Course Overview: Big Data Engineering for Analytics

This advanced course is designed for data engineers seeking to deepen their expertise in building robust and scalable data lakes and processing platforms. Participants will gain hands-on experience in constructing distributed datasets, applying key design and architectural practices, and leveraging big data technologies to optimize data storage and access models. Through a comprehensive curriculum, attendees will learn how to evaluate business requirements, design appropriate data architectures, and implement efficient data processing solutions for analytics.

Course Objectives: Big Data Engineering for Analytics

• Fundamentals of Big Data: Gain a solid understanding of the core principles of big data, including characteristics, storage solutions, analysis techniques, and distribution models.
• Fault-Tolerant Computing Frameworks: Acquire expertise in fault tolerance within big data environments, ensuring resilience and high availability of data systems.
• Task Construction and Execution: Learn to build configurable, executable tasks within big data platforms, using best practices for efficiency and scalability.
• Data Processing and Functional Programming: Understand how to write functional programs to handle large datasets and perform advanced data processing tasks such as filtering, aggregation, and categorization.
• Data Storage and Querying: Master various methods for data persistence and querying, including the use of Resilient Distributed Datasets (RDDs) within frameworks like Apache Spark.

Course Outline: Big Data Engineering for Analytics

Day 1: Introduction to Data Engineering and Big Data Analytics

• Overview of Data Science, Data Engineering, and Big Data: Introduction to the roles and scope of data science and engineering within the context of big data analytics.
• Data Scientist vs. Data Engineer: Understand the distinction between data scientists and data engineers, and how their roles intersect in big data projects.
• Core Skills and Resources for Data Engineering: Essential competencies required for data engineering, including programming, data modeling, and cloud infrastructure.
• Big Data Analytics Perspective: Explore how big data analytics drives business intelligence, decision-making, and innovation across industries.

Day 2: Architectural Design and the Hadoop Ecosystem

• Architectural Design Principles in Big Data: Learn the foundational principles of big data architectures, focusing on scalability, resilience, and cost-efficiency.
• Reference Architecture Concepts: Understand the conceptual and logical frameworks used in designing big data systems.
• Big Data and Oracle Product Integration: Review how Oracle products fit into big data architectures and how to leverage them effectively.
• The Hadoop Ecosystem: Gain a comprehensive understanding of the Hadoop ecosystem and its components (HDFS, MapReduce, YARN, etc.), and learn how they are used in big data processing.

Day 3: Data Storage Solutions and NoSQL Databases

• Distributed File Systems: Learn how to design and implement distributed file storage systems for big data, with a focus on performance, scalability, and fault tolerance.
• NoSQL Databases: Explore NoSQL database solutions like MongoDB, Cassandra, and HBase, and understand their use cases in big data environments.
• Apache Spark and Functional Programming: Delve into Spark’s capabilities for processing big data, with a focus on functional programming paradigms that facilitate data manipulation and computation at scale.

Day 4: Managing and Processing Big Data

• Spark and Resilient Distributed Datasets (RDDs): Gain hands-on experience with Spark RDDs, exploring their use for fault-tolerant distributed data processing.
• Spark SQL for Big Data: Learn how to use Spark SQL for querying large datasets and performing complex analytical tasks.
• Real-Time Stream Processing with Spark: Explore Spark Streaming and other technologies for processing real-time data streams in big data systems.
• Managing Big Data Projects: Learn best practices for managing large-scale big data initiatives, from planning and resource allocation to monitoring and scaling systems.

Day 5: Case Study and Practical Application

• Case Study Analysis: Work on a real-world big data engineering case study to apply concepts learned throughout the course.
• Project Requirement Elaboration: Develop a clear understanding of the project requirements and identify the necessary steps for implementation.
• Project Presentation and Assessment: Present your project to the class, receive feedback, and assess your project’s alignment with best practices.
• Final Report and Demonstration: Prepare a comprehensive project report and demonstrate your big data engineering solutions.

Conclusion

Upon completion of the Big Data Engineering for Analytics course, participants will have the skills and knowledge to design, build, and manage complex data lakes and processing platforms using cutting-edge big data technologies. The course prepares data engineers to tackle large-scale, distributed data processing challenges, ensuring that they can create scalable, resilient, and high-performing data systems to support advanced analytics and business intelligence needs. This course is ideal for professionals seeking to enhance their skills in data engineering and advance their careers in big data analytics.