Harness the Power of Serverless Data Processing with Google Cloud Dataflow

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Serverless Data Processing with Dataflow Course Overview

This course is designed to provide you with a comprehensive understanding of serverless data processing using Apache Dataflow. You will learn the ins and outs of designing and building data pipelines as well as configuring and monitoring them. The course will provide examples of real-world use cases, including streaming analytics, end-to-end data processing and managing large data sets. Additionally, students will explore the features and capabilities of the open-source service Apache Beam.
Throughout the course, students will gain the necessary skills to design and develop their own serverless data processing solutions. You’ll be able to process large datasets from multiple sources, such as batch processing, streaming data and backtesting, to explore and analyze data. Through practical applications and skill-building labs, this course will provide you with the foundational knowledge and skills needed to become a successful data scientist.

This is a Rare Course and it can be take up to 3 weeks to arrange the training.


The 1-on-1 Advantage


Flexible Dates

  • • Choose Start Date
  • • Reschedule After Booking
  • • Weekend / Evening Option

4-Hour Sessions

You will learn:

Module 1: Introduction
  • Introduce the course objectives
  • Demonstrate how Apache Beam and Dataflow work together to fulfill your organization’s data processing needs
  • Summarize the benefits of the Beam Portability Framework
  • Customize the data processing environment of your pipeline using custom containers
  • Review use cases for cross-language transformations.
  • Enable the Portability framework for your Dataflow pipelines.
  • Enable Shuffle and Streaming Engine, for batch and streaming pipelines respectively, for maximum performance.
  • Enable Flexible Resource Scheduling for more cost-efficient performance.
  • Select the right combination of IAM permissions for your Dataflow job.
  • Determine your capacity needs by inspecting the relevant quotas for your Dataflow jobs
  • Select your zonal data processing strategy using Dataflow, depending on your data locality needs.
  • Implement best practices for a secure data processing environment
  • Review main Apache Beam concepts (Pipeline, PCollections, PTransforms, Runner, reading/writing, Utility PTransforms, side inputs), bundles and DoFn Lifecycle
  • Implement logic to handle your late data.
  • Review different types of triggers.
  • Review core streaming concepts (unbounded PCollections, windows)
  • Write the I/O of your choice for your Dataflow pipeline.
  • Tune your source/sink transformation for maximum performance.
  • Create custom sources and sinks using SDF.
  • Introduce schemas, which give developers a way to express structured data in their Beam pipelines.
  • Use schemas to simplify your Beam code and improve the performance of your pipeline.
  • Identify use cases for state and timer API implementations.
  • Select the right type of state and timers for your pipeline.
  • Implement best practices for Dataflow pipelines.
  • Develop a Beam pipeline using SQL and DataFrames.
  • Prototype your pipeline in Python using Beam notebooks.
  • Use Beam magics to control the behavior of source recording in your notebook.
  • Launch a job to Dataflow from a notebook.
  • Navigate the Dataflow Job Details UI.
  • Interpret Job Metrics charts to diagnose pipeline regressions.
  • Set alerts on Dataflow jobs using Cloud Monitoring.
  • Use the Dataflow logs and diagnostics widgets to troubleshoot pipeline issues
  • Use a structured approach to debug your Dataflow pipelines.
  • Examine common causes for pipeline failures.
  • Understand performance considerations for pipelines
  • Consider how the shape of your data can affect pipeline performance
  • Testing approaches for your Dataflow pipeline
  • Review frameworks and features available to streamline your CI/CD workflow for Dataflow pipelines.
  • Implement reliability best practices for your Dataflow pipelines.
  • Using flex templates to standardize and reuse Dataflow pipeline code.
Live Online Training (Duration : 24 Hours) Fee On Request
We Offer :
  • 1-on-1 Public - Select your own start date. Other students can be merged.
  • 1-on-1 Private - Select your own start date. You will be the only student in the class.

