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Computer Science
Distributed Systems
Parallel and Distributed Computing
1. Introduction to Parallel and Distributed Computing
2. Parallel Computing Fundamentals
3. Parallel Algorithms and Applications
4. Distributed Computing Fundamentals
5. Time and Coordination in Distributed Systems
6. Replication and Consistency
7. Fault Tolerance in Distributed Systems
8. Distributed Algorithms
9. Large-Scale Data Processing
10. Cloud Computing
11. High-Performance Computing
12. Emerging Paradigms and Technologies
13. Performance Analysis and Optimization
14. Security in Parallel and Distributed Systems
9.
Large-Scale Data Processing
9.1.
MapReduce Framework
9.1.1.
MapReduce Programming Model
9.1.1.1.
Map Function
9.1.1.2.
Reduce Function
9.1.1.3.
Combiner Function
9.1.2.
MapReduce Execution
9.1.2.1.
Job Scheduling
9.1.2.2.
Data Locality
9.1.2.3.
Fault Tolerance
9.1.3.
MapReduce Algorithms
9.1.3.1.
Word Count
9.1.3.2.
Inverted Index
9.1.3.3.
PageRank
9.1.3.4.
K-means Clustering
9.2.
Apache Hadoop Ecosystem
9.2.1.
Hadoop Distributed File System
9.2.1.1.
HDFS Architecture
9.2.1.2.
Data Replication
9.2.1.3.
Block Management
9.2.1.4.
Fault Tolerance
9.2.2.
YARN Resource Manager
9.2.2.1.
Resource Allocation
9.2.2.2.
Application Management
9.2.2.3.
Scheduling Policies
9.2.3.
Hadoop MapReduce
9.2.3.1.
Job Execution
9.2.3.2.
Task Scheduling
9.2.3.3.
Speculative Execution
9.3.
Apache Spark
9.3.1.
Spark Architecture
9.3.1.1.
Driver Program
9.3.1.2.
Cluster Manager
9.3.1.3.
Executors
9.3.2.
Resilient Distributed Datasets
9.3.2.1.
RDD Operations
9.3.2.2.
RDD Lineage
9.3.2.3.
RDD Persistence
9.3.3.
Spark Components
9.3.3.1.
Spark SQL
9.3.3.2.
Spark Streaming
9.3.3.3.
MLlib
9.3.3.4.
GraphX
9.4.
Stream Processing
9.4.1.
Stream Processing Models
9.4.2.
Apache Storm
9.4.3.
Apache Kafka
9.4.4.
Apache Flink
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10. Cloud Computing