AI Agents for Data Analytics

The AI Agents for Data Analytics course is designed to help learners build practical skills in developing AI agents that interact with data platforms to support analytics workflows. Through a hands-on, project-based approach, you'll learn how to build a simple customer churn agent, connect it to BigQuery, and extend its capabilities using MCP (Model Context Protocol) and MCP Toolbox.

The course explores how AI agents can interact with different data platforms, including BigQuery, Databricks, and Snowflake. You'll learn how to integrate agents with data sources, understand the trade-offs between function-based tools and MCP, and apply secure credential management practices that prioritize governance and access control over relying solely on prompt-based instructions.

Through guided demonstrations and hands-on labs, you'll build and integrate an analytics agent with BigQuery, use MCP Toolbox to enable data access, explore Databricks Genie spaces, and connect agents to Snowflake through MCP. The course culminates in bringing these concepts together to understand how a single agent can interact with multiple data layers and support real-world data analytics use cases.

Course Modules & Learning Outcomes

Building Agent With BigQuery

Learn how to build a simple customer churn agent and integrate it with BigQuery for data-driven analysis. You'll explore the course prerequisites, connect an agent to BigQuery, and query data through agent interactions. You'll also understand the role of MCP, get introduced to MCP Toolbox, and integrate BigQuery using MCP to extend your agent's data access capabilities. Hands-on labs reinforce these concepts by having you build and connect a churn agent to BigQuery.

Building Agent With Databricks

Explore the integration of AI agents with Databricks and understand the trade-offs between function-based tools and MCP. Learn how to manage credentials securely through appropriate governance and access controls rather than relying on prompt-level restrictions. You'll also be introduced to Databricks Genie spaces and explore their role in enabling data analytics interactions through guided demonstrations.

Building Agent With Snowflake

Learn how to integrate AI agents with Snowflake using Snowflake MCP. Explore how a single agent can interact with multiple data layers and understand the considerations involved in connecting AI agents to different data platforms. Demonstrations will reinforce how these integrations can support flexible data analytics workflows.

Bringing It All Together

Connect the concepts covered throughout the course to understand how AI agents can work across BigQuery, Databricks, and Snowflake. Review the integration approaches, tool selection considerations, and data access patterns involved in building AI-powered analytics solutions.

Course Features

• Hands-on labs building a customer churn agent and integrating it with BigQuery.
• Practical experience connecting AI agents to data platforms using MCP and MCP Toolbox.
• Real-world integrations covering BigQuery, Databricks, and Snowflake.
• Guidance on function tools versus MCP and secure credential governance.
• Project-based learning focused on building AI-powered data analytics workflows.

Who Should Enroll?

• AI engineers and developers building AI-powered data analytics applications.
• Data engineers working with BigQuery, Databricks, or Snowflake.
• Cloud engineers integrating AI agents with data platforms.
• Data analysts interested in using AI agents to interact with and explore data.
• DevOps and platform engineers supporting AI agent integrations and access management.
• Professionals looking to understand MCP-based data access and agent-driven analytics.

Build practical skills in developing AI agents for data analytics and learn how to connect them securely to leading data platforms using BigQuery, Databricks, Snowflake, MCP, and MCP Toolbox.