HPE Ezmeral Data Fabric
Multimodal data platform for enterprise hybrid deployments
At a glance
- Editor scoreNot yet scored
- PricingPricing on request
- Best forEnterprise hybrid deployments needing strong consistency
- Deployment modelBoth
- Facts checked20 Sep 2026
Where it wins
- HBase and OJAI APIs support column and JSON document tables
- ACID transactions, strong consistency, replication, indexes, and CDC
- Connectors for Spark, Drill, Iceberg, and major cloud platforms
Where it doesn't
- Pricing requires contact with an HPE sales representative
- Broad platform scope may exceed the needs of narrow database projects
- Customer-managed and managed deployments require different planning
Our verdict on HPE Ezmeral Data Fabric
HPE Ezmeral Data Fabric is a multimodal data-storage platform for enterprise data teams that need database, file, object, and streaming services through one logical view and global namespace. Its database capabilities cover column-oriented tables through the HBase API and JSON tables through the OJAI API. REST, client-library, Apache Drill, and Apache Spark access give teams multiple ways to work with the data. With web, Linux, API, and self-hosted access, it fits organizations planning hybrid deployments across cloud and on-premises environments.
The database feature set is broad for workloads that require consistency and operational controls. Row- and document-level ACID transactions, strong data consistency, multi-master table replication, secondary indexes, and change data capture address transactional and distributed data requirements. Apache Drill provides distributed querying, while Apache Spark connectors support data processing workflows. Unified security includes authentication, authorization, encryption, and auditing. These capabilities make the product a fit for teams that need more than a basic wide-column store, particularly when HBase compatibility and JSON document access belong in the same platform.
Deployment and commercial planning are central considerations. HPE documents managed-as-a-service deployments alongside customer-managed cloud and on-premises installations, with integrations for AWS, Microsoft Azure, Google Cloud Platform, S3-compatible object storage, Iceberg, and the REST API. Pricing is consumption-based and available through an HPE sales representative, so buyers should involve procurement and architecture stakeholders early. Choose HPE Ezmeral Data Fabric when strong consistency, transactions, replication, hybrid deployment, and broad data-service coverage matter together. A narrower database project may be better served by a product with a simpler deployment and purchasing process.
HPE Ezmeral Data Fabric pricing
HPE Ezmeral Data Fabric fact sheet
| Free plan | Not verified |
|---|---|
| Paid from | Not verified |
| Deployment model | Both |
| Query interface | Multiple |
| Consistency controls | Strong |
| Multi-region support | Yes |
| Transaction support | Yes |
| Maximum row size | 32 MB |
| Horizontal scaling | Yes |
| Deployment | Cloud, Self-hosted |
| Platforms | Web, Linux |
| Support | Community, Docs |
| Built for | Enterprise, Mid-market (editorial estimate) |
| Integrations | 9 integrations: Apache HBase, Apache Drill, Apache Spark, Iceberg, AWS, Microsoft Azure … |
| Pricing | Pricing on request |
| Website | developer.hpe.com |
| Facts checked | 20 Sep 2026 |
HPE Ezmeral Data Fabric integrations
HPE Ezmeral Data Fabric lists 9 integrations on its own site.
- Apache HBase
- Apache Drill
- Apache Spark
- Iceberg
- AWS
- Microsoft Azure
- Google Cloud Platform
- S3-compatible object storage
- REST API
Alternatives to HPE Ezmeral Data Fabric
- Apache CassandraA mature self-hosted Cassandra-compatible database for distributed, high-volume workloads.—
- Google Cloud BigtableA scalable managed database for wide-column workloads with deep Google Cloud integration.4.6
- Azure Cosmos DB for Apache CassandraA managed Cassandra option for globally distributed workloads with CQL compatibility.8.8
See all HPE Ezmeral Data Fabric alternatives →
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Reviewed by iTechGuides Editors · Editorial team · Updated Sep 2026
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