CSBDE – Certified Senior Big Data Engineer


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.
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