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Apache Spark Mastery

Course Level: Intermediate to Advanced Prerequisites: Scala/Python basics, distributed systems concepts, HDFS knowledge helpful Duration: 30-35 hours Hands-on Projects: 20+ coding exercises and real-world scenarios

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What You’ll Master

Apache Spark revolutionized big data processing by providing a unified engine for batch, streaming, machine learning, and graph processing - all 100x faster than MapReduce for iterative workloads. You’ll gain deep expertise in:
  • Core Abstractions: RDDs, DataFrames, Datasets, and their performance characteristics
  • Spark SQL: Advanced query optimization, catalyst optimizer internals
  • Structured Streaming: Real-time processing with exactly-once semantics
  • MLlib: Distributed machine learning at scale
  • Performance Tuning: Memory management, partitioning, broadcast variables
  • Production Operations: Cluster managers, deployment patterns, monitoring
This course covers Spark 3.x with emphasis on both theoretical foundations (from the original Spark paper) and production-ready implementations.

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Course Structure

Duration: 3-4 hours | Foundation ModuleStart with the theoretical foundations from the Resilient Distributed Datasets (RDD) paper and understand why Spark is 100x faster than MapReduce for certain workloads.What You’ll Learn:
  • The limitations of MapReduce that Spark solves
  • Deep dive into the RDD paper (simplified)
  • Understanding lineage and fault tolerance without replication
  • Lazy evaluation and DAG execution model
  • Evolution from RDDs to DataFrames to Datasets
Key Topics:
  • In-memory computing vs disk-based MapReduce
  • Narrow vs wide transformations
  • Spark’s unified processing model
  • Architecture: Driver, Executors, Cluster Manager
Start Learning →
Duration: 4-5 hours | Core ModuleMaster the foundational RDD API and understand when to use RDDs vs higher-level APIs.What You’ll Learn:
  • Creating RDDs from various sources
  • Transformations: map, filter, flatMap, reduceByKey
  • Actions: collect, count, reduce, saveAsTextFile
  • Pair RDD operations and joins
  • Partitioning strategies
  • Persistence and caching
Hands-on Labs:
  • WordCount comparison: MapReduce vs Spark
  • Log analysis with RDD transformations
  • Implementing PageRank algorithm
  • Custom partitioners for optimization
  • Broadcast variables and accumulators
Code Examples: Scala and PySpark implementationsDeep Dive →
Duration: 5-6 hours | Core ModuleLearn the high-level DataFrame API and Spark SQL for structured data processing with automatic optimization.What You’ll Learn:
  • DataFrame vs Dataset vs RDD comparison
  • Creating DataFrames from various sources (Parquet, JSON, JDBC)
  • Catalyst optimizer internals
  • Tungsten execution engine
  • User-defined functions (UDFs) and User-defined aggregate functions (UDAFs)
  • Window functions and complex aggregations
Advanced Topics:
  • Physical plan generation
  • Predicate pushdown and column pruning
  • Adaptive Query Execution (AQE)
  • Data source API v2
Hands-on Projects:
  • ETL pipeline with DataFrame transformations
  • Complex SQL queries on large datasets
  • Performance comparison: UDF vs built-in functions
  • Schema evolution with Delta Lake
Master DataFrames →
Duration: 4-5 hours | Streaming ModuleBuild real-time streaming applications with exactly-once semantics and stateful processing.What You’ll Learn:
  • Structured Streaming model: micro-batches vs continuous
  • Source: Kafka, File, Socket
  • Sinks: Foreach, File, Memory, Console
  • Output modes: Append, Complete, Update
  • Watermarking for late data handling
  • Stateful operations: aggregations, joins, arbitrary stateful processing
Advanced Patterns:
  • Stream-stream joins
  • Stream-static joins
  • Deduplication
  • Sessionization
  • Exactly-once semantics with checkpointing
Real-World Project:
  • Real-time analytics on Kafka streams
  • IoT sensor data processing
  • CDC (Change Data Capture) pipeline
Stream Processing →
Duration: 5-6 hours | ML ModuleImplement machine learning pipelines at scale with Spark MLlib.What You’ll Learn:
  • ML Pipeline API: Transformers, Estimators, Pipelines
  • Feature engineering: VectorAssembler, StringIndexer, OneHotEncoder
  • Algorithms: Classification, Regression, Clustering
  • Model selection and tuning: CrossValidator, ParamGrid
  • Model persistence and deployment
Algorithms Covered:
  • Logistic Regression, Decision Trees, Random Forest
  • Gradient Boosted Trees (GBT)
  • K-Means, Bisecting K-Means
  • Collaborative Filtering (ALS)
  • Word2Vec, Topic Modeling (LDA)
Hands-on Projects:
  • Customer churn prediction pipeline
  • Recommendation system with ALS
  • Text classification with feature extraction
  • Hyperparameter tuning at scale
ML at Scale →
Duration: 4-5 hours | Advanced ModuleMaster Spark performance tuning for production workloads.What You’ll Learn:
  • Memory management: execution vs storage memory
  • Serialization: Kryo vs Java serialization
  • Shuffle optimization and partitioning strategies
  • Broadcasting and data locality
  • Catalyst optimizer and Tungsten
  • Adaptive Query Execution (AQE) deep dive
Tuning Topics:
  • Executor sizing and resource allocation
  • Data skew handling
  • Spill prevention
  • Caching strategies
  • Join optimizations (broadcast, sort-merge, shuffle hash)
Performance Patterns:
  • Avoiding wide transformations
  • Coalesce vs repartition
  • Predicate pushdown
  • Column pruning
Optimize Performance →
Duration: 3-4 hours | Operations ModuleDeploy and manage Spark applications in production across different cluster managers.What You’ll Learn:
  • Cluster managers: Standalone, YARN, Kubernetes, Mesos
  • Deploy modes: Client vs Cluster mode
  • Dynamic resource allocation
  • Application monitoring with Spark UI
  • Integration with Prometheus and Grafana
  • Common failure scenarios and debugging
Deployment Patterns:
  • On-premises with YARN
  • Cloud deployment (AWS EMR, Databricks, GCP Dataproc)
  • Kubernetes with Spark Operator
  • Docker containerization
Operational Skills:
  • Log aggregation and analysis
  • Metrics collection and alerting
  • Cost optimization strategies
  • Security: Kerberos, SSL/TLS
Deploy to Production →
Duration: 4-5 hours | Advanced ModuleExplore advanced Spark features and ecosystem integrations.What You’ll Learn:
  • Delta Lake for ACID transactions
  • GraphX for graph processing
  • Integration with: Hive, HBase, Cassandra, MongoDB
  • Spark with Kubernetes
  • Koalas (pandas API on Spark)
  • Arrow for columnar data exchange
Advanced Patterns:
  • Data lakehouse architecture
  • Lambda vs Kappa architecture
  • Multi-hop streaming pipelines
  • Change data capture (CDC)
Integration Projects:
  • Building a data lakehouse with Delta Lake
  • Graph analytics with GraphX
  • Migrating pandas code to Spark
Advanced Integrations →
Duration: 5-6 hours | Hands-on ProjectBuild a production-ready, real-time recommendation system.Project Overview: Build a recommendation engine that processes user clickstream data in real-time and generates personalized recommendations.Components You’ll Build:
  • Kafka producer for clickstream events
  • Structured Streaming for real-time feature extraction
  • MLlib for collaborative filtering (ALS)
  • Delta Lake for feature storage
  • REST API for serving recommendations
  • Monitoring dashboard
Skills Demonstrated:
  • End-to-end architecture design
  • Streaming + batch integration
  • Model training and serving
  • Performance optimization
  • Production deployment
Build Project →

