Architecting Serverless Big Data Solutions Using Google Dataflow from Pluralsight

Dataflow represents a fundamentally different approach to Big Data processing than computing engines such as Spark. Dataflow is serverless and fully-managed, meaning that provisioning resources and scaling can be transparent to the data architect.

Dataflow allows developers to process and transform data using easy, intuitive APIs. Dataflow is built on the Apache Beam architecture and unifies batch as well as stream processing of data. In this course, Architecting Serverless Big Data Solutions Using Google Dataflow, you will be exposed to the full potential of Cloud Dataflow and its radically innovative programming model. You will start this course off with a basic understanding of how Dataflow works for serverless compute. You’ll study the Apache Beam API used to build pipelines and understand what data sources, sinks, and transformations are. You’ll study the stages in a Dataflow pipeline and visualize it as a directed-acyclic graph. Next, you'll use Apache Beam APIs to build pipelines for data transformations in both Java as well as Python and execute these pipelines locally and on the cloud. You’ll integrate your pipelines with other GCP services such as BigQuery and see how you can monitor and debug slow pipeline stages. Additionally, you'll study different pipeline architectures such as branching and pipelines using side inputs. You’ll also see how you can apply windowing operations to perform aggregations on our data. Finally, you’ll work with Dataflow without writing any code using pre-built Dataflow templates that Google offers for common operations. At the end of this course, you should be comfortable using Dataflow pipelines to transform and process your data and integrate your pipelines with other Google services.

What's inside

Syllabus

Course Overview

Introducing Dataflow

Understanding and Using the Apache Beam APIs

Creating and Using PCollections and Side Inputs

Creating Pipelines from Google Templates

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Teaches the Apache Beam API, which is widely used in industry for data transformation

Develops skills and knowledge in pipeline architecture, which is highly relevant to data processing

Covers dataflow pipeline visualization, making it easier to understand the flow of data

Provides guidance on debugging and monitoring pipeline stages, ensuring optimal performance

Teaches how to use pre-built Dataflow templates, reducing the need for extensive coding

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Architecting Serverless Big Data Solutions Using Google Dataflow with these activities:

Review core programming concepts in Java or Python

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Strengthen your programming skills in Java or Python, which are essential for writing Dataflow pipelines.

Browse courses on Java

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Review basic programming concepts such as data types, variables, and control flow
Practice writing simple programs in Java or Python
Test your understanding by solving coding challenges

Organize and review course materials

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Enhance your understanding by organizing and reviewing course materials, solidifying your knowledge of key concepts.

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Organize notes, handouts, and assignments into a logical structure
Review the materials regularly to reinforce your learning
Identify areas where you need additional clarification or practice

Review Apache Beam architecture

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Refresh your understanding of Apache Beam architecture to strengthen your foundation for this course.

Browse courses on Apache Beam

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Reread the Apache Beam documentation on its architecture.
Review online tutorials and videos to reinforce knowledge.
Practice using the Apache Beam SDK in a local development environment.

15 other activities

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Follow tutorials on Apache Beam and Dataflow

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Supplement your learning by following tutorials that provide step-by-step guidance on building and running Dataflow pipelines.

Browse courses on Apache Beam

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Identify reputable tutorials on Apache Beam or Dataflow
Follow the tutorials and complete the exercises
Experiment with the code and modify it to fit your needs

Read 'Designing Data-Intensive Applications' by Martin Kleppmann

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Expand your knowledge of data processing systems and patterns by reviewing a foundational book in the field, enhancing your understanding of Dataflow.

View Secret Colors on Amazon

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Read the book and take notes on key concepts, such as data models, architectures, and consistency
Identify sections relevant to Dataflow and parallel processing
Discuss the book's ideas with classmates or online forums

Build a simple dataflow pipeline using Python

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Gain hands-on experience by following tutorials to build a basic dataflow pipeline.

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Find a tutorial on building dataflow pipelines in Python.
Set up your development environment and install necessary tools.
Follow the tutorial steps to create a Python dataflow pipeline.
Run the pipeline and analyze the results.

Explore Dataflow templates

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Explore Google's pre-built Dataflow templates to understand how they can simplify common data processing tasks.

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Visit the Google Cloud Dataflow templates page.
Choose a template that aligns with a data processing task you are interested in.
Follow the instructions provided by Google to run the template.
Review the output and documentation to understand the template's functionality.

Practice building Apache Beam pipelines

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Build Apache Beam pipelines to practice transforming and processing data, helping you grasp the pipeline construction process.

