Online Course – Certified Professional Specialization in Google Cloud Serverless Data Processing

Creating big data applications that can be scaled easily and efficiently.

Suggested by: Coursera (What is Coursera?)

Professional Certificate

Beginners Intermediate level Advanced involved

No prior knowledge required

Time to complete the course

7-day free trial

No unnecessary risks

Skills you will acquire in the course

  • Approximation error
  • graph
  • Regression
  • Causality
  • Data model
  • Extract, transform, and load (ETL)
  • Analytics
  • Statistics (Computer Science)

What you will learn in the course

Courses for which the course is suitable

  • Data Engineer
  • Big Data Analyst
  • Cloud Data Architect
  • Data Pipeline Developer
  • Data Operations Specialist
  • Data Scientist
  • Business Intelligence Developer
  • Data Processing Engineer

Internship – a three-part course series

It’s becoming increasingly difficult to maintain a technology stack that can meet the ever-increasing demands of a data-centric business. Every big data professional is familiar with the three V’s of big data: volume, velocity, and variety. What if there was a technology that was scalable and designed to meet these demands?

That’s where Google Cloud Dataflow comes in. Google Cloud Dataflow simplifies data processing by combining batch and stream processing, providing a serverless experience that lets users focus on analysis, not infrastructure. This specialization is for customers and partners who want to deepen their understanding of Dataflow and improve their data processing applications.

There are three courses within the specialization:

  • Foundations , which discusses how Apache Beam and Dataflow work together to meet your data processing needs without risking vendor lock-in
  • Developing Pipelines , which deals with how to convert our business logic into data processing applications that can be run in Dataflow
  • Operations , which explores the most important lessons for running a data application in Dataflow, including monitoring, troubleshooting, testing, and reliability.

Applied Learning Project:

This internship includes hands-on labs using the Qwiklabs platform. The labs are based on concepts discussed in the course modules. Where applicable, we provide Java and Python versions of the labs. For labs that require code addition/update, we provide a recommended solution for your reference.

Details of the courses that make up the specialization

Serverless Data Processing with Dataflow: Fundamentals in a Brazilian Portuguese Course

  • Course 1 • 3 hours

Course Details

What you’ll learn

  • Demonstrate how Apache Beam and Cloud Dataflow work together to meet your organization’s data processing needs
  • Summarize the benefits of Beam Portability Framework and enable you to use it in your Dataflow pipelines
  • Enable Shuffle & Streaming Engine for batch and streaming pipelines in a way that provides maximum performance
  • Enable flexible resource planning for more cost-effective performance

Serverless Data Processing with Dataflow: Course Operations in Brazilian Portuguese

  • Course 2 • 9 hours

Course Details

What you’ll learn

  • Perform monitoring, troubleshooting, testing, and CI/CD on Dataflow pipelines
  • Implement Dataflow pipelines with a focus on reliability to maximize the stability of the data processing platform

Skills you will acquire

  • Category: Approximation error
  • Category: Charts
  • Category: Regression
  • Category: Causality

Serverless Data Processing with Dataflow: Develop Pipelines in a Brazilian Portuguese Course

  • Course 3 • 18 hours

Course Details

What you’ll learn

  • In the second part of the Dataflow course series, we’ll delve deeper into pipeline development using the Beam SDK. We’ll start with an overview of basic ideas in Apache Beam.
  • Next, we will discuss processing streaming data using windows, watermarks, and drivers.
  • We’ll continue by covering options for sources and destinations in your pipelines, schemas for expressing your structured data, and how to perform transformations with state using the State and Timer APIs.
  • Let’s move on to reviewing best practices that will help maximize your pipeline performance.
  • Towards the end of the course, we will introduce SQL and Dataframes for representing your business logic in Beam and how to develop pipelines using Beam notebooks iteratively.

Skills you will acquire

  • Category: Data Model
  • Category: Extraction, Transformation, and Loading (ETL)
  • Category: Analytics
  • Category: Status (Computer Science)