# Datazone

## Docs

- [Introduction](https://docs.datazone.co/introduction.md): Build, manage, and serve your data pipelines and intelligent applications with ease using Datazone in minutes.
- [Key Concepts](https://docs.datazone.co/key-concepts.md): Datazone is a modern data platform that simplifies your data engineering journey by providing a unified environment for data ingestion, processing, analysis, and AI-driven automation. It seamlessly connects your data sources to a robust data lakehouse while offering powerful tools for transformation…
- [Installation](https://docs.datazone.co/installation.md): Before starting to use Datazone, you need to install the Datazone Python CLI. The client is a Python package that provides a simple interface to interact with the Datazone API.
- [From Zero to Production!](https://docs.datazone.co/from-zero-to-production.md): In this guide, you will learn how to build a Data Lakehouse from scratch using Datazone, including creating intelligent apps, deploying AI agents, and exposing data via secure endpoints.
- [Data Ingestion](https://docs.datazone.co/reference/sources/overview.md): Data ingestion is the first step in Datazone's data journey.
- [MySQL](https://docs.datazone.co/reference/sources/mysql.md): MySQL is an open-source relational database management system (RDBMS).
- [Microsoft SQL Server](https://docs.datazone.co/reference/sources/mssqlserver.md): Microsoft SQL Server is a relational database management system developed by Microsoft.
- [Oracle](https://docs.datazone.co/reference/sources/oracle.md): Oracle is a powerful enterprise-grade relational database management system.
- [SAP HANA](https://docs.datazone.co/reference/sources/sap-hana.md): SAP HANA is an in-memory, column-oriented, relational database management system.
- [PostgreSQL](https://docs.datazone.co/reference/sources/postgresql.md): PostgreSQL is a powerful, open source object-relational database system.
- [MongoDB](https://docs.datazone.co/reference/sources/mongodb.md): MongoDB is a popular NoSQL database that stores data in flexible, JSON-like documents.
- [AWS S3 CSV](https://docs.datazone.co/reference/sources/aws-s3.md): AWS S3 is a scalable object storage service that can be used to store and retrieve files.
- [Azure Blob Storage](https://docs.datazone.co/reference/sources/azure-blob.md): Azure Blob Storage is Microsoft's object storage solution for the cloud, designed to store massive amounts of unstructured data.
- [Google BigQuery](https://docs.datazone.co/reference/sources/bigquery.md): Connect to Google BigQuery for large-scale analytics data extraction.
- [SAP ERP](https://docs.datazone.co/reference/sources/sap-erp.md): Connect to SAP ERP systems via the Datazone CloudFeed SAP Connector.
- [SAP S/4HANA](https://docs.datazone.co/reference/sources/sap-s4hana.md): Connect to SAP S/4HANA systems via the Datazone CloudFeed SAP Connector.
- [Command Line](https://docs.datazone.co/reference/development/command-line.md): One of the interaction way with Datazone is using Command Line Interface. You can manage your projects, datasets, and models with Datazone CLI commands.
- [Project Repository](https://docs.datazone.co/reference/development/project.md): Organize and deploy your pipelines, actions, apps, and endpoints in a single project structure.
- [Pipeline](https://docs.datazone.co/reference/development/pipeline.md): Define data processing steps using transforms and dependencies to build workflows
- [Transform](https://docs.datazone.co/reference/development/transform.md): Transform functions are the bricks of your pipeline. You can atomize your data processing steps into small functions and chain them together to build a pipeline.
- [Context](https://docs.datazone.co/reference/development/context.md): Each pipeline in Datazone has a context object that provides access to resources and configuration settings.
- [Variables](https://docs.datazone.co/reference/development/variables.md): Variables are used to store and manage data in the Datazone platform. You can define variables in the Datazone dashboard and use them in your pipelines.
- [Actions](https://docs.datazone.co/reference/development/actions.md): Deploy serverless Python functions to automate workflows and extend agent capabilities
- [Vectors](https://docs.datazone.co/reference/development/vectors.md): Transform your data into searchable embeddings for AI-powered applications
- [API Keys](https://docs.datazone.co/reference/development/api-key.md): API keys are used to authenticate requests to the Datazone API. You can create and manage your API keys from the Datazone dashboard.
- [Access Keys](https://docs.datazone.co/reference/development/access-keys.md): Create and manage access keys for programmatic access to your project resources
- [File Container](https://docs.datazone.co/reference/development/file-container.md): File Container is a storage solution in the Datazone platform that allows you to manage and store files. You can create, update, and delete file containers, and use them to store data for your pipelines and notebooks.
- [Model Accounts](https://docs.datazone.co/reference/development/model-accounts.md): Configure and manage AI model provider credentials for building intelligent applications with Datazone
- [Policy](https://docs.datazone.co/reference/development/policy.md): Role-based access control with hierarchical permissions for fine-grained authorization
- [Overview](https://docs.datazone.co/reference/integration/overview.md): Overview of Datazone Integration and API capabilities
- [API Key Authentication](https://docs.datazone.co/reference/integration/authentication/api-key.md): Learn how to authenticate with Datazone using API keys
- [SAML Authentication](https://docs.datazone.co/reference/integration/authentication/saml/overview.md): Configure SAML-based single sign-on for your Datazone instance
- [Azure AD (Entra ID) SAML Setup](https://docs.datazone.co/reference/integration/authentication/saml/azure-ad.md): Configure SAML authentication with Microsoft Azure Active Directory
- [Okta SAML Setup](https://docs.datazone.co/reference/integration/authentication/saml/okta.md): Configure SAML authentication with Okta
