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Updated in [May 25th, 2023]

Cloudera Data Platform (CDP) is a comprehensive data platform that enables organizations to securely store, process, and analyze data in the cloud. It provides a unified platform for data engineering, data warehousing, machine learning, and analytics. With CDP, organizations can quickly and easily deploy and manage their data infrastructure, while leveraging the scalability and flexibility of the cloud.

Learners can gain a comprehensive understanding of CDP by taking this course. They will learn how to install and configure CDP on a multi-node cluster, as well as how to connect Azure Data Lake Gen-2 Storage Container with CDP. They will also learn about YARN Service in CDP, including how to use YARN Queue Manager. Additionally, learners will gain an understanding of the prerequisites for master nodes and how to deploy master virtual machines on Azure.

By taking this course, learners will gain the skills and knowledge necessary to effectively use CDP for data engineering, data warehousing, machine learning, and analytics. They will be able to confidently deploy and manage their data infrastructure in the cloud, while leveraging the scalability and flexibility of the cloud. This course is ideal for data engineers, data scientists, and analytics professionals who want to gain a comprehensive understanding of CDP.

[Applications]
After completing this course, students should be able to apply the knowledge they have gained to configure and manage a Cloudera Data Platform cluster. They should be able to deploy master virtual machines on Azure, configure prerequisites for master, and connect Azure Data Lake Gen-2 Storage Container with CDP cluster. Additionally, they should be able to understand and manage YARN Service in ClouderA Data Platform, as well as use YARN Queue Manager.

[Career Paths]
1. Data Engineer: Data Engineers are responsible for designing, building, and maintaining data pipelines and architectures. They are also responsible for developing and deploying data models and algorithms to support data-driven decision making. Data Engineers are in high demand due to the increasing need for data-driven insights. As the demand for data-driven insights continues to grow, the need for Data Engineers will continue to increase.

2. Data Scientist: Data Scientists are responsible for analyzing and interpreting data to uncover insights and trends. They use a variety of techniques, such as machine learning, natural language processing, and statistical analysis, to uncover patterns and trends in data. Data Scientists are in high demand due to the increasing need for data-driven insights. As the demand for data-driven insights continues to grow, the need for Data Scientists will continue to increase.

3. Big Data Architect: Big Data Architects are responsible for designing and implementing big data solutions. They are responsible for designing and deploying data architectures, such as Hadoop clusters, and for developing and deploying data models and algorithms to support data-driven decision making. Big Data Architects are in high demand due to the increasing need for data-driven insights. As the demand for data-driven insights continues to grow, the need for Big Data Architects will continue to increase.

4. Cloud Solutions Architect: Cloud Solutions Architects are responsible for designing and implementing cloud-based solutions. They are responsible for designing and deploying cloud architectures, such as Cloudera Data Platform, and for developing and deploying data models and algorithms to support data-driven decision making. Cloud Solutions Architects are in high demand due to the increasing need for data-driven insights. As the demand for data-driven insights continues to grow, the need for Cloud Solutions Architects will continue to increase.

[Education Paths]
1. Bachelor of Science in Computer Science: This degree path provides students with a comprehensive understanding of computer science fundamentals, including programming, software engineering, computer architecture, and operating systems. Students will also learn about developing trends in computer science, such as artificial intelligence, machine learning, and cloud computing.

2. Master of Science in Data Science: This degree path provides students with a deep understanding of data science principles, including data mining, machine learning, and data visualization. Students will also learn about developing trends in data science, such as big data analytics, natural language processing, and predictive analytics.

3. Master of Science in Artificial Intelligence: This degree path provides students with a comprehensive understanding of artificial intelligence principles, including robotics, computer vision, and natural language processing. Students will also learn about developing trends in artificial intelligence, such as deep learning, reinforcement learning, and autonomous systems.

4. Master of Science in Cloud Computing: This degree path provides students with a comprehensive understanding of cloud computing principles, including distributed computing, virtualization, and cloud security. Students will also learn about developing trends in cloud computing, such as serverless computing, containerization, and edge computing.