Data Engineering on Google Cloud Platform

GCP-126 (4 Days)
Request Pricing for Data Engineering on Google Cloud Platform

Data Engineering on GCP Training Overview

This Data Engineering on Google Cloud Platform training course teaches attendees how to design data processing systems, build end-to-end data pipelines, analyze data, and carry out machine learning. This GCP course covers structured, unstructured, and streaming data.

Location and Pricing

This course is taught as a private online class for teams of 3 or more. All our private online courses are hands-on, instructor-led, and customizable to fit your group’s goals and needs. To receive a customized proposal and price quote, please contact us.

Data Engineering on GCP Training Objectives

All students will learn how to:

  • Design and build data processing systems on Google Cloud
  • 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

Data Engineering on GCP Training Outline

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Introduction
Google Cloud Dataproc Overview
  • Creating and managing clusters.
  • Leveraging custom machine types and preemptible worker nodes.
  • Scaling and deleting Clusters.
Running Dataproc Jobs
  • Running Pig and Hive jobs.
  • Separation of storage and compute.
Integrating Dataproc with Google Cloud Platform
  • Customize cluster with initialization actions.
  • BigQuery Support.
Making Sense of Unstructured Data with Google’s Machine Learning APIs
  • Google’s Machine Learning APIs
  • Common ML Use Cases
  • Invoking ML APIs
  • Serverless Data Analysis with Google BigQuery and Cloud Dataflow
Serverless Data Analysis with BigQuery
  • What is BigQuery
  • Queries and Functions
  • Loading data into BigQuery
  • Exporting data from BigQuery
  • Nested and repeated fields
  • Querying multiple tables
  • Performance and pricing
Serverless, Autoscaling Data Pipelines with Dataflow
  • The Beam programming model
  • Data pipelines in Beam Python
  • Data pipelines in Beam Java
  • Scalable Big Data processing using Beam
  • Incorporating additional data
  • Handling stream data
  • GCP Reference architecture
  • Serverless Machine Learning with TensorFlow on Google Cloud Platform
Getting Started with Machine Learning
  • What is machine learning (ML)
  • Effective ML: concepts, types
  • ML datasets: generalization
Building ML Models with Tensorflow
  • Getting started with TensorFlow
  • TensorFlow graphs and loops + lab
  • Monitoring ML training
Scaling ML Models with CloudML
  • Why Cloud ML?
  • Packaging up a TensorFlow model
  • End-to-end training
Feature Engineering
  • Creating good features
  • Transforming inputs
  • Synthetic features
  • Preprocessing with Cloud ML
  • Building Resilient Streaming Systems on Google Cloud Platform
Architecture of Streaming Analytics Pipelines
  • Stream data processing: Challenges
  • Handling variable data volumes
  • Dealing with unordered/late data
Ingesting Variable Volumes
  • What is Cloud Pub/Sub?
  • How it works: Topics and Subscriptions
Implementing Streaming Pipelines
  • Challenges in stream processing.
  • Handle late data: watermarks, triggers, accumulation.
Streaming Analytics and Dashboards
  • Streaming analytics: from data to decisions
  • Querying streaming data with BigQuery
  • What is Google Data Studio?
High Throughput and Low-Latency with Bigtable
  • What is Cloud Spanner?
  • Designing Bigtable schema
  • Ingesting into Bigtable
Conclusion
Request Pricing for Data Engineering on Google Cloud Platform
Lecture percentage

50%

Lecture/Demo

Lab percentage

50%

Lab

Course Number:

GCP-126

Duration:

4 Days

Prerequisites:

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

Training Materials:

All GCP training students receive comprehensive courseware.

Software Requirements:

Students must have the Google Chrome web browser and Internet access.

Contact Us:

Accelebrate’s training classes are available for private groups of 3 or more people at your site or online anywhere worldwide.

Don't settle for a "one size fits all" public class! Have Accelebrate deliver exactly the training you want, privately at your site or online, for less than the cost of a public class.

For pricing and to learn more, please contact us.

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