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Computer Science
Big Data
Big Data Technologies
1. Introduction to Big Data
2. Core Principles of Distributed Systems
3. The Hadoop Ecosystem
4. Modern Data Processing with Apache Spark
5. Stream Processing Technologies
6. NoSQL Databases
7. Data Warehousing and Analytics on Big Data
8. Cloud-Based Big Data Platforms
9. Supporting Ecosystem and Tools
10. Big Data Governance and Security
11. Performance Optimization and Best Practices
12. Emerging Trends and Future Directions
Modern Data Processing with Apache Spark
Introduction to Apache Spark
Advantages over MapReduce
In-Memory Processing
Ease of Use
Performance Improvements
Iterative Algorithm Support
Unified Analytics Engine
Batch Processing
Stream Processing
Machine Learning Integration
Graph Processing
Spark Programming Languages
Scala
Python (PySpark)
Java
R (SparkR)
SQL
Spark Core Concepts
Resilient Distributed Datasets (RDDs)
Creation of RDDs
From Files
From Collections
From Other RDDs
Transformations
Map
Filter
FlatMap
GroupBy
Join
Union
Actions
Collect
Count
Save
Reduce
Foreach
Lazy Evaluation
Execution Plan
Optimization
Lineage Graph
RDD Persistence
Caching Strategies
Storage Levels
Spark Architecture
Driver Program
Job Coordination
Task Scheduling
SparkContext
Cluster Manager
Standalone
YARN
Mesos
Kubernetes
Executors
Task Execution
Memory Management
JVM Processes
Spark Execution Model
Jobs and Stages
Tasks and Partitions
Shuffle Operations
Dynamic Allocation
Structured APIs
DataFrames
Schema Definition
Operations on DataFrames
Catalyst Optimizer
Datasets
Type Safety
Encoders
Performance Benefits
Spark SQL
SQL Queries
Integration with DataFrames
Hive Integration
JDBC/ODBC Support
Spark Ecosystem Components
Spark Streaming (Legacy)
DStream Abstraction
Micro-Batch Processing
Input Sources
Output Operations
Structured Streaming
Event-Time Processing
Continuous Applications
Watermarking
Triggers
MLlib (Machine Learning Library)
Algorithms
Classification
Regression
Clustering
Collaborative Filtering
Pipelines
Transformers
Estimators
Pipeline Construction
Feature Engineering
Model Selection
GraphX (Graph Processing)
Graph Representation
Graph Algorithms
PageRank
Connected Components
Triangle Counting
Graph Operations
Spark Performance Optimization
Memory Management
Heap Memory
Off-Heap Storage
Garbage Collection Tuning
Partitioning Strategies
Data Partitioning
Custom Partitioners
Caching and Persistence
Storage Levels
Cache Management
Broadcast Variables
Efficient Data Distribution
Accumulators
Distributed Counters
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5. Stream Processing Technologies