Data Engineering
Training
Master the pipelines, ETL workflows and cloud platforms that every serious AI system is built on. The foundation that separates AI that ships from AI that stalls.
6 Modules. Production-Grade Skills.
Everything from raw data ingestion to cloud deployment — structured for engineers who want to build systems that actually work in production.
Data Ingestion & Sources
Connect to databases, APIs, streams and file systems. Build reliable ingestion pipelines for batch and real-time data.
ETL & Data Transformation
Design and build ETL pipelines that clean, validate, enrich and transform raw data into analytics-ready formats.
Data Warehousing
Design dimensional models, build data warehouses and implement best practices for analytics and reporting.
Cloud Data Platforms
Deploy and manage data infrastructure on AWS, Azure and GCP. Optimize for cost, performance and reliability.
Pipeline Orchestration
Schedule, monitor and manage complex data workflows. Handle failures, retries and dependencies with confidence.
Data for AI Systems
Connect your data infrastructure directly into AI and ML pipelines. Build the foundation for RAG, embeddings and model training.
The ETL Pipeline Mastered
Every great data system runs on the same three-stage foundation. We go deep on each one.
- Batch & real-time sources
- REST APIs & webhooks
- Databases & data lakes
- Kafka streams & queues
- Cloud storage (S3, GCS)
- Data cleaning & validation
- Schema enforcement
- Aggregations & enrichment
- Spark & dbt pipelines
- Data quality checks
- Data warehouses
- Data lakes & lakehouses
- Snowflake & BigQuery
- Dimensional modelling
- AI feature stores
Industry Tools You'll Actually Use
Every tool in this program is production-grade and actively used at companies like Netflix, Airbnb, Uber and the Fortune 500.
Apache Kafka
Real-time event streaming at scale
Apache Spark
Distributed data processing engine
Snowflake
Cloud data warehousing platform
Apache Airflow
Workflow scheduling & monitoring
dbt
SQL-first data transformation
AWS / Azure / GCP
Multi-cloud data infrastructure
Delta Lake
Open-source data lakehouse
Prefect / Dagster
Modern pipeline orchestration
Your 8-Week Journey
A progressive path from data fundamentals to AI-ready infrastructure — one week at a time.
Data Foundations
Sources, formats and ingestion patterns
ETL Pipelines
Spark, dbt and transformation logic
Data Warehousing
Snowflake, BigQuery, dimensional models
Cloud Platforms
AWS, Azure and GCP deployments
Orchestration
Airflow, Prefect and monitoring
AI Integration
Capstone + AI-ready pipelines
What Engineers Say
Data engineers from Microsoft, Comcast and Citi Bank have trained with BuraqAI.
The Data Engineering training gave me exactly what I needed — practical, production-grade pipeline skills. Trainer Haroon made every concept click with real-world examples.
As a Tech Architect at Comcast, I needed deep data engineering skills fast. This course delivered — the Spark and dbt modules alone were worth the entire investment.
Finally a course that goes beyond theory. We built real pipelines on AWS from day one. The orchestration and cloud modules were exactly what my team needed.
The AI integration week was a game-changer. I finally understood how data engineering and AI systems connect — and shipped a production pipeline within two weeks of finishing.
Build the Foundation for AI
Every great AI system starts with great data. Join engineers from Microsoft, Comcast and Citi Bank who trained with BuraqAI.