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Data Engineering on Google Cloud Platform (DEGCP)


Course Overview

This four-day instructor-led class provides participants a hands-on introduction to designing and building data processing systems on Google Cloud Platform. Through a combination of presentations, demos, and hand-on labs, participants will learn how to design data processing systems, build end-to-end data pipelines, analyze data and carry out machine learning. The course covers structured, unstructured, and streaming data.

Who should attend

This class is intended for experienced developers who are responsible for managing big data transformations including:

  • Extracting, Loading, Transforming, cleaning, and validating data
  • Designing pipelines and architectures for data processing
  • Creating and maintaining machine learning and statistical models
  • Querying datasets, visualizing query results and creating reports


To get the most of out of this course, participants should have:

  • Completed Google Cloud Fundamentals: Big Data and Machine Learning (GCF-BDM) course OR have equivalent experience
  • Basic proficiency with common query language such as SQL
  • Experience with data modeling, extract, transform, load activities Developing applications using a common programming language such Python
  • Familiarity with Machine Learning and/or statistics

Course Objectives

This course teaches participants the following skills:

  • Design and build data processing systems on Google Cloud Platform
  • Process batch and streaming data by implementing autoscaling data pipelines on Cloud Dataflow
  • Derive business insights from extremely large datasets using Google BigQuery
  • Train, evaluate and predict using machine learning models using Tensorflow and Cloud ML
  • Leverage unstructured data using Spark and ML APIs on Cloud Dataproc
  • Enable instant insights from streaming data

Course Content

Module 1: Google Cloud Dataproc Overview
  • Creating and managing clusters.
  • Leveraging custom machine types and preemptible worker nodes.
  • Scaling and deleting Clusters.
  • Lab: Creating Hadoop Clusters with Google Cloud Dataproc.
Module 2: 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.
Module 3: Integrating Dataproc with Google Cloud Platform
  • Customize cluster with initialization actions.
  • BigQuery Support.
  • Lab: Leveraging Google Cloud Platform Services.
Module 4: 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.
Module 5: 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.
Module 6: 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.
Module 7: Getting started with Machine Learning
  • What is machine learning (ML).
  • Effective ML: concepts, types.
  • ML datasets: generalization.
  • Lab: Explore and create ML datasets.
Module 8: 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.
Module 9: 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.
Module 10: Feature Engineering
  • Creating good features.
  • Transforming inputs.
  • Synthetic features.
  • Preprocessing with Cloud ML.
  • Lab: Feature engineering.
Module 11: Architecture of streaming analytics pipelines
  • Stream data processing: Challenges.
  • Handling variable data volumes.
  • Dealing with unordered/late data.
  • Lab: Designing streaming pipeline.
Module 12: Ingesting Variable Volumes
  • What is Cloud Pub/Sub?
  • How it works: Topics and Subscriptions.
  • Lab: Simulator.
Module 13: Implementing streaming pipelines
  • Challenges in stream processing.
  • Handle late data: watermarks, triggers, accumulation.
  • Lab: Stream data processing pipeline for live traffic data.
Module 14: 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.
Module 15: High throughput and low-latency with Bigtable
  • What is Cloud Spanner?
  • Designing Bigtable schema.
  • Ingesting into Bigtable.
  • Lab: streaming into Bigtable.
Classroom Training

Duration 4 days

  • Singapore: 3,450.- SGD
  • India: US$ 2,300.-
Online Training

Duration 4 days

Click on town name to book Schedule
Asia Pacific
04/11/2019 - 07/11/2019 Singapore Enroll

Fast Lane Flex™ Classroom If you can't find a suitable date, don't forget to check our world-wide FLEX™ training schedule.

07/10/2019 - 10/10/2019 Hamburg Enroll
15/10/2019 - 18/10/2019 Stuttgart Enroll
04/11/2019 - 07/11/2019 Munich Enroll
12/11/2019 - 15/11/2019 Berlin Enroll
26/11/2019 - 29/11/2019 Düsseldorf Enroll
10/12/2019 - 13/12/2019 Frankfurt Enroll
14/01/2020 - 17/01/2020 Munich Enroll
28/01/2020 - 31/01/2020 Berlin Enroll
04/11/2019 - 07/11/2019 Vienna (iTLS) Enroll
21/04/2020 - 24/04/2020 Vienna (iTLS) Enroll
17/12/2019 - 20/12/2019 Brussels Course language: English Enroll
14/10/2019 - 17/10/2019 Sofia This is an English language FLEX course.   Time zone: Europe/Sofia Course language: English Enroll
17/12/2019 - 20/12/2019 Paris Enroll
12/11/2019 - 15/11/2019 Rome Course language: English Enroll
10/12/2019 - 13/12/2019 Milan Course language: English Enroll
29/10/2019 - 01/11/2019 Utrecht Course language: English Enroll
05/11/2019 - 08/11/2019 Lisbon Enroll
02/12/2019 - 05/12/2019 Bucharest This is an English language FLEX course.   Time zone: Europe/Bucharest Course language: English Enroll
22/10/2019 - 25/10/2019 Madrid Enroll
10/12/2019 - 13/12/2019 Zurich Enroll
11/02/2020 - 14/02/2020 Zurich Enroll
18/08/2020 - 21/08/2020 Zurich Enroll
United Kingdom
19/11/2019 - 22/11/2019 London, City This is an English language FLEX course.   Time zone: Europe/London £ 2,195.- Enroll
04/02/2020 - 07/02/2020 London, City This is an English language FLEX course.   Time zone: Europe/London £ 2,195.- Enroll
12/05/2020 - 15/05/2020 London, City This is an English language FLEX course.   Time zone: Europe/London £ 2,195.- Enroll
04/08/2020 - 07/08/2020 London, City This is an English language FLEX course.   Time zone: Europe/London £ 2,195.- Enroll
North America
United States
22/10/2019 - 25/10/2019 Online Training Time zone: US/Central Course language: English Enroll
17/12/2019 - 20/12/2019 Online Training Time zone: US/Eastern Course language: English Enroll
22/10/2019 - 25/10/2019 Online Training Time zone: Canada/Central Course language: English Enroll
Latin America
10/12/2019 - 13/12/2019 Online Training Time zone: America/Buenos_Aires Course language: Spanish Enroll
03/12/2019 - 06/12/2019 Online Training Time zone: America/Sao_Paulo Course language: Portuguese Enroll
05/11/2019 - 08/11/2019 Online Training Time zone: America/Lima Course language: Spanish Enroll
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This is a FLEX course, which is delivered both virtually and in the classroom.