Cloud Technology training: Data Engineering on Google Cloud Platform

Become a Professional Data Engineer on GCP. Design data processing systems, build end-to-end data pipelines, analyze data and carry out machine learning. This course is part of Google's Data Engineering track that leads to the Professional Data Engineer certificate.

Become a GCP Data Engineer

Are you a Developer responsible for managing Big Data transformations? Do you want to become a Professional on the Google Cloud Platform? Time to gain the knowledge and skills to prepare for your Google certificate. This training prepares you to design and build data processing systems on the Google Cloud Platform. You will learn how to analyze data and carry out Machine Learning. We cover structured, unstructured, and streaming data.

Data Engineering on GCP is perfect for 

Join us if you are an experienced Developer responsible for extracting, loading, transforming, cleaning, and validating data, and designing pipelines and architectures for data processing. If you are responsible for creating and maintaining Machine Learning and statistical models, querying datasets, visualizing query results, and creating reports, you're more than welcome to join too.

Before enrolling, you need to complete the Google Cloud Fundamentals: Big Data & Machine Learning course, or have basic proficiency with: a common query language like SQL, data modeling, extracting, transforming, and loading activities, developing applications using a programming language like Python, and Machine Learning and/or statistics

What will you learn during the Data Engineering training? 

This 4-day training offers a combination of presentations, demos, and hands-on labs. You will learn how to design data processing systems, build end-to-end data pipelines, analyze data, and carry out Machine Learning.


Google Cloud Dataproc Overview

  • Creating and managing clusters.
  • Leverage custom machine types and preemptible worker nodes.
  • Scaling and deleting Clusters.
  • Lab: Creating Hadoop Clusters with Google Cloud Dataproc.

Running Dataproc Jobs

  • Running Pig and Hive jobs.
  • Separation of storage and compute.
  • Lab: Running Hadoop and Spark Jobs with Dataproc.
  • Lab: Submit and monitor jobs.

Integrating Dataproc with Google Cloud Platform

  • Customize clusters with initialization actions.
  • BigQuery Support.
  • Lab: Leveraging Google Cloud Platform Services.

Making Sense of Unstructured Data with Google’s Machine Learning APIs

  • Google’s Machine Learning APIs.
  • Common ML Use Cases.
  • Invoking ML APIs.
  • Lab: Adding Machine Learning Capabilities to Big Data Analysis.

Serverless data analysis with BigQuery

  • What is BigQuery?
  • Queries and Functions.
  • Lab: Writing queries in BigQuery.
  • Loading data into BigQuery.
  • Exporting data from BigQuery.
  • Lab: Loading and exporting data.
  • Nested and repeated fields.
  • Querying multiple tables.
  • Lab: Complex queries.
  • Performance and pricing.

Serverless, autoscaling data pipelines with Dataflow

  • The Beam programming model.
  • Data pipelines in Beam Python.
  • Data pipelines in Beam Java.
  • Lab: Writing a Dataflow pipeline.
  • Scalable Big Data processing using Beam.
  • Lab: MapReduce in Dataflow.
  • Incorporating additional data.
  • Lab: Side inputs.
  • Handling stream data.
  • GCP Reference architecture.

Getting started with Machine Learning

  • What is machine learning (ML).
  • Effective ML: concepts, types.
  • ML datasets: generalization.
  • Lab: Explore and create ML datasets.

Building ML models with Tensorflow

  • Getting started with TensorFlow.
  • Lab: Using tf.learn.
  • TensorFlow graphs and loops + lab.
  • Lab: Using low-level TensorFlow + early stopping.
  • Monitoring ML training.
  • Lab: Charts and graphs of TensorFlow training.

Scaling ML models with CloudML

  • Why Cloud ML?
  • Packaging up a TensorFlow model.
  • End-to-end training.
  • Lab: Run a ML model locally and on cloud.

Feature Engineering

  • Creating good features.
  • Transforming inputs.
  • Synthetic features.
  • Preprocessing with Cloud ML.
  • Lab: Feature engineering.

Architecture of streaming analytics pipelines

  • Stream data processing: Challenges.
  • Handling variable data volumes.
  • Dealing with unordered/late data.
  • Lab: Designing streaming pipeline.

Ingesting Variable Volumes

  • What is Cloud Pub/Sub?
  • How it works: Topics and Subscriptions.
  • Lab: Simulator.

Implementing streaming pipelines

  • Challenges in stream processing.
  • Handle late data: watermarks, triggers, accumulation.
  • Lab: Stream data processing pipeline for live traffic data.

Streaming analytics and dashboards

  • Streaming analytics: from data to decisions.
  • Querying streaming data with BigQuery.
  • What is Google Data Studio?
  • Lab: build a real-time dashboard to visualize processed data.

High throughput and low-latency with Bigtable

  • What is Cloud Spanner?
  • Designing Bigtable schema.
  • Ingesting into Bigtable.
  • Lab: streaming into Bigtable.

GCP Trainers

This Cloud Technology training is brought to you by develops Cloud-Based Solutions, trains and coaches teams, and provides Managed Cloud Services. is Google Cloud Authorized Training Partner, and your trainer is a real Cloud guru who enjoys sharing his or her experiences to help you get the most out of GCP.

Google Certification

Our Google Cloud Platform training courses all follow the curriculum drawn up by Google. The Data Engineering on Google Cloud Platform training gives you in-depth knowledge and a Professional level understanding of processing and analyzing data on GCP. This training prepares you for Google's Professional Data Engineer exam and certificate. Google does recommend you have 3+ years of industry experience, including 1+ years designing and managing solutions using GCP before taking the exam. The exam is not included in this training and more information about the exam and how to buy/plan it can be found here.

Google Cloud Platform Learning Journey

Your GCP Data Engineering Learning Journey starts with a Foundation level training. Once you have the basics in place, you can select the Certified Google Cloud Platform track that best fits your professional ambitions. This training is an essential step on your journey to becoming a Google Cloud Professional Data Engineer. If you are just getting started, the Foundation level training you need for the Data Engineer track is Google Cloud Platform Fundamentals: Big Data and Machine Learning

Yes, I want to become a Professional Data Engineer

After registering for this training, you will receive a confirmation email with practical information. A week before the training, we will ask you about any dietary requirements. See you soon!

What else should I know?

  • Literature and a delicious lunch are included in the price of the training
  • Travel and accommodation expenses are not included
  • You need to bring your laptop to this training

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