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Apache Spark Course

Course Index

Apache Spark CourseApache Spark Course1
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  1. 1Apache Spark Course for Beginners

Module 1: Introduction to Big DataModule 1: Introduction to Big Data4
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  1. 1What is Big Data?
  2. 23Vs of Big Data: Volume, Velocity, Variety
  3. 3Limitations of Traditional Data Processing
  4. 4Overview of Big Data Tools: Hadoop, Spark, Hive

Module 2: Getting Started with Apache SparkModule 2: Getting Started with Apache Spark6
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  1. 1What is Apache Spark?
  2. 2Use Cases of Apache Spark in Industry
  3. 3Installing Apache Spark on Windows (Step-by-Step Guide)
  4. 4Installing Apache Spark on a Local Linux System
  5. 5Installing Apache Spark on MacOS
  6. 6Running Your First Spark Application

Module 3: Apache Spark ArchitectureModule 3: Apache Spark Architecture4
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  1. 1Spark Ecosystem Components: Core, SQL, Streaming, MLlib
  2. 2Driver, Executors, and Cluster Manager in Apache Spark
  3. 3Job, Stage, and Task in Apache Spark
  4. 4Understanding DAG and Lazy Evaluation in Apache Spark

Module 4: RDDs - Resilient Distributed DatasetsModule 4: RDDs - Resilient Distributed Datasets5
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  1. 1What is an RDD in Apache Spark?
  2. 2Creating RDDs from Collections and Files
  3. 3Transformations vs Actions in Apache Spark
  4. 4Persistence and Caching in Apache Spark
  5. 5Limitations of RDDs

Module 5: Introduction to DataFramesModule 5: Introduction to DataFrames4
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  1. 1Why DataFrames over RDDs?
  2. 2Creating and Displaying DataFrames in PySpark
  3. 3Reading CSV, JSON, and Parquet Files in PySpark
  4. 4Selecting, Filtering, and Transforming Data in Spark DataFrames

Module 6: PySpark EssentialsModule 6: PySpark Essentials4
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  1. 1Setting up PySpark in Jupyter Notebook
  2. 2Basic DataFrame Operations in PySpark
  3. 3Working with Columns, Expressions, and User-Defined Functions in PySpark
  4. 4Practical Examples with Real Datasets

Module 7: Spark SQLModule 7: Spark SQL4
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  1. 1Creating Temporary Views and Global Views in Spark SQL
  2. 2Using SQL Queries on DataFrames in Spark
  3. 3Common Aggregations and Joins in Spark SQL
  4. 4Optimization using Catalyst in Apache Spark

Module 8: Working with Complex DataModule 8: Working with Complex Data4
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  1. 1Dealing with Nested JSON in Apache Spark
  2. 2Exploding Arrays and Structs in Apache Spark
  3. 3Flattening Hierarchical Data
  4. 4Schema Evolution and Inference in Apache Spark

Module 9: Data Cleaning & TransformationsModule 9: Data Cleaning & Transformations4
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  1. 1Dropping Nulls and Handling Missing Values in Apache Spark
  2. 2Replacing, Filtering, and Grouping Data in PySpark
  3. 3Window Functions in Apache Spark
  4. 4Data Aggregation and Pivots in PySpark

Module 10: Spark StreamingModule 10: Spark Streaming4
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  1. 1What is Spark Streaming?
  2. 2Micro-batching and Continuous Data Processing in Spark Streaming
  3. 3Reading from Kafka, Socket, and File Stream in Spark
  4. 4Window Operations and Aggregations in Spark Streaming

Module 11: Introduction to Machine Learning with Spark MLlibModule 11: Introduction to Machine Learning with Spark MLlib6
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  1. 1Overview of Spark MLlib
  2. 2Feature Engineering with VectorAssembler and StringIndexer in PySpark
  3. 3Building Machine Learning Pipelines in Apache Spark
  4. 4Classification Using Logistic Regression in Spark MLlib
  5. 5Linear Regression in Spark MLlib
  6. 6Clustering with KMeans in Spark MLlib

Module 12: Project – Real-World Data PipelineModule 12: Project – Real-World Data Pipeline1
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  1. 1Movie Ratings Project with Apache Spark

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