4 Hours
8 Hours
Week Days

Start Time : At any time

12 AM
12 PM

1-On-1 Training is Guaranteed to Run (GTR)
Group Training
Date On Request
Course Prerequisites

No, there are no prerequisites for Serverless Data Processing with Dataflow Training. However, it is recommended that you have a basic understanding of software engineering concepts and some hands-on experience with orchestration of Data Pipelines. Having background knowledge on Apache Beam, Cloud Dataflow and/or Google Cloud Platform would be a plus.

Target Audience

The target audience for a Serverless Data Processing with Dataflow training course would include developers, cloud engineers, and data scientists who are looking to increase their knowledge on serverless data processing with Google Cloud Platform
The course would be beneficial for those professionals who have a basic understanding of cloud computing, basic computing language, and web technologies
It would also be helpful for professionals who are interested in learning new technologies and working with Big Data
The training would give them the necessary skills to work with data flows such as processing large volumes of data using Dataflow, monitoring and controlling pipelines and their execution, or enabling scale-out of their data processing pipelines
Additionally, the course could provide insights into designing, building and managing production-level data pipelines with a focus on cost-efficiency

Learning Objectives of Serverless Data Processing with Dataflow

1. Understand the concepts of serverless data processing and why dataflow is considered an effective solution.
2. Learn approaches to build end-to-end serverless data processing solutions with Dataflow.
3. Establish the ability to develop stateless and stateful serverless pipelines of Dataflow.
4. Gain understanding of the concepts of Creating Triggers and Publishing Messages
5. Get proficient with Structured Data Streams in Dataflow.
6. Learn approaches for Building Streaming Pipelines with Stateless and Stateful Processors.
7. Flaunt the capability of Monitoring and Debugging Dataflow Jobs.
8. Gain proficiency with using Text Parsing and ML Transformations.
9. Ability to use various Google Cloud services with Dataflow.
10. Set a clear understanding of scaling pipelines and using advanced configurations.

Student Feedback  (Check Koenig Feedback on Trustpilot)

Q1 Say something about the Trainer? Q2 How is Koenig different from other training Companies? Q3 Will you come back to Koenig for training ?

Student Name Feedback
United States
A1. Yes, she was very clear & a strong mastery of both English & Azure platforms hence following was easy. She was also patient and was answering every question asked, she also shared material & tips on how to pass the exam, Well done, and keep it up.
Nick Barker
United States
A1. Calm and understanding, she not only kept the course running well but also took time to help those with lots of questions. Very well done.
Robin Johnson
United States
A2. Individual attention during training, excellent customer service and coordination. Neha has been ever so helpful and is definitely an asset to Koenig.
Shivani Prasad
United States
A1. The trainer was great and his teaching methods were incredible. He used different teaching methods to ensure I was learning well. A fun trainer with lots of patience and understanding.
United States
A1. The trainer was very knowledgeable and well prepared about the presentation/training subject, with a positive attitude towards students and making every effort in answering questions during the training session. Top-notch service!
United States
A1. Exceptional... recommend to everyone!
Shyam Vaddakapett
United States
A1. My trainer, Mohit Kakkar was excellent as he focused on hands-on assignments to supplement the concepts of AZ 900. The training coordinator Jasmeet as well as the ops manager Sheshadri are also very professional and customer-centric. I recommend Koenig for training courses, and I will take some more courses with them soon.
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United States
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Abdulrahman Saad Alshahrani
United States
A1. Excellent trainer. I will take another course with him someday. He deserves a good appreciation from his Manager.
United States

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1-on-1 Public - Select your start date. Other students can be merged.
1-on-1 Private - Select your start date. You will be the only student in the class.
Yes, course requiring practical include hands-on labs.
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Schedule for Group Training is decided by Koenig. Schedule for 1-on-1 is decided by you.
In 1-on-1 you can select your own schedule, other students can be merged but you select the schedule. Choose 1-on-1 if published schedule do not meet your requirement. If you also want a private session, opt for 1-on-1 Public.
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