Browse courses on Apache Beam

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Review the Apache Beam documentation on pipeline construction
Create a simple Apache Beam pipeline using Java or Python
Add transformations to the pipeline, such as filtering, mapping, and grouping
Run the pipeline and observe the results
Experiment with different pipeline options and configurations

Solve Apache Beam programming exercises

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Reinforce your understanding of Apache Beam by solving programming exercises.

Browse courses on Cloud Dataflow

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Find online resources or textbooks with Apache Beam programming exercises.
Attempt to solve the exercises on your own.
Review your solutions and identify areas for improvement.
Seek assistance from peers or mentors if needed.

Write a blog post or article on Dataflow and its applications

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Share your knowledge and understanding of Dataflow by creating a blog post or article, reinforcing your learning and contributing to the community.

Browse courses on Dataflow

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Choose a specific aspect of Dataflow to focus on, such as its programming model, scalability, or use cases
Research the topic thoroughly and gather relevant information
Write an engaging and informative blog post or article
Publish your content on a platform like Medium, LinkedIn, or a personal blog
Promote your content and engage with readers

Create a diagram of a dataflow pipeline

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Solidify your understanding of dataflow pipelines by creating a visual representation.

Browse courses on Data Processing

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Identify the key stages and components of a dataflow pipeline.
Use a drawing tool or online diagramming service to create a diagram.
Label the components and explain their functionality.
Share your diagram with others for feedback.

Practice building Dataflow pipelines

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Sharpen your skills in building Dataflow pipelines to enhance your ability to process data effectively.

Browse courses on Google Dataflow

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Create a new Dataflow pipeline project in Google Cloud.
Choose a data source and sink for your pipeline.
Apply transformations to your data using the Apache Beam SDK.
Deploy your pipeline to the cloud and monitor its execution.

Design and implement a Dataflow pipeline for a specific use case

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Design and implement a Dataflow pipeline that addresses a real-world problem, providing practical hands-on experience in applying Dataflow.

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Identify a use case for Dataflow, such as data transformation, stream processing, or data analysis
Design the pipeline architecture, including data sources, transformations, and sinks
Implement the pipeline using Apache Beam, Java, or Python
Deploy and test the pipeline on Google Cloud
Monitor the pipeline and make necessary adjustments

Attend a meetup or conference on big data

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Expand your knowledge and connect with professionals in the big data field.

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Register and attend the event.
Identify meetups or conferences related to big data.
Engage in discussions and ask questions to industry experts.
Network with other attendees and exchange insights.

Start a side project to apply Dataflow in a personal or professional context

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Apply your Dataflow skills to a real-world project, solidifying your understanding and building a valuable portfolio piece.

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Identify a problem or opportunity where Dataflow can add value
Design a solution using Dataflow and other relevant technologies
Implement the solution and deploy it on Google Cloud
Monitor and evaluate the project's performance
Write a project report or present your findings to others

Contribute to an open-source dataflow project

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Gain practical experience and deepen your understanding by contributing to an open-source dataflow project.

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Identify open-source dataflow projects.
Choose a project that aligns with your interests and skills.
Review the project's documentation and contribution guidelines.
Identify an area where you can contribute.
Make your contributions and submit them for review.

Build a Dataflow pipeline to solve a real-world problem

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Apply your knowledge of Dataflow pipelines to solve a specific data processing challenge, solidifying your understanding.

Browse courses on Google Dataflow

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Identify a real-world problem that can be addressed using a Dataflow pipeline.
Design a Dataflow pipeline architecture to solve the problem.
Implement the pipeline using the Apache Beam SDK.
Deploy the pipeline to the cloud and evaluate its performance.

Contribute to the Apache Beam community

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Engage with the Apache Beam community by contributing to its open-source ecosystem, deepening your understanding and expanding your network.

Browse courses on Apache Beam

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Visit the Apache Beam GitHub repository.
Review the contribution guidelines and find an area where you can make a contribution.
Submit a pull request with your proposed contribution.
Collaborate with other contributors to refine and merge your contribution.

Career center

Learners who complete Architecting Serverless Big Data Solutions Using Google Dataflow will develop knowledge and skills that may be useful to these careers:

Data Engineer

Data Engineers are responsible for building and maintaining the infrastructure that supports large-scale data processing and analysis. They design, implement, and manage data pipelines that move data between different systems and applications. With this course on Architecting Serverless Big Data Solutions Using Google Dataflow, aspiring Data Engineers can gain a deep understanding of how to use Dataflow to build and manage serverless data pipelines, which can be a valuable skill in this role.

