Directory Data & AnalyticsData Warehouse & LakehouseKylin (Apache)

Kylin (Apache)

Open Source

About Kylin (Apache)

Apache Kylin is an open-source, high concurrency OLAP engine designed to deliver ultra-fast query performance through advanced pre-calculation technology. It enables enterprises to perform large-scale, multi-dimensional data analysis with sub-second query response times, supporting high concurrency while minimizing hardware and development costs. The platform is ideal for organizations seeking to optimize their data warehouse capabilities and accelerate analytics workflows.

Kylin integrates a native compute engine powered by Apache Gluten, doubling performance and providing flexible query analysis. It supports streaming-batch fusion analysis to reduce data latency to seconds or minutes, enhancing the accuracy and reliability of insights. The solution also offers a modern web UI that simplifies modeling by allowing users to define table relationships, dimensions, and measures on a single canvas. Its compatibility with popular BI tools such as Tableau, Power BI, and Excel makes it suitable for enterprises looking to leverage existing analytics ecosystems.

How to evaluate Data Warehouse & Lakehouse

CIOPages Research Team evaluation frameworks for this category — not an assessment of Kylin (Apache). This category covers two kinds of product, so both frameworks are shown; buyers usually need one of them.

Cloud Data Warehouse

25%

Query Performance & Concurrency

TPC-DS benchmarks, concurrent user support, query queuing, automatic scaling, sub-second response for dashboards

20%

Data Ingestion & Integration

Streaming ingestion (Kafka, Kinesis), batch loading, CDC support, native connectors, data sharing/marketplace

20%

Governance & Security

Column/row-level security, dynamic data masking, data lineage, access policies, audit logging, compliance certifications

15%

AI/ML Integration

Native ML runtimes (Python/Spark), feature store, vector search, LLM integration, model serving

10%

Cost Management

Compute/storage separation, auto-suspend, resource monitors, usage attribution, reserved capacity pricing

10%

Ecosystem & Tooling

BI tool compatibility, dbt/Airflow integration, Iceberg/Delta support, partner ecosystem, marketplace

Data Lakehouse Platforms

25%

Open Format & Catalog Interoperability

Native read AND write to Apache Iceberg and/or Delta Lake, Iceberg REST Catalog support, credential vending for external engines, cross-format bridges (Delta UniForm, Apache XTable), and whether grants and lineage travel with the table when another engine reads it

20%

Query Engine Performance & Concurrency

Vectorized execution (Photon, Arrow/Gandiva, native engines), caching and materialization (reflections, result cache, Warp Speed), high-concurrency BI behavior, autoscaling, and predictable performance on your own data — not vendor benchmarks

20%

Data Engineering, Streaming & AI/ML

Batch and streaming ingestion into open tables, incremental/CDC and upsert support, orchestration, ML lifecycle (feature store, training, model serving), notebook and Python/Spark depth, and native LLM/agentic and vector capabilities

15%

Governance, Security & Lineage

A unified catalog spanning tables, files, ML models and (increasingly) unstructured data; fine-grained RBAC/ABAC, row/column masking, data sharing, automated lineage, and consistent policy enforcement across every engine that touches the data

10%

Operational Simplicity & Table Maintenance

Automated compaction, clustering, snapshot expiry and orphan-file cleanup; serverless vs. cluster sizing; multi-cloud and hybrid/on-prem reach; admin and FinOps tooling; and how much table toil the team must own versus the platform absorbing it

10%

Cost Model & Consumption Control

Consumption unit (DBU, credit, capacity unit, bytes/slots), separation of storage and compute, idle-suspend and autoscaling guardrails, egress and cross-region exposure, workload isolation, and the FinOps tooling to attribute and cap spend

Other Data Warehouse & Lakehouse Vendors

Azure Synapse Analytics

Related Buyer Guides

Independent evaluation frameworks for this category.

AI/ML Platforms

Compare Databricks Mosaic AI, AWS SageMaker, Azure Machine Learning, Google Vertex AI, Snowflake Cortex, Dataiku, DataRobot, and Weights & Biases on the question this category actually turns on — getting governed models into production and keeping them healthy, not the accuracy of a one-off notebook.

Business Intelligence & Analytics

Evaluate Power BI, Tableau, Qlik, Looker, ThoughtSpot, Sigma, Amazon QuickSight, Strategy, SAP Analytics Cloud, and Domo on the question that decides BI value — whether self-service freedom and a governed semantic layer can coexist, not whose charts look best.

Cloud Data Warehouse

Enterprise evaluation framework for Cloud Data Warehouse platforms.

This profile was compiled by CIOPages from public sources with AI assistance, and may be incomplete or out of date. It is informational only and not an endorsement. Represent this vendor? Claim this listing or .