Governance and process changes emphasize model lifecycle management and real-time operationalization, with capabilities to observe interaction performance, self-correct based on feedback, and send real-time alerts to customers for potential fraud. The solution also addressed data protection and compliance management through centralized metadata and governance provided by Cloudera Navigator and security controls enforced by Apache Sentry.
Explicit results reported by Bank Danamon from the Cloudera Data Warehouse implementation include a greater than 300 percent increase in marketing conversion rates, improved customer retention, a 30 percent reduction in the number of fraud incidents, reduced marketing costs, identification of new fraud patterns, and a lower capital expenditure per terabyte compared to traditional data management mechanisms.
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Iqvia | Professional Services | 88000 | $15.4B | United States | Cloudera | Cloudera Data Warehouse | Data Warehouse | 2020 | n/a |
In 2020, IQVIA implemented Cloudera Data Warehouse as a Data Warehouse solution to consolidate and analyze its global life sciences datasets. IQVIA is a global provider of analytics and contract research services to the life sciences industry, and the deployment was driven by the need to bring analytics to large, distributed data holdings for faster discovery and operational decision making.
The Cloudera Data Warehouse implementation uses core Cloudera components including Apache Kudu, Apache Impala, and Apache Spark, alongside Cloudera Data Science Workbench and Cloudera Navigator to enable self-service BI, data science, and data engineering workflows. The platform supports development of predictive algorithms using R, Python, and Scala, and enables users to run interactive, high performance queries and machine learning model development without moving data out of the platform.
Operationally the initiative created a global multi-tenant data lake, consolidating more than two petabytes of data from roughly 250 source data warehouses including Oracle, Netezza, and Teradata systems. IQVIA partitioned the platform into four data tenants, a U.S. data lake, a Spain data lake, a France data lake, and a Japan data lake, and 70 internal teams with approximately 1,500 to 2,000 users access the environment. The platform ingests diverse data sources such as prescription data, electronic medical records, claims, sales, social data, and genomic data, and the environment has supported tens of hundreds of thousands of queries using BI tools such as Tableau and MicroStrategy.
Governance and security were implemented as a shared data experience with centralized encryption, governance controls, and role based access management, enabling global administrative oversight while preserving tenant isolation. IQVIA also architected the environment for a hybrid cloud future, using Cloudera Director to provision sister tenants in public cloud environments when a meeting place for client data and IQVIA data is required.
The deployment produced explicit operational outcomes documented by IQVIA, including accelerating query responses from days to seconds and improving the ability to predict if a patient is eligible for a clinical trial before they are symptomatic by four times. The platform also reduced the time to identify qualified clinical trial participants from weeks or months to seconds and minutes, enabling faster research cycles and more rapid development of new treatments.
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Regions Bank | Banking and Financial Services | 19969 | $7.5B | United States | Cloudera | Cloudera Data Warehouse | Data Warehouse | 2020 | n/a |
In 2020, Regions Bank implemented Cloudera Data Warehouse as part of a broader enterprise data science platform to centralize analytics for banking and wealth management use cases. The deployment addressed the need to unify fragmented data sources and enable scalable modelOps and data product delivery for corporate relationship managers, commercial teams, private wealth advisors, fraud detection teams, and centralized analytics groups across the banks US branch and ATM network.
The implementation used Cloudera Data Platform components including Cloudera Data Warehouse, CDP Private Cloud Base, Cloudera Data Science Workbench, Cloudera SDX, and Cloudera Stream Processing and Analytics to create a hybrid cloud capable data lake and analytic stack. Regions configured real-time ingestion pipelines and streaming features, and built analytics workflows that leverage Spark for enrichment and processing, Hive and Impala for deep data analytics, and Cloudera Data Science Workbench for model development and exploration.
Integrations and operational architecture focused on streaming ingestion with Kafka, Spark processing for large transaction volumes, and governed analytic serving layers, with Cloudera Professional Services supporting the in-place CDP Private Cloud upgrade to minimize downtime. Regions also worked with IBM on advanced analytics methodologies, and designed the environment to support containerized isolated workloads and future cloud bursting for ad hoc data science compute.
Governance and process changes included centralizing a data lake, instituting a data governance framework under Cloudera SDX for consistent security and metadata management, and standing up an analytics Center of Excellence to standardize model deployment and lifecycle practices. The platform consolidated data assets into a single security model and a unified data pipeline, enabling broader reuse of data and models by business-facing teams.
Explicit results reported from the deployment include improved customer conversations enabled by data products, over $10 million per year in retention savings, a production ML risk scoring model that improved fraud capture by 95 percent, a 30 percent decrease in false positive alerts, a 50 percent reduction in average daily dollar losses, and more than 10 percent cloud cost savings as Regions shifts workloads to burstable hybrid cloud patterns.
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Banking and Financial Services | 30000 | $4.0B | Brazil | Cloudera | Cloudera Data Warehouse | Data Warehouse | 2021 | n/a |
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Government | 1500 | $180M | Brazil | Cloudera | Cloudera Data Warehouse | Data Warehouse | 2014 | n/a |
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Professional Services | 7822 | $567M | Brazil | Cloudera | Cloudera Data Warehouse | Data Warehouse | 2019 | n/a |
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Retail | 201413 | $44.4B | Australia | Cloudera | Cloudera Data Warehouse | Data Warehouse | 2015 | n/a |
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