Design and build the data infrastructure that companies depend on. Data engineering is the fastest-growing tech role in India — every company swimming in data needs engineers to make it useful.
Data engineering runs on Python and SQL. Master both deeply — advanced SQL (window functions, CTEs), and Python for scripting and data manipulation.
By the end, you'll be able to
Mini-project
Analyze a 1M+ row dataset with SQL: write 20 complex queries including window functions, CTEs, and performance optimization.
Learn how companies store analytical data. Understand dimensional modeling (star/snowflake schemas), slowly changing dimensions, and ETL vs ELT.
By the end, you'll be able to
Mini-project
Design a data warehouse for an e-commerce company: fact tables for orders/payments, dimensions for products/customers/time.
Process massive datasets. Learn Spark (PySpark), understand distributed computing, and work with big data file formats (Parquet, Avro).
By the end, you'll be able to
Mini-project
Process a 10GB dataset with PySpark: clean, transform, aggregate, and write to Parquet with partitioning.
Real-time data is the future. Learn Kafka for event streaming, producers/consumers, and how to build real-time data pipelines.
By the end, you'll be able to
Mini-project
Build a real-time analytics pipeline: generate fake user events, stream through Kafka, process with Spark Streaming, and store results.
Production pipelines need orchestration. Learn Apache Airflow to schedule, monitor, and manage complex data workflows.
By the end, you'll be able to
Mini-project
Build an Airflow DAG that: extracts from an API, transforms with Spark, loads to a database, and sends a Slack alert on completion.
Learn cloud-native data tools: AWS (Redshift, Glue, S3), GCP (BigQuery), or Azure (Synapse). Companies are migrating everything to cloud.
By the end, you'll be able to
Mini-project
Build a complete cloud data pipeline: S3 → Glue → Redshift → QuickSight dashboard for a sample business dataset.
Bad data is worse than no data. Learn data quality frameworks, testing, lineage, and governance practices.
By the end, you'll be able to
Mini-project
Add data quality checks to your pipeline: schema validation, null checks, freshness monitoring, and anomaly detection.
Data engineering interviews test: SQL (hard), Python, system design for data pipelines, and tools knowledge. Practice daily.
By the end, you'll be able to
Mini-project
Solve 50 hard SQL problems on LeetCode/HackerRank. Design 5 data pipeline architectures. Do 3 mock interviews.
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