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Mastering Agentic AI SQL for Intelligent Data Systems
Rating: 2.7 out of 5(9 ratings)
2,206 students

Mastering Agentic AI SQL for Intelligent Data Systems

Learn SQL for agentic AI, data pipelines, real-time apps, query optimization, feature engineering, and Postgres + AWS.
Created byAkhil Vydyula
Last updated 7/2025
English
English [Auto],

What you'll learn

  • Write optimized SQL queries to support agentic AI workflows, real-time pipelines, and data-intensive applications.
  • Integrate SQL with Gen AI tools like LangChain and ChatGPT to automate data-driven decision-making processes.
  • Design and implement ETL workflows using PySpark, SparkSQL, AWS (S3, Glue, EMR), and Postgres/Redshift.
  • Build intelligent data systems with feature engineering, prompt-based agents, and key-value transformation techniques.

Course content

2 sections • 17 lectures • 1h 52m total length

  • Introduction6:16

    Explore data definition, manipulation, and transaction control languages, and learn how ddl defines schema, constraints, and data types, while understanding referential integrity and table relationships for sql and bi workflows.

  • video28:33

    Explore core SQL concepts from DDL to DML, including create, alter, drop, and rename, plus insert and update operations, slowly changing dimension type two, and assertions and domain constraints.

  • video38:06

    Learn SQL basics for defining and modifying tables, focusing on alter table syntax, adding or dropping columns, renaming, with DDL and an intro to DML.

  • video44:55

    Cover data definition, manipulation, and transaction control languages, explain constraints and data types, illustrate referential integrity and normalization, one-to-many relationships, and authorization for Power BI-ready database design.

  • video55:10

    Explore read, insert, and update authorization in a MySQL database, discuss slowly changing dimensions, type two, and assertions, and review DDL commands like create, alter, drop, and rename.

  • video65:27

    Build and explore a sample database by creating a table with columns, a primary key, and varchar fields, practicing ddl, dml, one-to-many relationships, and joins.

  • video74:45

    Explore DDL concepts, including primary keys and not null constraints, and learn to alter the table by adding, modifying, dropping, and renaming columns in the courses table.

  • video83:36

    Master renaming in SQL using the rename syntax to change column or table names, and explore DML basics, nested queries, data type validation, and basic transaction concepts in SQL.

  • video96:12

    Explore the data manipulation language of sql, contrasting procedural and declarative approaches, and learn to retrieve, insert, update, and delete data using select and other commands.

  • video107:19

    Master select queries to view and calculate salaries, apply aliases, and create temporary columns, then practice insert, update, and delete on the employee and dependent tables with one-to-many relationships.

  • video116:27

    Practice sql data manipulation with insert, update, delete, and copy operations, using where clauses and employee or dependent IDs, including counts and between ranges.

Requirements

  • Basic understanding of SQL or any programming logic is helpful, but not mandatory.
  • Interest in AI, data analytics, and cloud computing will boost your learning experience.
  • A computer with internet access and willingness to experiment with AI and data tools.
  • No need for advanced math or AI expertise—we’ll cover what you need step by step.

Description

Are you ready to future-proof your data skills in the era of Agentic AI and autonomous systems?

This course—Mastering Agentic AI SQL for Intelligent Data Systems—is your gateway to building robust SQL-based solutions that power intelligent agents, real-time applications, and decision-making systems. Designed by Akhil Vydyula, Lead Data Engineer at Publicis Sapient and former Senior Data Scientist at PwC, this course is packed with real-world use cases, advanced SQL logic, and AI integration techniques you won’t find elsewhere.

Whether you're a data analyst, ML engineer, backend developer, or an AI enthusiast, you’ll gain hands-on experience crafting queries that serve real-time agents, drive machine learning pipelines, and scale across distributed environments like AWS and PostgreSQL.

What You’ll Learn:

  • The architecture of Agentic AI systems

  • Writing optimized SQL queries for large-scale AI pipelines

  • Building feature stores, embedding tables, and real-time analytics

  • Leveraging SQL with Python and LangChain

  • Postgres + AWS + S3-based data lake transformations

  • Live projects using PySpark, SQL, and cloud workflows

  • Working with incremental data loads and real-time orchestration

  • Practical tricks to reduce latency and improve data freshness

By the end, you’ll be able to design, implement, and scale SQL workflows that directly empower AI agents, drive intelligent automation, and enhance decision systems.

Who this course is for:

  • Data analysts, engineers, and developers looking to apply SQL in AI-driven systems and automation.
  • AI/ML enthusiasts and Python programmers who want to combine LLMs with structured data querying.
  • Cloud and data pipeline professionals exploring advanced SQL workflows for intelligent systems.
  • Students and career switchers eager to build cutting-edge skills in data engineering and Gen AI

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