Building Resilient Streaming Analytics Systems on GCP

Course Overview

This course, Building Resilient Streaming Analytics Systems on GCP, is designed to help learners understand the fundamentals of streaming analytics and how to build resilient streaming analytics systems on Google Cloud Platform (GCP). Learners will gain an understanding of how to use Cloud Pub/Sub to handle incoming streaming data, how to apply aggregations and transformations to streaming data using Cloud Dataflow, and how to store processed records to BigQuery or Cloud Bigtable for analysis. Additionally, learners will learn how to build resilient streaming analytics systems that can handle large volumes of data and scale up or down as needed. Finally, learners will gain an understanding of how to visualize streaming data using GCP tools such as Data Studio. This course is ideal for those who want to gain a comprehensive understanding of streaming analytics and how to build resilient streaming analytics systems on GCP.

[Applications]
After completing this course, participants should be able to apply the concepts learned to build resilient streaming analytics systems on GCP. They should be able to use Cloud Pub/Sub to ingest streaming data, Cloud Dataflow to apply aggregations and transformations, and BigQuery or Cloud Bigtable to store processed records. Participants should also be able to use the GCP console to monitor and manage their streaming analytics systems.

[Career Paths]
1. Cloud Data Engineer: Cloud Data Engineers are responsible for designing, developing, and maintaining data pipelines and architectures on cloud platforms such as Google Cloud Platform. They must be knowledgeable in streaming data processing, data storage, and data analysis. As streaming data processing becomes more popular, the demand for Cloud Data Engineers is expected to increase.

2. Big Data Analyst: Big Data Analysts are responsible for analyzing large datasets to uncover trends and insights. They must be knowledgeable in data mining, data visualization, and machine learning. As streaming data processing becomes more popular, the demand for Big Data Analysts is expected to increase.

3. Cloud Solutions Architect: Cloud Solutions Architects are responsible for designing and implementing cloud-based solutions. They must be knowledgeable in cloud computing, cloud security, and cloud architecture. As streaming data processing becomes more popular, the demand for Cloud Solutions Architects is expected to increase.

4. Data Scientist: Data Scientists are responsible for analyzing data to uncover patterns and insights. They must be knowledgeable in data mining, data visualization, and machine learning. As streaming data processing becomes more popular, the demand for Data Scientists is expected to increase.

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

3. Master of Science in Cloud Computing: This degree path focuses on the fundamentals of cloud computing, such as cloud architecture, cloud security, and cloud storage. It also covers topics such as streaming analytics, which is becoming increasingly important for businesses.

4. Master of Science in Big Data: This degree path focuses on the analysis and interpretation of large datasets. It covers topics such as data mining, machine learning, and data visualization. It also covers topics such as streaming analytics, which is becoming increasingly important for businesses.