DataGOL Documentation Hub
DataGOL is an AI-powered Business Agility platform which brings Data, AI, and Applications under one roof. Empowering every team with simple, affordable, and easy-to-use data tools that drive growth. The DataGOL platform eliminates complexity, integrates seamlessly with existing systems, and delivers quick, measurable results, enabling businesses to harness the power of data like large enterprises — without the need for big budgets or specialized skills.
This introduction page orients you to the entire doc set and suggests where to start, whether you are a data engineer wiring up sources, an analyst building dashboards, or a product team embedding AI‑powered insights in your app.
How the docs are organized
| Topic | What you will find | Why it matters |
|---|---|---|
| Getting Started | One‑page setup, quick tour, and your first workbook | Fast path from zero to an interactive, AI‑ready workspace. |
| DataGOL concepts | Key objects (Lakehouse, Workbooks, Lineage, Agents) and how they fit | Shared mental model before you dive deep. |
| Lakehouse | Connecting data sources, building Pipelines, managing tables and partitions | Central, governed store that feeds everything else |
| Playground | Ad‑hoc SQL + AI Copilot for query generation and optimization | Rapid exploration and SQL assistance without leaving the browser |
| AI Agents | Chart/BI agent, Data‑extraction agent, custom agents | Guided, persona‑based analysis that speeds up discovery |
| BI Analytics | Drag‑and‑drop charts, AI‑generated dashboards | Turn data into executive‑ready visuals in clicks |
| Data Lineage | Source, pipeline, and workbook‑level lineage views plus impact analysis | Trace every field end‑to‑end for trust, compliance, and blast‑radius checks |
| Machine Learning (ML) | Model training, prediction APIs, and MLOps hooks | Use the same data foundation for predictive workflows |
| Workspaces and Workbooks | Multi‑team tenancy, versioning, API access, export options | Structure projects and share results at scale |
| Best Practices | Opinionated guidance on security, performance, and governance | Avoid rookie mistakes and design for scale from day one |
| Release Notes | What’s new, changed, deprecated | Stay aligned with platform evolution |
Suggested first steps
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Read Getting Started topic. Set up your first Lakehouse, connect a data source, and publish a Workbook in under 15 minutes.
- 🔗 Connect: Gather data from any source.
- 🔄 ETL, Pipelines, and Orchestration: Easily orchestrate data ingestion, transformation, and data movement.
- Collaborate: Connect with your team with no hassle from Workspaces.
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Explore the Playground. Run a sample query and test the SQL Copilot.
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Generate a dashboard with AI. Pick the Business Executive persona and watch DataGOL auto‑build KPIs.
- 📊 Visualize: Create BI visualizations.
- 🤖 AI Personal Assistant Use AI as your personal assistant.
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Inspect Lineage. Open the Lineage tab in your workbook to see upstream sources and downstream consumers.
-
Review Best Practices before pushing to production.
Who should use these docs
- Business users - for workbooks, visualizer, dashboards and insights
- Data engineers – for connectors, pipelines, and API automation
- Analysts and domain experts – for Playground queries, BI visualizations, and AI Agents
- ML engineers – for ML module, feature sourcing, and model deployments
- Developers/ISVs – for embedding analytics via the Workbook REST API
- Governance and security teams – for data lineage, audit controls, and role‑based access
Every page has a Was this helpful? widget. Drop a note any time. Your input directly shapes the docs. Happy building.
The DataGOL Documentation Team
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