Data Engineering
Learn to build robust, scalable data pipelines and infrastructure. Master the tools that power modern data-driven organizations.
What You'll Learn
This program focuses on building the infrastructure that enables data-driven decision making. You'll learn to design, build, and maintain the pipelines that move and transform data at scale.
Why Learn Data Engineering?
Build the infrastructure that powers data-driven organizations
Explosive Demand
Data engineering is one of the fastest-growing tech roles. Companies need engineers to build data infrastructure.
Critical Role
Data engineers enable analytics and ML teams. Without good data infrastructure, data science fails.
Cloud-Native Skills
Master cloud platforms (AWS, GCP, Azure) and modern tools that companies are actively adopting.
Real-time Processing
Learn stream processing with Kafka and Spark Streaming - skills needed for modern data architectures.
Big Data Scale
Work with petabytes of data using distributed systems like Spark and modern data lakehouse architectures.
Premium Salaries
Data engineers command some of the highest salaries in tech due to specialized skills and high demand.
Career Opportunities
Roles you can pursue after mastering Data Engineering
Data Engineer
Design and build data pipelines, ETL processes, and data infrastructure for analytics.
High DemandCloud Data Engineer
Build and manage data solutions on AWS, GCP, or Azure cloud platforms.
TrendingStreaming Engineer
Build real-time data pipelines using Kafka, Spark Streaming, and Flink.
SpecializedBig Data Engineer
Work with large-scale distributed systems processing petabytes of data.
High DemandPlatform Engineer
Build and maintain data platforms that support analytics and ML workloads.
GrowingData Architect
Design enterprise data architectures, data lakes, and warehouse solutions.
Senior RoleTechnologies Deep Dive
Master the modern data engineering stack
Processing & Compute
Apache Spark
Distributed computing for batch and stream processing of large datasets.
PySpark
Python API for Spark - DataFrames, SQL, UDFs, and performance tuning.
Spark Streaming
Real-time stream processing with Structured Streaming and watermarks.
Apache Kafka
Distributed event streaming for real-time data pipelines and integration.
Apache Flink
Stream processing framework with exactly-once semantics and low latency.
Apache Airflow
Workflow orchestration for scheduling and monitoring data pipelines.
dbt
Data transformation tool for building reliable, tested data models.
Schema Registry
Manage and evolve data schemas for Kafka streaming pipelines.
Storage & Data Modeling
Data Modeling
Star schema, snowflake schema, and Data Vault design patterns.
Delta Lake
ACID transactions, time travel, and schema evolution for data lakes.
Apache Iceberg
Open table format with hidden partitioning and partition evolution.
Medallion Architecture
Bronze, silver, and gold layers for lakehouse data organization.
Slowly Changing Dimensions
SCD Types 1, 2, 3, and 6 for tracking historical data changes.
Data Warehouses
Build star schema models on Snowflake, Redshift, or BigQuery.
PostgreSQL
Advanced SQL, query optimization, and database design principles.
Data Quality & CDC
Cloud Platforms
AWS Data Services
S3, Glue, Redshift, EMR, and Kinesis for cloud data engineering.
Google BigQuery
Serverless data warehouse with SQL analytics and BigQuery ML.
Azure Data Services
Synapse Analytics, Data Factory, and Data Lake Storage Gen2.
Snowflake
Cloud data warehouse with streams, tasks, and time travel.
Databricks
Unified analytics platform with Unity Catalog and lakehouse architecture.
Curriculum Overview
A comprehensive path to becoming a skilled data engineer
- Python for Data Engineering
- Advanced SQL & Query Optimization
- Database Design Principles
- Data Modeling Techniques
- Working with APIs
- Spark Architecture & Components
- PySpark DataFrames & SQL
- Data Transformations at Scale
- Spark Optimization Techniques
- Spark Structured Streaming
- Apache Airflow Architecture
- DAGs & Operators
- Task Dependencies & Scheduling
- Error Handling & Retries
- Monitoring & Alerting
Projects You'll Build
Production-grade data engineering projects for your portfolio
Real-time Analytics Pipeline
Build a streaming pipeline that ingests, processes, and visualizes data in real-time.
Key Features:
- Kafka producer for event ingestion
- Spark Streaming for processing
- Real-time aggregations and windowing
- Dashboard integration for visualization
Data Warehouse on Cloud
Design and implement a star schema data warehouse on AWS Redshift with dbt transformations.
Key Features:
- Star schema dimensional modeling
- dbt models with testing and docs
- Airflow DAGs for orchestration
- Data quality checks and monitoring
Data Lake Architecture
Create a modern data lakehouse with bronze/silver/gold layers and automated quality checks.
Key Features:
- Medallion architecture (bronze/silver/gold)
- Delta Lake for ACID transactions
- Schema evolution and time travel
- Data quality validation pipeline
ETL Platform
Build a complete ETL platform with orchestration, monitoring, and data quality validation.
Key Features:
- Modular ETL pipeline architecture
- Airflow DAGs with error handling
- Great Expectations for validation
- Containerized with Docker
Skills You'll Master
Technical skills to build production-grade data infrastructure
Technical Skills
Professional Skills
Who Is This Program For?
Software Developers
Developers looking to transition into data engineering roles.
Data Analysts
Analysts wanting to move into more technical, infrastructure-focused roles.
Database Administrators
DBAs looking to expand into modern data infrastructure and cloud platforms.
Prerequisites
- Proficiency in Python programming
- Strong SQL knowledge
- Basic understanding of databases
- Familiarity with command line
This is an intermediate-level program. Some programming experience is required.
Frequently Asked Questions
Everything you need to know about our Data Engineering program
What prerequisites do I need?
You need proficiency in Python and strong SQL knowledge. Some experience with databases and command line is expected. This is an intermediate-level program.
What is the program duration?
The program runs for 6 months with flexible scheduling. Sessions are personalized 1:1 to fit your availability.
How is data engineering different from data science?
Data engineers build the infrastructure and pipelines that move and transform data. Data scientists analyze that data for insights. DE is more about building reliable systems than building models.
Which cloud platform will I learn?
We focus primarily on AWS (S3, Redshift, Glue, EMR) as it has the largest market share. The concepts transfer easily to GCP and Azure.
Do I need Spark experience before joining?
No, we teach Spark from scratch. You'll go from basics to building production-grade Spark applications during the program.
Will I work on real infrastructure?
Yes! You'll work with actual cloud services, set up real pipelines, and deploy production-like systems during the projects.
How is the mentorship conducted?
Sessions are 1:1 with your mentor, either online or at our Kochi center. You get personalized attention, architecture reviews, and career guidance.
What kind of support do I get?
Beyond sessions, you get doubt clearing support, project guidance, interview preparation, and access to our data engineering community.
Ready to Build Data Infrastructure?
Book a free consultation to discuss your background and create a personalized learning plan.