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DirectoryData & AnalyticsData Warehouse & LakehouseQubole

Qubole

About Qubole

Qubole offers a data lake platform that lets organizations use multiple data engines together in one platform. The platform is described as simple, open, and secure, intended for machine learning, streaming, and ad-hoc analytics. It provides end-to-end services covering data pipelines, streaming analytics, and machine learning workloads on any cloud. Qubole is operated as an Idera Inc company. The platform provides cloud infrastructure management, data management, continuous data engineering, analytics, and machine learning services.

Products

  • Qubole Workbench: Used to author, save, template and share reports and queries.
  • Assisted Pipeline Builder: Used to build streaming data pipelines combining multiple streaming or batch data sources.
  • Qubole Scheduler: Used to create and manage automated ingestion pipelines.
  • SparkLens: A Spark tuning tool used to configure and improve the performance of data science jobs on Spark.
  • Qubole Cost Explorer: Used to manage total cost of ownership.

Used for

  • Building data pipelines
  • Bringing machine learning models to production
  • Analyzing data from any data source
  • Collecting and processing stateful events
  • Scheduling and monitoring end-to-end data science workflows

Who it is for

  • The product is designed for data scientists, data engineers, data analysts and administrators.

From Qubole's own website, read 2026-10-23. How we build listings.

How to evaluate Data Warehouse & Lakehouse

This is how the CIOPages Research Team evaluates this category. It is not an assessment of Qubole. The category covers two kinds of product, so both frameworks are here. Most buyers 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

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Quick Facts

www.qubole.com Pricing page
CategoryData & Analytics
SubcategoryData Warehouse & Lakehouse
DeploymentMulti-cloud

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Keep reading

Buyer Guide
Cloud Data Warehouse
Job 06
Secure and comply

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Toolkits
Frameworks and templates

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