Data Engineering with AI
Build Scalable Lakehouses, Real-Time Streaming & AI-Ready Data Pipelines.
Get Placed in Top MNCs.
Master Data Engineering with AI covering Apache Spark, PySpark, Databricks, Snowflake, dbt, Airflow, Kafka, and Vector Lakehouse pipelines. 100% placement support.
Autonomous
Systems
& Real Data
Start Your Career in Data Engineering with AI
Get personalized curriculum & free 1-on-1 counseling.
Online / Offline Classes
(10+ Years Exp)
Capstone Projects
Mock Technical Interviews
Certification Guidance
Placement Drives
Complete Data Engineering with AI Curriculum — Foundations to Enterprise Scale
Engineered in collaboration with principal engineers from leading product and Fortune 500 AI teams.
Advanced SQL, Dimensional Modeling & Lakehouse Architecture
- Advanced SQL: Window Functions, CTEs, Recursive Queries & Query Execution Plans
- Dimensional Modeling: Star Schemas, Snowflake Schemas, Fact vs Dimension Tables
- Slowly Changing Dimensions (SCD Type 1, Type 2, Type 3)
Big Data Processing with Apache Spark & PySpark
- Spark Architecture: Driver, Executors, Cluster Managers, RDDs vs DataFrames
- PySpark DataFrame API: Transformations, Actions, Filtering, Aggregations & Joins
- Spark Optimization: Catalyst Optimizer, Tungsten Execution Engine & Adaptive Query Execution (AQE)
Databricks Delta Lake & Snowflake Cloud Lakehouse
- Delta Lake ACID Transactions: Time Travel, Schema Enforcement & Schema Evolution
- OPTIMIZE, Z-Ordering & Compaction for Sub-Second Delta Query Performance
- Databricks Unity Catalog: Centralized Governance, Access Control & Data Lineage
Event-Driven Streaming with Apache Kafka & Orchestration with Airflow
- Apache Kafka Architecture: Topics, Partitions, Producers, Consumers & Consumer Groups
- Schema Registry & Avro Serialization for Resilient Event Ingestion
- Kafka Connect: Sourcing Data from RDBMS (CDC with Debezium) into S3/Lakehouse
Detailed Module-by-Module Breakdown
Click each module below to explore technical topics, coding labs, and tools covered.
Tools & Frameworks You Will Master
Gain hands-on proficiency in the exact modern tech stack used across Fortune 500 tech teams.
Build Experience That Gets You Interview-Ready
Real project work. Agile exposure. Mentor feedback. A portfolio you can talk about in interviews.
Project Reviews
Prep
Already trained. Now build proof of your skills.
Turn learning into practical experience you can discuss with confidence.
- Work on live enterprise projects
- Build a real-world portfolio
- Use Agile workflows & Jira
- Get mentor feedback
- Practice with mock interviews
Career & Salary Calculator
Explore verified 2026 compensation benchmarks and market demand curves across India's top tech hubs.
Based on verified 2026 hiring data from Fortune 500 and Top MNC tech recruiters.
Frequently Asked Questions
Everything you need to know about the Data Engineering with AI Certification Course, batches, prerequisites & placement assurance.
How does Data Engineering with AI differ from traditional Data Engineering?
Traditional Data Engineering focuses on moving and transforming structured tabular data with SQL and Spark. Data Engineering with AI expands this to handle unstructured data (documents, audio, logs), building automated vector ingestion pipelines, embedding data in-flight, and leveraging AI (Databricks AI, Snowflake Cortex, LLMs) to clean, classify, and enrich enterprise data at scale.
Which cloud platforms are covered in this course?
You will work directly with Databricks (AWS/Azure) and Snowflake, the two dominant cloud lakehouse platforms in the global enterprise market.
Will I get hands-on experience with PySpark, Kafka, and dbt?
Yes! Every single module features dedicated hands-on labs where you build actual production code, write streaming jobs with PySpark and Kafka, build incremental models in dbt, and orchestrate them with Apache Airflow.