See salaries and explore the career path for Data Engineer

Data Architect

Data Architects design and manage the overall data architecture for an organization, ensuring that data is managed and used effectively. They work with business stakeholders to understand their data needs and develop solutions that meet those needs. The Architecting Serverless Big Data Solutions Using Google Dataflow course can provide Data Architects with the knowledge and skills they need to design and implement data pipelines using Dataflow, which can be a valuable skill in this role.

See salaries and explore the career path for Data Architect

Data Analyst

Data Analysts use data to solve business problems and make informed decisions. They collect, clean, and analyze data to identify trends and patterns. The Architecting Serverless Big Data Solutions Using Google Dataflow course can provide Data Analysts with the skills they need to use Dataflow to process and analyze large datasets, which can be a valuable skill in this role.

See salaries and explore the career path for Data Analyst

Software Engineer

Software Engineers design, develop, and maintain software applications. They work with business stakeholders to understand their needs and develop solutions that meet those needs. The Architecting Serverless Big Data Solutions Using Google Dataflow course can provide Software Engineers with the skills they need to use Dataflow to build and manage data pipelines, which can be a valuable skill in this role.

See salaries and explore the career path for Software Engineer

Data Scientist

Data Scientists use data to solve complex business problems. They develop and apply machine learning and statistical models to data to identify patterns and trends. The Architecting Serverless Big Data Solutions Using Google Dataflow course can provide Data Scientists with the skills they need to use Dataflow to process and analyze large datasets, which can be a valuable skill in this role.

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Cloud Architect

Cloud Architects design and manage cloud computing infrastructure. They work with customers to understand their needs and develop solutions that meet those needs. The Architecting Serverless Big Data Solutions Using Google Dataflow course can provide Cloud Architects with the skills they need to use Dataflow to build and manage data pipelines in the cloud, which can be a valuable skill in this role.

See salaries and explore the career path for Cloud Architect

DevOps Engineer

DevOps Engineers work to bridge the gap between development and operations teams. They ensure that software is developed and deployed efficiently and reliably. The Architecting Serverless Big Data Solutions Using Google Dataflow course can provide DevOps Engineers with the skills they need to use Dataflow to build and manage data pipelines, which can be a valuable skill in this role.

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Business Analyst

Business Analysts work with business stakeholders to understand their needs and develop solutions that meet those needs. They may also be involved in the design and implementation of software applications. The Architecting Serverless Big Data Solutions Using Google Dataflow course can provide Business Analysts with the skills they need to use Dataflow to analyze data and develop solutions that meet business needs.

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Project Manager

Project Managers plan and execute projects. They work with stakeholders to define project goals, develop project plans, and manage project resources. The Architecting Serverless Big Data Solutions Using Google Dataflow course can provide Project Managers with the skills they need to use Dataflow to manage data-driven projects.

See salaries and explore the career path for Project Manager

Data Quality Analyst

Data Quality Analysts ensure that data is accurate, complete, and consistent. They develop and implement data quality standards and procedures. The Architecting Serverless Big Data Solutions Using Google Dataflow course can provide Data Quality Analysts with the skills they need to use Dataflow to analyze data quality and develop data quality solutions.

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Data Governance Specialist

Data Governance Specialists develop and implement data governance policies and procedures. They work with stakeholders to ensure that data is used in a responsible and ethical manner. The Architecting Serverless Big Data Solutions Using Google Dataflow course can provide Data Governance Specialists with the skills they need to use Dataflow to manage data governance.

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Database Administrator

Database Administrators manage and maintain databases. They ensure that databases are running smoothly and that data is protected. The Architecting Serverless Big Data Solutions Using Google Dataflow course may be useful for Database Administrators who want to learn how to use Dataflow to manage data pipelines.

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Systems Administrator

Systems Administrators manage and maintain computer systems. They ensure that systems are running smoothly and that data is protected. The Architecting Serverless Big Data Solutions Using Google Dataflow course may be useful for Systems Administrators who want to learn how to use Dataflow to manage data pipelines.

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IT Manager

IT Managers plan and direct the activities of an organization's IT department. They develop and implement IT policies and procedures and manage IT resources. The Architecting Serverless Big Data Solutions Using Google Dataflow course may be useful for IT Managers who want to learn how to use Dataflow to manage data pipelines.

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Web Developer

Web Developers design and develop websites and web applications. They work with designers and other stakeholders to create websites that are both visually appealing and functional. While the Architecting Serverless Big Data Solutions Using Google Dataflow course is not directly related to web development, it may be useful for Web Developers who want to learn how to use Dataflow to analyze website data.

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