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The Data Lakehouse component represents IOMETE's core storage and processing architecture. It combines the flexibility and cost-effectiveness of data lakes with the performance and reliability of traditional data warehouses. Built on Apache Iceberg, it provides ACID transaction support and schema evolution capabilities, enabling organizations to store and process both structured and unstructured data efficiently. This modern architecture ensures data consistency while maintaining the ability to handle diverse data types and workloads. Spin up unlimited data lakehouses for specific workloads, teams or departments.

IOMETE's SQL Editor provides an interactive, web-based environment for data analysis and exploration. Data analysts can write, test, and execute queries directly in their browser, with features like auto-completion and syntax highlighting enhancing productivity. The editor supports collaborative query development with version control capabilities, allowing teams to work together effectively while maintaining a history of their work. Users can save and share queries, creating a library of reusable analytics assets.

The Spark Jobs component manages and orchestrates Apache Spark workloads across the platform. It enables organizations to schedule, monitor, and maintain complex data processing operations with automated resource management and job recovery capabilities. Teams can develop and deploy sophisticated ETL (Extract, Transform, Load) processes while maintaining operational efficiency through automated scaling and resource allocation.

Spark Connect enhances the platform's connectivity and stability by enabling remote Spark session management. This component facilitates seamless connections from business intelligence tools and notebooks while improving resource utilization. It supports multiple concurrent user sessions efficiently, ensuring consistent performance across various analytical workloads while maintaining system stability.

The Data Catalog serves as a central repository for metadata management and data discovery. It enables organizations to document their data assets, track lineage, and understand relationships between different data elements. The catalog simplifies data governance and compliance efforts by providing a comprehensive view of data assets and their usage across the organization. Users can easily search, understand, and access the data they need while maintaining proper governance controls.

IOMETE's Data Access Controls provide comprehensive security management capabilities at both row and column levels. This component integrates seamlessly with enterprise authentication systems while maintaining detailed audit logs of all data access. Organizations can implement fine-grained security policies that ensure regulatory compliance and data protection while enabling appropriate access for different user roles and responsibilities.

The ML Notebooks component provides an interactive environment specifically designed for data science and machine learning workflows. It supports development in Python, R, and Scala while integrating with popular machine learning frameworks. Data scientists can develop, test, and deploy models within a familiar notebook interface, with access to the full power of the underlying data platform. This enables end-to-end machine learning workflows from data preparation through model deployment and monitoring.