1. Big data fundamentals

• Understanding Big Data Concepts: Exploring the definition, characteristics, and sources of Big Data, including the concepts of volume, velocity, variety, and veracity.
• Big Data Ecosystem: Familiarization with the components of the Big Data ecosystem, such as distributed systems, and core Big Data frameworks like Hadoop and Spark.
• Drawbacks of RDBMS: Understanding the limitations of traditional relational database management systems when dealing with large and complex datasets. 

2. Distributed storage and processing

• Hadoop Distributed File System (HDFS): Learning how Hadoop stores large datasets in a distributed environment, including concepts like file blocks and architecture.
• Apache Spark: Understanding Spark's ecosystem, including its core components, Resilient Distributed Datasets (RDDs), Spark SQL, and Spark Streaming for real-time analytics.
• Data Processing Techniques: Exploring batch processing with Hadoop MapReduce and other techniques for processing large datasets in a distributed environment.
• YARN (Yet Another Resource Negotiator): Understanding YARN's architecture and its role in managing resources and scheduling jobs in a Hadoop cluster. 

3. Real-time processing and streaming analytics

• Apache Kafka: Learning about Kafka's architecture, components like producers, consumers, brokers, and clusters, as well as its application in building event-driven systems.
• Spark Structured Streaming: Understanding how to use Spark Structured Streaming for processing real-time data streams and building streaming pipelines.
• Stream Processing Concepts: Exploring concepts like streaming sources, sinks, output modes, and handling event time and windowing in stream processing. 

4. Cloud platforms for big data

• Cloud Computing Fundamentals: Understanding the basics of cloud computing, including deployment models, service models (IaaS, PaaS, SaaS), and cloud providers like AWS, Azure, and GCP.
• Big Data on Cloud: Learning how to leverage cloud services for building and deploying Big Data solutions on platforms like AWS, Azure, and GCP.
• Cloud Services for Big Data: Exploring cloud-specific services like Amazon S3, Azure Data Lake Storage, Google Cloud Storage, Amazon EMR, AWS Glue, Azure Databricks, GCP BigQuery, and others for storing, processing, and analyzing Big Data. 

5. Data modeling, architecture, and governance

• Data Modeling: Designing and implementing effective data models for Big Data environments, including relational and NoSQL databases.
• Data Warehousing and Data Lakes: Understanding the concepts of data warehouses and data lakes, and their use in Big Data architectures.
• Data Governance and Management: Implementing data quality measures, metadata management, security protocols, and compliance best practices for Big Data solutions.
• Architecture Patterns: Exploring different Big Data architecture patterns like Lambda and Kappa architectures. 

6. Additional skills and tools

• Programming Languages: Developing proficiency in languages like Java, Python, or Scala, which are commonly used in Big Data development.
• NoSQL Databases: Understanding different types of NoSQL databases and their applications in Big Data solutions.
• ETL (Extract, Transform, Load): Learning about ETL processes and tools for data integration and warehousing.
• Data Visualization: Exploring tools and techniques for visualizing Big Data insights.
• Machine Learning and AI: Gaining an understanding of the application of machine learning and artificial intelligence in Big Data analytics. 

7. Hands-on experience

• Real-world Projects: Working on practical, industry-based projects and case studies to apply learned skills in designing and implementing Big Data solutions.
• Lab Sessions and Exercises: Participating in hands-on lab sessions to gain practical experience with Big Data tools and technologies.