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Learning Path

1

Foundations

Understand the RDD paper and why Spark outperforms MapReduce for iterative algorithms.Start with Papers →
2

Core APIs

Master RDDs, DataFrames, and Spark SQL - the three levels of abstraction.Modules 2-3 | 9-11 hours
3

Streaming & ML

Learn real-time processing and distributed machine learning.Modules 4-5 | 9-12 hours
4

Production Readiness

Tune performance and deploy to production clusters.Modules 6-7 | 7-9 hours
5

Advanced & Capstone

Explore advanced topics and build a complete real-time system.Modules 8-9 | 9-11 hours

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Why Learn Spark?

Industry Leader

Most popular big data processing engine, adopted by Netflix, Uber, Apple, and thousands more.

Unified Engine

One framework for batch, streaming, ML, and graph processing - learn once, use everywhere.

Performance

100x faster than MapReduce for in-memory workloads, optimized execution with Catalyst and Tungsten.

Career Growth

High demand for Spark skills, with data engineers commanding top-tier salaries.

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What Makes This Course Different?

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1. Research Paper Foundation

We start with the RDD paper to understand the “why” behind Spark’s design decisions, giving you intuition for performance optimization.

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2. Multi-Language Support

All examples in both Scala (Spark’s native language) and PySpark (most popular in data science).

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3. Performance-First Approach

Every module includes performance considerations, not just correct code but fast code.

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4. Modern Spark 3.x

Covers latest features: Adaptive Query Execution, Dynamic Partition Pruning, Kubernetes native, Delta Lake.

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Prerequisites

Before starting this course, you should have:
  • Programming: Comfortable with Scala or Python (we teach both)
  • Distributed Systems: Basic understanding from Hadoop course or equivalent
  • SQL Knowledge: Familiarity with SQL queries
  • Data Structures: Understanding of hash maps, trees (helpful for optimization)
If you need distributed systems basics, start with our Hadoop course first.

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Learning Resources

Throughout this course, we reference:
  • Research Papers:
    • “Resilient Distributed Datasets: A Fault-Tolerant Abstraction for In-Memory Cluster Computing” (Zaharia et al., 2012)
    • “Spark SQL: Relational Data Processing in Spark” (Armbrust et al., 2015)
    • “Structured Streaming: A Declarative API for Real-Time Applications” (Armbrust et al., 2018)
  • Official Documentation: Apache Spark 3.x docs
  • Books: “Learning Spark” (2nd Edition) by Damji et al. (supplementary)
  • Code Repository: All examples available on GitHub

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Ready to Begin?

Start your journey into unified analytics with the theoretical foundations.

Module 1: Introduction & Spark Foundations

Begin with the research that made in-memory computing practical

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Course Outcomes

By completing this course, you’ll be able to:
  • Design and implement scalable Spark applications
  • Choose the right API (RDD vs DataFrame vs Dataset) for each use case
  • Build real-time streaming pipelines with exactly-once semantics
  • Train and deploy machine learning models at scale
  • Tune Spark applications for optimal performance
  • Deploy and monitor production Spark clusters
  • Integrate Spark with modern data platforms
  • Interview confidently for Spark/data engineering roles
Estimated Time to Complete: 30-35 hours of focused learning Recommended Pace: 2 modules per week for thorough understanding