- [Google Workspace SAML Setup](https://docs.datazone.co/reference/integration/authentication/saml/google-workspace.md): Configure SAML authentication with Google Workspace
- [ODBC/JDBC Connections](https://docs.datazone.co/reference/integration/odbc-jdbc-connection.md): Connect to Datazone using Clickhouse ODBC or JDBC drivers
- [Views](https://docs.datazone.co/reference/integration/views.md): Create optimized relational database views from your datasets with advanced partitioning and indexing
- [Endpoints](https://docs.datazone.co/reference/integration/endpoints.md): Create custom API endpoints for secure data access
- [Notebooks](https://docs.datazone.co/reference/analysis/notebook.md): Notebooks are the interactive documents that you can write and run your code in Datazone.
- [Kernels](https://docs.datazone.co/reference/analysis/kernel.md): Kernels are the execution environments for your Datazone notebooks.
- [Toolkit](https://docs.datazone.co/reference/analysis/toolkit.md): The Toolkit is a collection of tools and utilities that help you manage your notebooks in Datazone.
- [Overview](https://docs.datazone.co/reference/intelligent-apps/overview.md): Build interactive data applications with Datazone's Intelligent Apps feature
- [Components](https://docs.datazone.co/reference/intelligent-apps/components.md): Detailed reference for components in Datazone Intelligent Apps
- [Filters](https://docs.datazone.co/reference/intelligent-apps/filters.md): How to use filters for interactivity in Datazone Intelligent Apps.
- [Variable Usage](https://docs.datazone.co/reference/intelligent-apps/query-manipulation.md): Examples of using variables and Jinja-style templating in Intelligent App queries.
- [Orion AI (LLM Assistant)](https://docs.datazone.co/reference/intelligent-apps/orion-ai.md): Overview of Orion AI, the LLM-powered assistant for Intelligent Apps.
- [YAML Reference](https://docs.datazone.co/reference/intelligent-apps/yaml-reference.md): Comprehensive reference for all YAML attributes in Intelligent App definitions.
- [Embedding](https://docs.datazone.co/reference/intelligent-apps/embedding.md): You can build a Data Intensive Application in a couple of minutes with Datazone.
- [Overview](https://docs.datazone.co/reference/knowledge-objects/overview.md): Define business entities as versioned objects and manage their instances through a governed API
- [YAML Reference](https://docs.datazone.co/reference/knowledge-objects/yaml-reference.md): Comprehensive reference for every attribute in a Knowledge Object definition.
- [Instance API](https://docs.datazone.co/reference/knowledge-objects/api.md): REST API for managing Knowledge Objects and their instances.
- [Actions SDK](https://docs.datazone.co/reference/knowledge-objects/actions-sdk.md): Read and write Knowledge Object instances from within an action.
- [Overview](https://docs.datazone.co/reference/flows/overview.md): Build orchestration graphs that call LLMs, REST APIs, and Actions with a declarative YAML flow
- [Node Reference](https://docs.datazone.co/reference/flows/nodes.md): Detailed reference for every node type available in Datazone Flows
- [Triggers & Runs](https://docs.datazone.co/reference/flows/triggers-and-runs.md): How Datazone Flows start, and how to read the status of a run.
- [Templating & Expressions](https://docs.datazone.co/reference/flows/templating.md): How Flow nodes reference upstream data and run parameters.
- [YAML Reference](https://docs.datazone.co/reference/flows/yaml-reference.md): Comprehensive reference for every attribute in a Flow document.
- [Overview](https://docs.datazone.co/reference/agents/overview.md): Create custom AI agents to interact with your data through natural language
- [Chat Interface](https://docs.datazone.co/reference/agents/chat.md): Interact with your agents through conversational AI
- [Embedding](https://docs.datazone.co/reference/agents/embedding.md): Embed your agents into any website, app, or third-party tool with a single line of code
- [Best Practices](https://docs.datazone.co/reference/agents/best-practices.md): Optimize your agents for better performance, accuracy, and cost efficiency
- [Channels](https://docs.datazone.co/reference/platform/channels.md): Configure notification channels for sending reports and alerts
- [Reports](https://docs.datazone.co/reference/platform/reports.md): Schedule and automate intelligent app report delivery
- [Resources Overview](https://docs.datazone.co/reference/platform/resources/overview.md): Understanding Datazone resource types and their usage
- [Quotas](https://docs.datazone.co/reference/platform/resources/quotas.md): Set limits and manage resource consumption with quotas
- [UI Overview](https://docs.datazone.co/reference/ui/overview.md): Navigate Datazone's main interface sections
- [SQL Explorer](https://docs.datazone.co/reference/ui/sql-explorer.md): A complete SQL workspace inside Datazone — write, run, save and revisit your queries without ever losing your place
- [Change Logs](https://docs.datazone.co/changelog.md): Release notes and feature updates for Datazone
- [Public Cloud](https://docs.datazone.co/deployment/public-cloud.md): Datazone public cloud deployment for community and pro users
- [Private Cloud](https://docs.datazone.co/deployment/private-cloud.md): Enterprise deployment of Datazone in your cloud environment
- [Datazone SDK](https://docs.datazone.co/tutorial/datazone-sdk.md): Learn how to use Datazone SDK to access and manage your data in Datazone in your local environment.
- [Building an AI-Powered Customer Response Automation System with Datazone](https://docs.datazone.co/tutorial/examples/ai-powered-message-automation.md): We built this cool email response system using Datazone, and its handling customer support emails like a champ! 🚀
- [Pyspark Examples in Datazone Transforms](https://docs.datazone.co/tutorial/examples/pyspark-transform-examples.md)
- [Pipeline Examples](https://docs.datazone.co/tutorial/examples/pipeline-examples.md): Learn how to create and manage data pipelines in Datazone through practical examples

## OpenAPI Specs

- [openapi](https://docs.datazone.co/api-reference/openapi.json)

## Optional

- [Blog](https://www.datazone.co/blog)