Overview
This topic provides an introduction to big data management, covering the fundamental concepts, challenges, and opportunities in the domain of big data.

Relevance
Understanding the basics of big data management is crucial for laying the foundation for further exploration of advanced concepts and techniques in the field.

Overview
This topic delves into the concepts of data warehousing and data lakes, exploring their significance in storing and managing large volumes of data in big data environments.

Relevance
Data warehousing and data lakes are essential components of big data management infrastructure, making this topic crucial for understanding data storage and organization.

Overview
This topic covers the selection criteria for relational and non-relational data architectures, as well as cloud-native data storage concepts in the context of big data.

Relevance
Understanding various big data storage approaches is vital for designing efficient and scalable data storage solutions in big data environments.

Overview
This topic focuses on data wrangling techniques, including data extraction, transformation, and cleaning, essential for preparing diverse data sources for analysis.

Relevance
Data wrangling is a critical aspect of big data management, ensuring that data is structured and cleaned for meaningful analysis and insights.

Overview
This topic explores the application of design principles, human perception, and effective storytelling in the context of data visualization, emphasizing the role of visualization tools in big data analytics.

Relevance
Data visualization is crucial for communicating insights derived from big data, making this topic essential for understanding how to present data effectively.

Overview
This topic delves into the application of business analytics techniques in the context of big data, focusing on using data-driven insights for decision-making and problem-solving.

Relevance
Understanding the integration of business analytics with big data is essential for leveraging data for strategic business purposes.

Overview
This topic covers advanced techniques for relational data modeling in the context of big data, emphasizing the design aspects of relational databases for complex data structures.

Relevance
Advanced relational data modeling skills are crucial for designing efficient and scalable data models in big data management.

Overview
This topic explores the concepts of data marts, data lakes, and data warehouses, focusing on their roles in organizing and storing data for analytics and reporting.

Relevance
Understanding the architecture and usage of data marts, data lakes, and data warehouses is essential for effective data storage and retrieval in big data environments.

Overview
This topic provides an understanding of change management principles and best practices in the context of data management, emphasizing strategies for implementing and sustaining change.

Relevance
Change management is crucial for successfully implementing new data management processes and technologies in the context of evolving big data environments.

Overview
This topic offers an introduction to creating conceptual, logical, and physical data models, along with skills in creating databases and tables in SQL-enabled database management systems.

Relevance
Data management foundations are essential for establishing a strong understanding of database design and management in the context of big data environments.

Overview
This topic provides an overview of big data management systems and platforms, focusing on distributed file systems, parallel queries, and programming models used in big data environments.

Relevance
Understanding big data platforms and management systems is crucial for implementing efficient and scalable solutions for storing and processing large volumes of data.

Overview
This topic explores techniques for real-time data processing and analysis, including hands-on tutorials using real-time and semi-structured data examples.

Relevance
Real-time data processing is essential for extracting insights and value from streaming data sources, making this topic vital for understanding real-time data management.

Overview
This topic introduces key big data management tools, such as AsterixDB, HP Vertica, Impala, Neo4j, Redis, and SparkSQL, highlighting their relevance and applications in big data management.

Relevance
Understanding and gaining familiarity with these tools is essential for leveraging their capabilities in managing and analyzing big data effectively.

Overview
This topic covers the principles of data governance and data quality management in the context of big data, emphasizing the importance of maintaining data integrity and quality.

Relevance
Data governance and quality are critical aspects of big data management, ensuring that data is reliable and compliant with regulations and best practices.

Overview
This topic discusses the reasons behind the evolving plethora of new big data platforms and management systems, providing insights into the changing landscape of big data technologies.

Relevance
Understanding the evolving landscape of big data management systems is crucial for staying abreast of new technologies and making informed decisions in big data projects.

Overview
This topic focuses on the design principles and considerations for building a robust big data infrastructure plan and information system, addressing the unique requirements of big data environments.

Relevance
Designing an effective big data infrastructure is essential for ensuring scalability, performance, and reliability in managing and analyzing large volumes of data.

Overview
This topic provides insights into data management challenges and strategies specific to online game companies, offering real-world examples and best practices for managing game-related data.

Relevance
Understanding data management in online gaming is crucial for applying specialized data management techniques in the gaming industry.

Overview
This topic explores the challenges and techniques for managing big spatial data, focusing on the storage, indexing, and analysis of spatial datasets in the context of big data management.

Relevance
Big spatial data management is essential for handling geospatial data and deriving insights from spatially distributed information.

Overview
This topic introduces NoSQL databases and document databases, such as MongoDB, emphasizing their applications and relevance in big data management.

Relevance
Understanding NoSQL and document databases is crucial for leveraging their flexibility and scalability in managing diverse data types in big data environments.

Overview
This topic covers Apache Spark MLlib, focusing on machine learning techniques and applications in the context of big data management, and provides insights into future trends and advancements in big data management.

Relevance
Understanding machine learning techniques and future trends is essential for leveraging advanced analytics and staying updated with the latest developments in big data management.

The live sessions will provide an opportunity for learners to engage in interactive doubt clearing and mentorship, offering personalized guidance and support to reinforce the concepts covered in the pre-recorded videos.