Overview

This course offers a comprehensive guide to cloud analytics, focusing on the process of utilizing Google Cloud Platform (GCP) for processing and analyzing large-scale data. It is designed to equip learners with the knowledge required to harness cloud analytics tools, enabling businesses to extract actionable insights from big data. Throughout the course, you will learn how to design and implement end-to-end analytics solutions, leveraging machine learning and deep learning techniques to analyze data more effectively. The course includes practical steps for data ingestion, processing, and best practices for building analytics pipelines. What sets this course apart is its unique combination of theory and hands-on practice, empowering learners to build real-world cloud-based analytics engines. You will also gain insights into the design and business considerations necessary to deliver effective cloud analytics solutions. This course is suitable for professionals involved in data analytics, machine learning, and cloud computing, particularly those aiming to implement cloud-based analytics systems. While prior experience with cloud platforms is helpful, it is not a requirement.

Syllabus

Introducing Cloud Analytics

In this section, we explore cloud analytics fundamentals, cloud computing models, and service levels (IaaS, PaaS, SaaS) to enable effective data management.

Design and Business Considerations

In this section, we explore cloud migration strategies, prerequisites for cloud deployment, and multi-provider architecture design to ensure effective cloud adoption and infrastructure planning.

GCP 10,000 Feet Above A High-Level Understanding of GCP

In this section, we explore GCP services, their purposes, and compare cloud vendor categories to guide effective tool selection for specific workloads.

Ingestion and Storing Bring the Data and Capture It

In this section, we explore cloud data ingestion and storage services, including Cloud Dataflow, Cloud Pub/Sub, and Cloud Storage, focusing on their use cases and practical implementation.

Processing and Visualizing Close Encounter

In this section, we explore GCP services for data processing and visualization, including BigQuery, Cloud Datalab, and Data Studio, focusing on practical applications and real-world data workflows.

Machine Learning, Deep Learning, and AI on GCP

In this section, we explore AI and machine learning on GCP, focusing on text classification, NLP pipelines, and cloud-based ML services with practical applications.

Guidance on Google Cloud Platform Certification

In this section, we explore GCP certification exam guides and service selection for cloud architecture and data engineering.

Business Use Cases

In this section, we explore business use cases on GCP, focusing on real-time data architectures, data lakes, and recommendation systems. We analyze practical applications and end-to-end project planning using cloud services.