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PySpark with Snowflake Course Details :
PySpark with Snowflake is an advanced Data Engineering and Big Data course designed to help students and working professionals build scalable, high-performance data pipelines using Apache Spark and Snowflake Cloud Data Warehouse.
This course covers Python fundamentals, PySpark internals, Spark SQL, Delta Lake, performance tuning, and Snowflake integration, with a strong focus on real-time industry use cases and hands-on projects. Training is delivered by industry experts with 12+ years of real-world experience.
Module 1: Big Data & PySpark Foundations
- What is Big Data & Distributed Computing
- Why Spark is used in real companies
- Spark Architecture & Execution Flow
- Spark 3.x latest features
- PySpark environment setup
Module 2: RDDs – Core Spark Internals (Interview Focus)
- What are RDDs & why they exist
- Creating RDDs
- Transformations: map, filter, flatMap, join
- Actions: reduce, aggregate, count
- Lazy evaluation, DAG, narrow vs wide transformations
Module 3: DataFrames & Spark SQL (Most Important)
- Creating DataFrames
- Schema design (explicit vs inferred)
- Reading & writing CSV, JSON, Parquet
- DataFrame operations: filter, select, joins
- Spark SQL queries & temporary views
Module 4: Advanced DataFrame Transformations
- Aggregations & groupBy
- Window functions (rank, row_number, running totals)
- Complex data types (arrays, maps, explode)
- Date & time functions
- Statistical transformations
Module 5: UDFs & Pandas UDFs
- What are UDFs & why they are slow
- Performance issues with UDFs
- Pandas UDFs (vectorized processing)
- Best practices & real use cases
Module 6: Performance Tuning & Optimization
- Partitioning strategies
- Repartition vs Coalesce
- Broadcast joins
- Caching & persistence
- Shuffle optimization
- Handling data skew
- Spark UI for debugging
Module 7: Data Storage & File Formats
- CSV vs JSON vs Parquet
- Partitioned data writes
- Handling corrupt & bad records
- Incremental data processing
Module 8: Delta Lake (Industry Standard)
- What is Delta Lake?
- ACID transactions in Spark
- Delta tables & schema evolution
- Merge (Upsert) operations
- Time travel & data versioning
Module 9: PySpark with Snowflake
- Snowflake architecture overview
- Spark–Snowflake connector
- Reading & writing data
- Pushdown optimization
- Cost & performance best practices
Module 10: Cloud Basics for Data Engineers
- Spark on Azure / AWS
- ADLS / S3 integration
- Cluster modes
- Job deployment basics
Module 11: Real-World Capstone Project
End-to-End Data Engineering Project
- Raw data ingestion
- Data cleaning & validation
- Transformation using PySpark
- Performance optimization
- Delta + Snowflake storage
- Production-style pipeline
Module 12: Interview Preparation
- PySpark interview questions
- Performance tuning scenarios
- Real production issues
- Resume & project explanation