MariaDB Enterprise Analytics
MariaDB Enterprise Analytics: A SQL-first columnar database for distributed enterprise analytics. Ranked #21 of 26 in Columnar Databases by our editors (7.1/10); pricing: Pricing on request; best for enterprise columnar MPP analytics.
At a glance
- Editor score7.1 / 10
- PricingPricing on request
- Best forEnterprise columnar MPP analytics
- Deployment modelHybrid
- Facts checked20 Sep 2026
Where it wins
- Distributed MPP processing for large analytical workloads
- Standard SQL with columnar storage and compression
- Kafka, Spark, S3-compatible storage, and language connectors
Where it doesn't
- Pricing is quote-based rather than published
- Advanced clustering requires higher-tier plans
- Enterprise features may exceed simpler analytics needs
Our verdict on MariaDB Enterprise Analytics
MariaDB Enterprise Analytics is a columnar database solution for real-time and ad hoc analytics. It is designed for organizations running large analytical workloads on premises, in private clouds, or in public clouds. MariaDB ColumnStore provides columnar storage, while massively parallel processing supports distributed analytics. Interactive queries use standard SQL, making the product a fit for teams that want columnar performance without moving away from familiar SQL workflows.
The product’s strongest fit is an environment that needs more than a standalone analytical store. It supports S3-compatible object storage, Apache Kafka streaming ingestion, and an Apache Spark connector, with bulk import connectors for C, Java, and Python. Supported data formats include CSV, TSV, JSON, and Avro. This combination connects the database to streaming, object-storage, data-processing, and application workflows, while hybrid deployment supports on-premises and cloud architectures. Columnar compression is also included for analytical storage efficiency.
Pricing is organized into three quote-based Enterprise Platform tiers. Enterprise Platform provides single-node analytics and 24×7 expert support. Enterprise Platform Plus adds synchronous clustering, RAFT-based clustering, and a Kubernetes Operator. Enterprise Platform Ultimate adds agentic AI and RAG capabilities plus geo-distributed deployment. The tier structure gives larger or more distributed environments a path to additional operational features, but organizations looking for transparent self-serve pricing may prefer another product. MariaDB Enterprise Analytics is a strong choice for enterprise teams needing SQL-based columnar MPP analytics with streaming and object-storage integrations; it is less suitable for small deployments seeking a narrowly scoped, simple analytics database.
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MariaDB Enterprise Analytics pricing
All 3 MariaDB Enterprise Analytics plans and prices →
MariaDB Enterprise Analytics fact sheet
| Free plan | Not verified |
|---|---|
| Paid from | Not verified |
| Deployment model | Hybrid |
| Managed service | Not verified |
| Object storage support | Yes |
| Streaming ingestion | Yes |
| Supported data formats | CSV, TSV, JSON, Avro |
| Included storage | Not verified |
| Deployment | Cloud, Self-hosted |
| Support | Tickets, Phone, Live chat, Docs · 24/7 |
| Built for | Mid-market, Enterprise (editorial estimate) |
| Integrations | 6 integrations: Apache Kafka, Apache Spark, Amazon S3-compatible object storage, C, Java, Python |
| Pricing | Pricing on request (source) |
| Website | mariadb.com |
| Facts checked | 20 Sep 2026 |
MariaDB Enterprise Analytics integrations
MariaDB Enterprise Analytics lists 6 integrations on its own site.
- Apache Kafka
- Apache Spark
- Amazon S3-compatible object storage
- C
- Java
- Python
Alternatives to MariaDB Enterprise Analytics
- Google BigQueryA flexible serverless warehouse for teams combining lakehouse analytics, streaming, and ML.9.4
- Amazon RedshiftA flexible AWS warehouse for analytics, data lakes, streaming, and SQL-based ML.9.2
- ClickHouse CloudA managed ClickHouse service for elastic, high-performance analytical workloads.9.0
See all MariaDB Enterprise Analytics alternatives →
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Reviewed by iTechGuides Editors · Editorial team · Updated Oct 2026
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