Big Data Engineering
In-depth, practical training in designing, building and managing large-scale data systems using Hadoop, Spark and Kafka, preparing learners for roles such as Big Data Engineer, Data Platform Specialist, or ETL Developer.
UET Peshawar
Course Introduction
Learning Outcomes
Understand big data architecture and ecosystem components
Set up and operate Hadoop and Spark clusters
Build and automate scalable data ingestion and transformation pipelines
Implement real-time stream processing using Apache Kafka and Spark Streaming
Work with NoSQL databases (HBase, Cassandra) and data lakes
Deploy ETL workflows using Apache NiFi, Airflow, or cloud-native tools
Integrate data pipelines with cloud storage and analytics platforms
Follow best practices in data partitioning, fault tolerance, and governance
Target Audience
- group ICT graduates with interest in large-scale data systems
- group Early-career data engineers or backend developers
- group Freelancers pursuing data pipeline and processing contracts
- group Professionals seeking to enter data science infrastructure roles
Certification Criteria
- check Maintain 80%+ attendance.
- check Score 80% or more in the final assessment.
- check Successfully execute and present capstone project.
Assessment Strategy
12 Weeks Duration.
Modular Course Structure
Technical modules from big data fundamentals to capstone presentation and closure
Introduction to Big Data
3Vs, architecture, data lifecycle
Hadoop Framework
HDFS, MapReduce, Yarn
Apache Spark Fundamentals
RDDs, Spark Core, DAG
PySpark & DataFrames
Structured data, joins, aggregates
Spark SQL & Optimization
Queries, Catalyst, memory tuning
Apache Kafka
Brokers, producers, consumers
Spark Streaming
Batch vs real-time, windowing
ETL Pipeline Design
Data ingestion, transformation, load
Apache NiFi / Airflow
Workflow scheduling, flow-based ETL
Data Lake vs Data Warehouse
Delta Lake, partitioning, S3, Redshift
NoSQL Databases
HBase, Cassandra, key-value stores
Data Modeling & Partitioning
Sharding, schema design, compaction
Data Governance & Security
Lineage, audit logs, encryption
Cloud Platforms for Big Data
AWS EMR, Azure Synapse, GCP BigQuery
Monitoring & Debugging
Logging, metrics, Spark UI
Business Intelligence Tools
Power BI, Tableau, Superset (basic)
Freelancing & Industry Mapping
Fiverr/Upwork opportunities, data gigs
Capstone Planning
Data source, ingestion tools, outputs
Capstone Execution
End-to-end pipeline build
Capstone Presentation
Demo, use case walkthrough, evaluation
Revision, Final Exam, Feedback & Closure
Final review, mock interviews, evaluation
Tools & Platforms
Distributed Systems Hadoop, Spark, Kafka
ETL & Workflow Apache NiFi, Apache Airflow
Data Modeling Cassandra, HBase, Hive
Cloud Integration AWS EMR, Azure Data Lake, GCP BigQuery
BI & Analytics Power BI, Superset, Tableau
Automation & Debug Spark UI, Grafana, Logstash
AI Support ChatGPT, AutoGPT, CodeWhisperer
Hands-On Industry Capstone Project
Apply what you master into a comprehensive, real-world portfolio project evaluated by industry professionals.
Project Scope & Problem Statement
Build and Deploy a Real-Time Big Data Pipeline with End-to-End Workflow
Required Deliverables & Key Outcomes
Select a large dataset (IoT sensor data, transaction logs, social feeds, etc.)
Ingest data via Kafka or external connectors
Build ETL pipeline using Spark or NiFi
Store data in warehouse/data lake (Hive/S3/Redshift)
Visualize insights using BI tools
Present the full architecture with performance reports and diagrams to an expert panel
Ready to lead the Digital Revolution?
Join the next cohort and master the technologies shaping the future of global enterprise.
