Platform Overview

IOMETE is a state-of-the-art, fast, scalable, user-friendly Data Lakehouse Platform for AI and Analytics. It offers flexibility and can be deployed anywhere – on-premises, in the cloud, or in a hybrid environment.

A traditional data warehouse only handles structured data and uses proprietary formats. A data lakehouse, like IOMETE, supports structured, semi-structured, and unstructured data using open formats while still delivering warehouse-like query performance. You get the flexibility of a data lake with the speed and reliability of a warehouse, all in one platform.

Companies choose self-hosted platforms for three main reasons: full control over their data and where it lives, predictable costs without vendor markup, and the ability to meet strict compliance requirements. When you host it yourself, you're not locked into a vendor's pricing model or forced to send sensitive data to third-party systems.

Unlike SaaS platforms such as Snowflake or Databricks, IOMETE runs entirely on your infrastructure. This ensures data sovereignty, enables usage of your cloud discounts, and provides full control over security and deployment. It's built for teams who value flexibility, transparency, and ownership. In addition, IOMETE runs on on-premises, in your data centers, in your private cloud, your public cloud or all of the above (hybrid), whereas Snowflake and Databricks only run on the three main US public clouds (AWS, Azure and Google Cloud).

Yes, that's exactly what a lakehouse does. You can retire separate systems and manage everything in one place. Your structured business data, raw logs, ML training datasets, and IoT streams all live together. You get ACID transactions and SQL query performance on the same data you use for machine learning and analytics.

IOMETE works with all major BI tools using standard connectors. Power BI, Tableau, Looker, Qlik, and other tools connect via JDBC or ODBC. Apache Superset connects natively through Spark SQL. Your analysts can use their familiar tools while querying data stored in IOMETE's lakehouse.

Yes. DBT works seamlessly with IOMETE using the dbt-spark adapter. You can build transformation pipelines, test data quality, document your models, and follow analytics engineering best practices—all running against your Iceberg tables in a self-hosted environment.

Yes. Data scientists can connect Jupyter notebooks to IOMETE's Spark clusters and work with data using SQL, PySpark, or pandas. You can query tables interactively, build machine learning models, and share notebooks with colleagues—all while working with production data under proper governance.

You have multiple options depending on your team's skills and needs. Use SQL for simple transformations, write PySpark or Scala code for complex logic, integrate with orchestration tools like Airflow or Prefect, use dbt for analytics engineering, or leverage Spark Structured Streaming for real-time pipelines. IOMETE provides the foundation while you choose the right tools.

Migration usually happens in phases to reduce risk. First, export data to cloud storage or convert Hive tables to Iceberg format. Then migrate your data pipelines and jobs to run on IOMETE's Spark clusters. Update BI tools to connect to the new platform. Finally, run both systems in parallel while validating results before decommissioning the old system. The timeline depends on data volume and pipeline complexity.

es, and many organizations do this during migration. Start with one team or use case—maybe your data science group or a single department. IOMETE can read from your existing data lake or warehouse, so you can gradually move workloads while keeping legacy systems running. This phased approach reduces risk and lets you validate everything before committing fully.