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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.

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.

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

Prerequisites

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
Classroom Training

Duration 4 days

Price
  • United States: US$ 2,495
Online Training

Duration 4 days

Price
  • United States: US$ 2,495
 
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This is an Instructor-Led Classroom course
This is an Instructor-Led Online (ILO) course. These sessions are conducted via WebEx in a VoIP environment and require an Internet Connection and headset with microphone connected to your computer or laptop.
This is a FLEX course, which is delivered simultaneously in two modalities. Choose to attend the Instructor-Led Online (ILO) virtual session or Instructor-Led Classroom (ILT) session.
United States
Dec 17-20, 2019 Online Training 09:30 US/Eastern Enroll
Jan 21-24, 2020 Online Training 09:30 US/Central Enroll
Apr 28-May 1, 2020 Online Training 09:30 US/Central Enroll
Aug 11-14, 2020 Online Training 09:30 US/Central Enroll
Nov 10-13, 2020 Online Training 09:30 US/Central Enroll
Canada
Jan 21-24, 2020 Online Training 09:30 Canada/Central Enroll
Apr 28-May 1, 2020 Online Training 09:30 Canada/Central Enroll
Aug 11-14, 2020 Online Training 09:30 Canada/Central Enroll
Nov 10-13, 2020 Online Training 09:30 Canada/Central Enroll

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

Europe
Germany
Nov 12-15, 2019 Berlin Enroll
Nov 26-29, 2019 Düsseldorf Enroll
Dec 10-13, 2019 Frankfurt Enroll
Jan 14-17, 2020 Munich Enroll
Jan 28-31, 2020 Berlin Enroll
Feb 11-14, 2020 Frankfurt Enroll
Feb 25-28, 2020 Hamburg Enroll
Mar 10-13, 2020 Düsseldorf Enroll
Mar 24-27, 2020 Stuttgart Enroll
Apr 21-24, 2020 Munich Enroll
Austria
Nov 4-7, 2019 Vienna (iTLS) Enroll
Apr 21-24, 2020 Vienna (iTLS) Enroll
Oct 13-16, 2020 Vienna (iTLS) Enroll
Belgium
Dec 17-20, 2019 Brussels Course language: English Enroll
Czech Republic
Nov 9-12, 2020 This is a FLEX event Prague Course language: English Enroll
Online Training Time zone: Europe/Prague Enroll
Italy
Nov 12-15, 2019 Rome Course language: English Enroll
Dec 10-13, 2019 Milan Course language: English Enroll
Portugal
Nov 5-8, 2019 Lisbon Enroll
Romania
Dec 2-5, 2019 This is a FLEX event Bucharest Course language: English Enroll
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Mar 23-26, 2020 This is a FLEX event Bratislava Enroll
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Sep 22-25, 2020 This is a FLEX event Bratislava Enroll
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Spain
Oct 22-25, 2019 Madrid Enroll
Switzerland
Dec 10-13, 2019 Zurich Enroll
Feb 11-14, 2020 Zurich Enroll
Aug 18-21, 2020 Zurich Enroll
Turkey
Feb 3-6, 2020 This is a FLEX event Istanbul Course language: English Enroll
Online Training Time zone: Asia/Istanbul Enroll
United Kingdom
Feb 4-7, 2020 This is a FLEX event London, City Enroll
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May 12-15, 2020 This is a FLEX event London, City Enroll
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Aug 4-7, 2020 This is a FLEX event London, City Enroll
Online Training Time zone: Europe/London Enroll
Nov 3-6, 2020 This is a FLEX event London, City Enroll
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Latin America
Argentina
Dec 10-13, 2019 Online Training Time zone: America/Buenos_Aires Enroll
Brazil
Dec 3-6, 2019 Online Training Time zone: America/Sao_Paulo Enroll
Peru
Nov 5-8, 2019 Online Training Time zone: America/Lima Course language: Spanish Enroll
Asia Pacific
India
Oct 28-31, 2019 This is a FLEX event Bangalore Enroll
Online Training Time zone: Asia/Calcutta Enroll