List of Apache Kylin Customers
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Since 2010, our global team of researchers has been studying Apache Kylin 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 Apache Kylin 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 Apache Kylin for Data Warehouse include: JD.Com, a China based Retail organisation with 570895 employees and revenues of $161.56 billion, eBay, a United States based Retail organisation with 11500 employees and revenues of $2.60 billion, Beijing Jinhua Dayi Building Materials Co., a China based Distribution organisation with 10 employees and revenues of $1.0 million and many others.
Contact us if you need a completed and verified list of companies using Apache Kylin, 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 Apache Kylin 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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Beijing Jinhua Dayi Building Materials Co. | Distribution | 10 | $1M | China | Apache Software | Apache Kylin | Data Warehouse | 2015 | n/a |
In 2015, Beijing Jinhua Dayi Building Materials Co. deployed Apache Kylin for OLAP scenarios including traffic analysis and payment channel analysis to support multi-product line analytics. The deployment in China targeted product analytics and payments use cases, positioning Apache Kylin as the analytical acceleration layer for interactive BI workloads.
Implementation leveraged Apache Kylin to provide cube-based OLAP acceleration and a SQL query surface for large-scale analytical queries, with configurations focused on pre-aggregation, query caching, and concurrency controls to support interactive exploration. Functional capabilities implemented included multi-dimensional cube processing, sub-second query serving for high-cardinality datasets, and support for simultaneous analytical queries from product analytics teams.
Operational scope covered product analytics and payment channel analysis across the company, with the Apache Kylin deployment handling billion-record datasets to unify multi-product line analytics. The implementation emphasized serving BI and product analytics use cases with stable, high-performance access to aggregated telemetry and payment data, improving data platform responsiveness for analytics consumers.
Governance and rollout centered on enabling analytics teams to consume pre-aggregated cubes and SQL queries for self-service analysis, while operational monitoring focused on query latency and cube refresh cycles. Apache Kylin delivered stable, high-performance BI with sub-second query latency on billion-record datasets, directly improving responsiveness for product analytics teams.
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eBay | Retail | 11500 | $2.6B | United States | Apache Software | Apache Kylin | Data Warehouse | 2014 | n/a |
In 2014, eBay implemented Apache Kylin to provide a high-performance analytical layer for BI and reporting. Apps Category
The deployment was used across behavior analytics, traffic reporting and streaming dashboards, with Apache Kylin configured to accelerate interactive queries on very large datasets. The implementation incorporated OLAP-style pre-aggregation and cube building to support sub-second query response and to serve both batch and near real time reporting workloads.
Operational coverage for the US deployment included Data Services and Solutions, Classifieds and SEO teams, and it supported product and search teams that consume behavior and traffic analysis. Integrations focused on feeding BI pipelines and streaming dashboard layers, providing consistent query semantics across reporting and analytics surfaces.
Governance centered on embedding Apache Kylin into existing reporting workflows and on aligning analytics teams around a single analytical engine, which reduced dashboard refresh latency and enabled sub-second query response on very large datasets, improving analytics performance for product and search teams.
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JD.Com | Retail | 570895 | $161.6B | China | Apache Software | Apache Kylin | Data Warehouse | 2015 | n/a |
In 2015, JD.Com deployed Apache Kylin to serve as a data analytics engine for JOS API access behavior and reporting within its JCloud environment, the Apps Category is . The deployment in China focused on platform analytics and API metrics, concentrating on multi-dimensional analysis of high-cardinality telemetry from JOS APIs. Apache Kylin was used to enable ad hoc querying and reporting on platform access patterns and API usage trends.
The implementation leveraged Apache Kylin’s multi-dimensional analysis capabilities and OLAP-style query processing to support analytics across tens of billions of records, with query latencies measured in seconds. Configuration centered on pre-aggregation and cube-based modeling to sustain extreme data growth in platform metrics, aligning storage and index strategies to optimize API metric queries. Apache Kylin served as the core analytics engine for platform-level reporting workloads and supported SQL-based access for analytics consumers.
Operational integration tied Apache Kylin into JD.Com’s JCloud telemetry and reporting pipelines to ingest JOS API logs and metric streams for downstream analysis, operating within the China-region JCloud environment. The scope of the implementation emphasized platform analytics and engineering teams responsible for API observability and reporting, with governance focused on metric schema, cube refresh cadence, and query access controls. The deployment addressed extreme scale by enabling second-scale query performance on multi-dimensional API metrics at tens of billions of rows.
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