Building Codeless Pipelines on Cloud Data Fusion

Course Overview

This course, Building Codeless Pipelines on Cloud Data Fusion, provides learners with the opportunity to gain hands-on experience with Cloud Data Fusion, a cloud-native, code-free, data integration platform. ETL Developers, Data Engineers and Analysts can benefit from the pre-built transformations and connectors to build and deploy their pipelines without worrying about writing code. The course begins with a quickstart lab that introduces learners to the Cloud Data Fusion UI. Learners will then have the chance to run batch and realtime pipelines as well as use the built-in Wrangler plugin to perform data transformations.

[Applications]
The application of this course can be seen in the development of data pipelines for ETL Developers, Data Engineers and Analysts. With the knowledge gained from this course, they can use the pre-built transformations and connectors to build and deploy their pipelines without having to write code. Additionally, they can use the Wrangler plugin to perform data transformations. This course can also be used to familiarise learners with the Cloud Data Fusion UI.

[Career Paths]
1. Data Engineer: Data Engineers are responsible for designing, building, and maintaining data pipelines. With Cloud Data Fusion, Data Engineers can quickly and easily build and deploy pipelines without having to write code. This makes it easier to manage and maintain data pipelines, as well as to scale them up or down as needed. Additionally, Cloud Data Fusion provides a wide range of pre-built transformations and connectors, making it easier for Data Engineers to quickly build and deploy pipelines.

2. ETL Developer: ETL Developers are responsible for designing, building, and maintaining Extract, Transform, and Load (ETL) processes. With Cloud Data Fusion, ETL Developers can quickly and easily build and deploy ETL processes without having to write code. This makes it easier to manage and maintain ETL processes, as well as to scale them up or down as needed. Additionally, Cloud Data Fusion provides a wide range of pre-built transformations and connectors, making it easier for ETL Developers to quickly build and deploy ETL processes.

3. Data Analyst: Data Analysts are responsible for analyzing data and providing insights. With Cloud Data Fusion, Data Analysts can quickly and easily build and deploy pipelines to analyze data without having to write code. This makes it easier to manage and maintain data pipelines, as well as to scale them up or down as needed. Additionally, Cloud Data Fusion provides a wide range of pre-built transformations and connectors, making it easier for Data Analysts to quickly build and deploy pipelines.

4. Data Scientist: Data Scientists are responsible for developing and deploying machine learning models. With Cloud Data Fusion, Data Scientists can quickly and easily build and deploy pipelines to develop and deploy machine learning models without having to write code. This makes it easier to manage and maintain data pipelines, as well as to scale them up or down as needed. Additionally, Cloud Data Fusion provides a wide range of pre-built transformations and connectors, making it easier for Data Scientists to quickly build and deploy pipelines.

The demand for Data Engineers, ETL Developers, Data Analysts, and Data Scientists is growing rapidly as organizations increasingly rely on data-driven insights to make decisions. Cloud Data Fusion provides a powerful platform for these professionals to quickly and easily build and deploy pipelines, making it easier for them to manage and maintain data pipelines, as well as to scale them up or down as needed. Additionally, Cloud Data Fusion provides a wide range of pre-built transformations and connectors, making it easier for these professionals to quickly build and deploy pipelines.

[Education Paths]
1. Bachelor of Science in Computer Science: This degree path focuses on the fundamentals of computer science, such as programming, software engineering, and data structures. It also covers topics such as artificial intelligence, machine learning, and cloud computing. With the increasing demand for data engineers and analysts, this degree path is becoming increasingly popular.

2. Bachelor of Science in Data Science: This degree path focuses on the analysis and interpretation of data. It covers topics such as data mining, machine learning, and data visualization. It also covers topics such as data engineering, data warehousing, and cloud computing. This degree path is becoming increasingly popular as businesses are looking for professionals who can help them make sense of their data.

3. Master of Science in Data Engineering: This degree path focuses on the design and implementation of data systems. It covers topics such as data modeling, data warehousing, and cloud computing. It also covers topics such as data mining, machine learning, and data visualization. This degree path is becoming increasingly popular as businesses are looking for professionals who can help them build and maintain their data systems.

4. Master of Science in Artificial Intelligence: This degree path focuses on the development of intelligent systems. It covers topics such as machine learning, natural language processing, and computer vision. It also covers topics such as data engineering, data warehousing, and cloud computing. This degree path is becoming increasingly popular as businesses are looking for professionals who can help them develop intelligent systems.