Serverless Data Processing with Dataflow Course Overview

Target Audience for Serverless Data Processing with Dataflow

The "Serverless Data Processing with Dataflow" course is tailored for IT professionals focusing on scalable, efficient data processing and analytics.

• Data Engineers
• Cloud Solutions Architects
• Software Developers with a focus on data processing
• DevOps Engineers involved in CI/CD pipeline optimization
• IT Professionals interested in Apache Beam and Google Cloud Dataflow
• Technical Project Managers overseeing data processing projects
• Data Analysts looking to leverage serverless data processing capabilities
• System Administrators managing cloud resources and infrastructure
• Business Intelligence Professionals interested in real-time analytics
• IT Consultants specializing in cloud-based data solutions
• Technical Leads and Team Leads responsible for data processing strategies
• Data Scientists requiring large-scale data processing infrastructure
• Cloud Security Specialists focusing on secure data processing environments
• Quality Assurance Engineers involved in testing data processing pipelines
• Technical Support Engineers for troubleshooting and debugging data pipelines
• Professionals pursuing Google Cloud certifications related to data engineering

Learning Objectives - What you will Learn in this Serverless Data Processing with Dataflow?

Course Learning Outcomes and Concepts Overview:

This course equips students with comprehensive knowledge and skills for building and managing serverless data processing pipelines using Apache Beam and Dataflow, emphasizing performance, security, and best practices.

Learning Objectives and Outcomes:

• Understand how Apache Beam and Dataflow integrate to address complex data processing requirements.
• Leverage the Beam Portability Framework to create versatile, cross-platform data pipelines with custom containers.
• Optimize data processing by decoupling compute and storage, utilizing Shuffle and Streaming Engine, and employing Flexible Resource Scheduling.
• Manage IAM permissions, quotas, and capacity planning effectively for Dataflow jobs to enhance security and efficiency.
• Implement security best practices and choose the appropriate zonal data processing strategy for compliance with data locality needs.
• Review key Apache Beam concepts, including pipeline construction, PCollections, and PTransforms, to build foundational knowledge.
• Employ strategies for handling late data using windows, watermarks, and triggers in streaming pipelines.
• Develop custom sources and sinks, optimizing I/O operations for peak pipeline performance.
• Simplify pipeline code and boost performance using schemas and structured data.
• Utilize state and timers API for complex use cases, ensuring precise data processing control.
• Apply best practices for building, monitoring, and troubleshooting Dataflow pipelines to maintain high reliability and performance.
• Prototype and execute Dataflow pipelines within Beam notebooks, enhancing pipeline development with interactive tools.
• Integrate testing and CI/CD strategies to ensure pipeline quality and streamline deployment processes.
• Harness flex templates to create standardized, reusable Dataflow pipeline code, promoting efficiency and consistency across projects.