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DirectoryData & AnalyticsData Warehouse & LakehouseMySQL

MySQL

About MySQL

It is built on MySQL Enterprise Edition and includes ACID compliance, high concurrency, and security features. The service can be deployed across multiple public clouds. It allows querying of data in object storage, MySQL databases, or both.

Used for

  • Getting real-time marketing analytics
  • Scaling startups while reducing costs
  • Delivering fintech solutions
  • Gaining a competitive edge for ISV applications
  • Building, training, and explaining ML models

Who it is for

  • Digital marketing agencies
  • Fintech startups
  • ISVs delivering SaaS applications

From MySQL's own website, read 2026-10-24. 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 MySQL. 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.oracle.com/mysql Pricing page
CategoryData & Analytics
SubcategoryData Warehouse & Lakehouse
DeploymentPublic cloud (OCI, AWS, Azure)

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Keep reading

Buyer Guide
Cloud Data Warehouse
Job 02
Govern the AI already in use

Staff already use AI tools nobody approved, and most SaaS products now ship AI features by default.

Job 06
Secure and comply

AI agents act on systems with their own identities, and auditors and boards ask about them.