High-Demand Tech

Data Engineering (PySpark & Airflow)

Data Engineers build the plumbing that feeds modern analytics and AI models. Learn distributed computation, schema migrations, delta lakes, and orchestration.

Curated 2026 Curriculum GuideProject-Based Track

PySparkApache AirflowSnowflakeKafkadbtAWS S3PostgreSQL

🇮🇳 Indian Market Benchmark

Expected CTC Range₹9.0L – ₹26.0L LPA

Estimated Timeline16 – 20 Weeks

Demand Scope11,000+ Openings

Experience LevelIntermediate

Top Hubs:Bengaluru, Hyderabad, Pune

Explore Career Compass Match

Core Track Highlights

Critical backend requirement for all data-driven enterprises

Higher average pay than traditional software development

Combines SQL, Python, and cloud infrastructure

Structured Phase-by-Phase Syllabus

Focus on build-by-doing milestones rather than passive video consumption.

Weeks 1-5

Phase 1: Advanced SQL & Data Warehousing

Star schema, SCD Type 2, partitioning
Snowflake architecture & performance clustering
dbt transformations

🎯 Milestone Proof Project: Scalable E-Commerce Analytics Warehouse on Snowflake.

Weeks 6-12

Phase 2: Big Data with PySpark & Delta Lake

Spark DataFrames, lazy evaluation, shuffling
Delta Lake ACID transactions
AWS EMR / Databricks clusters

🎯 Milestone Proof Project: Batch Processing 100M FinTech Transactions with Spark.

Weeks 13-18

Phase 3: Streaming & Airflow Orchestration

Kafka producer/consumer event queues
DAG scheduling, sensors, and alerting in Airflow
Data quality tests with Great Expectations

🎯 Milestone Proof Project: End-to-End Real-Time Clickstream Pipeline.

Technical Interview Questions & Answers

Q1: What is the difference between narrow and wide transformations in Apache Spark?

Narrow transformations (like map, filter) do not require data shuffling across partitions. Wide transformations (like groupBy, join) require shuffling data across worker nodes, which is computationally expensive.