Course Description

Course Overview

The Serverless Data Processing with Dataflow (SDPF) course is designed to provide individuals with the knowledge and skills necessary to process and analyze large-scale data using serverless data processing techniques on the Google Cloud Platform (GCP). This course focuses on the key concepts, tools, and best practices for building scalable and efficient data processing pipelines using Dataflow.

Prerequisites

To enroll in the SDPF course, participants should have a strong understanding of cloud computing concepts and familiarity with GCP fundamentals. Basic knowledge of data processing concepts and a programming language such as Java or Python will be beneficial. Participants should also have access to a GCP project or demo environment to practice the concepts covered in the course.

Methodology

The SDPF course follows a blended learning approach, combining theoretical instruction, demonstrations, discussions, and hands-on labs. Participants will engage in instructor-led sessions where data processing concepts, best practices, and Dataflow features are explained. They will also have access to GCP resources and tools to gain practical experience in building data processing pipelines. The course encourages active participation, discussions, and collaborative problem-solving to reinforce learning.

Course Outline

Introduction to Serverless Data Processing

Overview of serverless data processing concepts and benefits

Understanding the role of Dataflow in serverless data processing

Exploring GCP tools and services for data processing

Building Data Processing Pipelines with Dataflow

Configuring and deploying Dataflow jobs

Understanding Dataflow transformations and data windowing

Implementing data processing patterns using Dataflow

Data Input and Output in Dataflow

Ingesting data from various sources into Dataflow pipelines

Writing data to different output sinks and systems

Utilizing GCP services like Pub/Sub and BigQuery with Dataflow

Data Transformation and Analytics

Performing data transformations using Dataflow’s built-in functions

Implementing advanced analytics and aggregations with Dataflow

Integrating external libraries and custom functions in Dataflow pipelines

Scaling and Optimization in Dataflow

Scaling Dataflow pipelines dynamically based on workload demands

Optimizing pipeline performance and resource utilization

Monitoring and troubleshooting Dataflow jobs

Real-time Data Processing with Dataflow

Building real-time data processing pipelines with Dataflow

Implementing windowing and event time processing in real-time scenarios

Handling late data and out-of-order events in real-time processing

Outcome

By the end of the SDPF course, participants will have:

• Developed a comprehensive understanding of serverless data processing concepts and best practices
• Acquired practical knowledge in building scalable and efficient data processing pipelines using Dataflow
• Gained expertise in ingesting, transforming, and analyzing data with Dataflow
• Learned techniques for scaling and optimizing Dataflow pipelines for performance
• Gained hands-on experience through practical labs and exercises
• Prepared to leverage serverless data processing capabilities with Dataflow on GCP

Labs

The SDPF course includes hands-on labs that provide participants with practical experience in building data processing pipelines using Dataflow. Some examples of lab exercises include:

• Configuring and running a Dataflow pipeline to process data from a specific source
• Performing data transformations and aggregations using Dataflow functions
• Integrating external libraries and custom functions in Dataflow pipelines
• Scaling and optimizing Dataflow pipelines based on workload demands
• Building real-time data processing pipelines with Dataflow and Pub/Sub
• Monitoring and troubleshooting Dataflow jobs for performance and errors

These labs enable participants to apply the concepts learned in the course and gain hands-on experience in building data processing pipelines using Dataflow, allowing them to develop practical skills in serverless data processing on GCP.