Course Title: Certified Senior Big Data Engineer
Proficiency Level: Advanced
Prerequisite Requirements: Certification as a Big Data Engineer or equivalent work experience of at least 3 years.
Course Description:
Unit 1: Introduction to Big Data Architecture and Design
Unit 2: Analyzing Large-Scale Data with Apache Spark
Unit 3: Distributed Data Science and Engineering Using Spark
Unit 4: Migrating Data Processing Workloads to Cloud
Unit 5: Big Data Implementation on AWS
Unit 6: Architecting Big Data Solutions with Google Cloud
Unit 7: Designing and Implementing Big Data on Microsoft Azure
Unit 8: Data Warehouse Architecture and Design
Unit 9: Implementing Data Lakes and Data Warehouses
Unit 10: Big Data Governance and Security
Unit 11: Data Pipeline and Workflow Orchestration
Unit 12: Integrating Big Data with Enterprise Systems
Unit 13: Machine Learning for Big Data
Unit 14: Applying Deep Learning Techniques on Big Data
Unit 15: Distributed Data Processing with Apache Flink
Unit 16: Capstone Project
Course Objectives:
- Learn how to design, build and deploy large-scale big data systems for complex enterprise architectures.
- Understand various distributed processing frameworks and engine main with Apache Spark, Hadoop, Apache Flink, and their use-cases.
- Learn how to migrate traditional data processing workloads to cloud-based big data services with AWS, Google Cloud, and Microsoft Azure.
- Gain practical skills in architecting and configuring big data systems on cloud platforms as well as on-premise infrastructures.
- Learn how to design data warehouse solutions and understand different ETL techniques.
- Develop hands-on experience in implementing data lakes, warehouses, and data marts.
- Understand data governance, security, and privacy best practices in big data systems.
- Learn how to implement end-to-end workflows across batch processing, stream processing, and machine learning using stack Apache Spark, Apache Flink, and TensorFlow.
- Develop hands-on skills in integrating heterogeneous systems including communication with restful APIs, file transfers, message queues, and publish/subscribe mechanisms.
- Gain practical experience in implementing deep learning techniques for real-world problems, including natural language processing, image, and voice recognition.
