Live Online Apache Spark Course for Data Analytics

Live Course Module: Apache Spark Course for Data Analytics

Total Duration: 40 Hours (6 Weeks)

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Week 1: Introduction to Big Data and Apache Spark (6 Hours)

1. Introduction to Big Data and Distributed Computing

2. Overview of Apache Spark ecosystem and architecture

3. Components: Spark Core, SQL, Streaming, MLlib, and GraphX

4. Spark installation and environment setup (Standalone / Cluster)

5. Understanding RDD (Resilient Distributed Dataset) concepts

6. Hands-on: Writing your first Spark application using PySpark or Scala

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Week 2: Spark Core and RDD Operations (6 Hours)

1. Working with RDDs – creation, transformations, and actions

2. Lazy evaluation and Spark execution flow (DAG)

3. Caching, persistence, and partitioning for performance optimization

4. Pair RDDs and key-value transformations

5. Debugging and monitoring Spark jobs using Spark UI

6. Hands-on: RDD-based analytics on real datasets

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Week 3: Spark SQL and DataFrames (6 Hours)

1. Introduction to Spark SQL and DataFrames

2. Reading and writing data from multiple sources (CSV, JSON, Parquet, Hive)

3. Schema definition and data type management

4. Querying structured data using Spark SQL

5. Working with Datasets API (Scala/Java)

6. Hands-on: ETL pipeline and SQL-based analytics using DataFrames

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Week 4: Spark Streaming and Real-Time Data Processing (6 Hours)

1. Introduction to real-time data analytics and Spark Streaming

2. Micro-batch processing architecture and DStreams

3. Integrating Spark Streaming with Apache Kafka

4. Windowed operations and stateful streaming

5. Structured Streaming in Spark 3.x

6. Hands-on: Real-time data analytics pipeline with Kafka + Spark Streaming

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Week 5: Machine Learning with MLlib and Data Analytics (6 Hours)

1. Overview of MLlib and its role in data analytics

2. Data preparation, feature extraction, and transformation

3. Implementing supervised algorithms (Regression, Classification)

4. Implementing unsupervised algorithms (Clustering, PCA)

5. Model evaluation and tuning in Spark

6. Hands-on: Predictive analytics project using Spark MLlib

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Week 6: Advanced Topics, Optimization, and Capstone Project (6 Hours)

1. Spark optimization techniques: broadcast variables, accumulators, and caching

2. Advanced configurations for performance tuning and resource management

3. Spark on Cloud Platforms (AWS EMR, GCP Dataproc, Azure HDInsight)

4. Integration with Hadoop, Cassandra, and Elasticsearch

5. Capstone Project: End-to-End Data Analytics Pipeline using Apache Spark

6. Final review, project presentations, and certification assessment

🧩 Mini Project Ideas (Week 4 Hands-on)

Learners will implement a complete analytics project using Spark:

1. Project 1: Real-time Log Stream Analysis using Spark Streaming

2. Project 2: Customer Churn Prediction using Spark MLlib

3. Project 3: ETL Pipeline for Sales Data using Spark SQL

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🧑‍🏫 Teaching Methodology

• Live Coding Sessions and real-time demonstrations

• Hands-on Labs for each topic

• Assignments and quizzes after every module

• Interactive Discussions and Q&A

• Capstone Mini Project in the final week

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🏁 Final Deliverables

• Certificate of Completion

• End-to-End Spark Project

• Proficiency in PySpark/Spark SQL for data analytics

Course Outcome:

By the end of the course, learners will be able to:

• Understand Spark architecture and components.

• Write Spark applications using PySpark or Scala.

• Process batch and streaming data using Spark Core, SQL, and Streaming.

• Perform data analytics and machine learning tasks using Spark MLlib.

• Integrate Spark with data sources and visualization tools.