Getting Started

What is NATIS Data Platform?

An introduction to NATIS Data Platform — an enterprise-grade data platform for data ingestion, processing, governance, quality, analytics, and AI-ready data operations.

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NATIS Data Platform, also known as NDP, is an enterprise-grade data platform designed to help organizations manage the end-to-end data lifecycle within a unified ecosystem.

Core Capabilities

NATIS Data Platform provides a unified set of capabilities for building, governing, and operating enterprise data platforms.

• Sources & Connections — Define reusable connections to upstream systems such as databases, files, APIs, and enterprise data sources.
• Data Ingestion — Ingest data using batch, streaming, and Change Data Capture patterns to support different data freshness and synchronization requirements and support connectors to 200+ data sources
• Workflow Management — Coordinate ingestion, transformation, validation, and delivery tasks through scheduled, event-driven, or manually triggered workflows.
• Scalable Data Processing — Process and transform data using scalable execution engines designed for batch and streaming workloads.
• Metadata Management — Manage data catalog, lineage, ownership, classification, glossary, and metadata ingestion across the platform.
• Data Quality — Define test cases, group them into test suites, run validation, monitor results, and manage data quality incidents.
• Security & Access Control — Manage authentication, authorization, role-based access control, and data access policies across platform capabilities.
• Analytics & Integration — Provide governed data for downstream reporting, dashboards, BI tools, APIs, and analytical workloads.
• Business Intelligence — built-in dashboard and report builder powered by NATIS SQL Engine
• AI/ML Enablement — Prepare trusted and governed data assets for feature engineering, model experimentation, machine learning workflows, and AI applications.

Platform Architecture

NATIS Data Platform is designed as a unified data ecosystem where data capabilities are organized into functional domains and governed by cross-cutting control layers. Besides NDP can deployment is tailored to the customer environment, including cloud, on-premises, private cloud, Kubernetes-based, and hybrid architectures. At a high level, the architecture includes three primary layers:

Deployment options may vary by customer environment. NATIS can be designed for cloud, on-premises, private cloud, Kubernetes-based, or hybrid deployment models depending on enterprise architecture and infrastructure requirements.

• Functional Capability Layer — This layer contains the core platform capabilities, including data ingestion, storage, workflow management, machine learning, business intelligence, and data sharing.
• Control & Governance Layer — This layer ensures that data operations are governed, secured, monitored, and validated consistently across the platform. It includes data governance, data security, data quality, and monitoring.
• Execution & Coordination Layer — This layer coordinates workflows, jobs, dependencies, and execution logic across the platform. Orchestration acts as the backbone that ensures data operations are executed in a controlled, repeatable, and reliable manner.

Who Uses NATIS?

NATIS Data Platform is designed for cross-functional enterprise data teams. Different users interact with the platform based on their responsibilities across data engineering, platform operations, analytics, machine learning, governance, and administration.

• Data Engineers: NATIS Data Platform is designed for cross-functional enterprise data teams. Different users interact with the platform based on their responsibilities across data engineering, platform operations, analytics, machine learning, governance, and administration.
• Platform Operators: Manage system health, resource allocation, monitoring, logging, scaling, high availability, and platform operations.
• Data Analysts: Use governed data assets for reporting, dashboards, SQL analytics, business intelligence, and trusted decision-making.
• Data Scientists: Use trusted and governed data assets for feature engineering, model experimentation, machine learning workflows, and AI applications.
• Administrators: Manage users, roles, permissions, external identity integration, system configuration, and platform-level access control.
• Data Leaders and Architects: Design enterprise data strategy, lakehouse architecture, governance models, data operating models, and AI-ready data foundations.