Distributed Systems for Big Data
( 25 Modules )

Module #1
Introduction to Big Data
Overview of big data, its characteristics, and the need for distributed systems

Module #2
Distributed Systems Fundamentals
Basic concepts of distributed systems, such as communication, concurrency, and fault tolerance

Module #3
Scalability and Performance in Distributed Systems
Designing for scalability, performance metrics, and bottleneck analysis

Module #4
Distributed System Architectures
Overview of different distributed system architectures, such as client-server, peer-to-peer, and microservices

Module #5
Hadoop and HDFS
Introduction to Hadoop, HDFS, and its ecosystem

Module #6
MapReduce Programming
Fundamentals of MapReduce programming, including data processing and job scheduling

Module #7
Spark and In-Memory Computing
Introduction to Apache Spark, its architecture, and in-memory computing concepts

Module #8
Spark Programming
Spark programming fundamentals, including RDDs, DataFrames, and Datasets

Module #9
Distributed Databases
Overview of distributed databases, including NoSQL databases and NewSQL databases

Module #10
HBase and Cassandra
In-depth coverage of HBase and Cassandra, including data modeling and query optimization

Module #11
Distributed File Systems
In-depth coverage of distributed file systems, including HDFS, Ceph, and Gluster

Module #12
Cloud Computing for Big Data
Overview of cloud computing, its benefits, and big data analytics in the cloud

Module #13
Amazon Web Services (AWS) for Big Data
In-depth coverage of AWS services, including S3, EMR, and Redshift

Module #14
Microsoft Azure for Big Data
In-depth coverage of Azure services, including Azure Storage, Azure Databricks, and Azure Synapse Analytics

Module #15
Google Cloud Platform (GCP) for Big Data
In-depth coverage of GCP services, including GCS, BigQuery, and Cloud Dataproc

Module #16
Distributed Streaming Systems
Overview of distributed streaming systems, including Apache Kafka, Apache Flink, and Apache Storm

Module #17
Real-Time Data Processing with Kafka
In-depth coverage of Apache Kafka, including its architecture, producers, and consumers

Module #18
Distributed Machine Learning
Overview of distributed machine learning, including parallel processing and model deployment

Module #19
TensorFlow and Deep Learning
In-depth coverage of TensorFlow, including its architecture, models, and distributed training

Module #20
Security and Authentication in Distributed Systems
Security concerns, authentication mechanisms, and authorization in distributed systems

Module #21
Monitoring and Debugging Distributed Systems
Monitoring tools, debugging techniques, and performance optimization in distributed systems

Module #22
Distributed System Design Patterns
Common design patterns for distributed systems, including master-slave, peer-to-peer, and microservices

Module #23
Case Studies in Distributed Systems for Big Data
Real-world examples of distributed systems in big data analytics, including use cases and architectures

Module #24
Future of Distributed Systems for Big Data
Emerging trends and technologies in distributed systems for big data, including edge computing and serverless computing

Module #25
Course Wrap-Up & Conclusion
Planning next steps in Distributed Systems for Big Data career

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Distributed Systems for Big Data ( 25 Modules )

Distributed Systems for Big Data
( 25 Modules )