Softlogic Systems Cloud Computing and Big Data Course Syllabus is specifically designed for College Students, Freshers, and Job Seekers. Our Cloud Computing and Big Data syllabus covers cloud platforms, storage services, virtualization, distributed computing, Hadoop ecosystem, Spark, and real-time data analytics. Our Cloud Computing and Big Data Course Content helps you learn Cloud Computing and Big Data step by Step with real-time projects and Interview Preparations.
Cloud Computing And Big Data Syllabus
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Syllabus for The Cloud Computing And Big Data Syllabus Course
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Module 1: Introduction to Cloud Computing
- What is Cloud Computing?
- Benefits and characteristics of Cloud Computing
- Evolution of Cloud technologies
- Cloud Service Models: IaaS, PaaS, SaaS
- Cloud Deployment Models: Public, Private, Hybrid, and Community
- Key players: AWS, Microsoft Azure, Google Cloud, IBM Cloud
Module 2: Cloud Infrastructure and Virtualization
- Understanding virtualization and hypervisors
- Virtual machines and containers (Docker, Kubernetes basics)
- Networking in cloud environments
- Storage services in the cloud (Object, Block, File storage)
- Load balancing, auto-scaling, and high availability
Module 3: Amazon Web Services (AWS) Fundamentals
- AWS account setup and IAM
- EC2 instances: launch, manage, and configure
- S3 storage and bucket policies
- RDS, Lambda, CloudWatch, and CloudTrail
- Introduction to AWS CLI and SDK
- Building and deploying applications on AWS
Module 4: Microsoft Azure Fundamentals (Optional Track)
- Azure architecture and core services
- Azure virtual machines and resource groups
- Azure Blob storage and databases
- Azure DevOps basics and automation tools
Module 5: Introduction to Big Data
- What is Big Data? Characteristics and types
- Applications of Big Data in industry
- Structured vs Unstructured data
- Role of Big Data in Cloud environments
Module 6: Hadoop Ecosystem
- Hadoop architecture and components
- HDFS: Features and file storage mechanisms
- MapReduce: Programming model and workflow
- YARN: Resource Management
- Hands-on with Hadoop commands and configuration
Module 7: Data Processing Tools
- Apache Hive: Querying large datasets
- Apache Pig: Data transformation scripts
- Apache Sqoop and Flume: Data ingestion techniques
- Real-time ingestion with Kafka (Intro level)
Module 8: Apache Spark and In-Memory Computing
- Spark architecture and core components
- RDDs, DataFrames, and SparkSQL
- Spark Streaming: Real-time processing
- Integration of Spark with HDFS and Hive
- Deploying Spark jobs on clusters
Module 9: Cloud Integration with Big Data
- Storing and processing Big Data on the cloud
- Data lakes and warehouses on AWS, Azure, and GCP
- Tools: Amazon Redshift, Google BigQuery, Azure Data Lake
- Cloud-based ETL pipelines and orchestration
Module 10: Security, Compliance, and Best Practices
- Cloud and data security fundamentals
- Identity and access management (IAM)
- Encryption, firewalls, and network security
- Compliance standards (GDPR, HIPAA, ISO, etc.)
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