IOMETE provides a self-hosted data lakehouse platform designed for the demands of the AI era. This platform supports hybrid deployment across on-premises, private, and public clouds, ensuring customers maintain data sovereignty and control. It integrates data warehousing, engineering, and real-time analytics capabilities while offering cost efficiency and robust data governance features.
Funding
$2.2M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
CVECFFTFFounders
Product
Problem
Organizations face challenges in managing and analyzing large-scale data across diverse environments due to the complexity and cost of existing data lakehouse solutions. Traditional SaaS offerings often lead to vendor lock-in, unpredictable pricing, and concerns around data ownership and compliance. This makes it difficult for enterprises to maintain control over their data infrastructure while ensuring security and regulatory adherence.
Solution
IOMETE provides a self-hosted data lakehouse platform powered by Apache Iceberg and Apache Spark, enabling organizations to securely store, process, and analyze data across on-premises, hybrid, and cloud environments. The platform offers a transparent pricing model without vendor lock-in, providing ACID transactions, real-time streaming, and seamless integration with BI and orchestration tools. By deploying IOMETE within their own infrastructure, organizations maintain complete data ownership and ensure compliance with regulations such as SOC 2, HIPAA, and GDPR. IOMETE simplifies data management, allowing users to sync, prepare, and consume unified data for analytics, machine learning, and AI workloads.
Target Audience
IOMETE is designed for enterprise data teams, financial institutions, healthcare providers, and public sector organizations that require a secure, scalable, and cost-effective data lakehouse solution with full control over their data.
Features
- Self-hosted deployment on-premises, in private clouds, or in hybrid configurations, ensuring data never leaves the trust perimeter
- Apache Iceberg table format support, providing ACID transactions, time travel, snapshots, schema evolution, and data versioning
- Apache Spark engine for fast, versatile, and scalable data processing
- Built-in SQL editor with features like search, autocomplete, and schema explorer for ad-hoc analysis
- Data catalog for Google-like search, indexing, and discovery of data assets
- Advanced data access controls for managing access at the person, team, table, row, and column levels
- Seamless integration with BI tools like Tableau, PowerBI, Metabase, and Apache Superset
- Serverless Spark jobs for syncing data from operational databases and other sources
- Query federation to access object storage, relational databases, and No-SQL systems with the same SQL
- Integration with DBT for data transformations