Google Cloud Data Engineer

Live Online (VILT) & Classroom Corporate Training Course

This GCP course covers structured, unstructured, and streaming data.

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Google Cloud Data Engineer

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.

Objectives

At the end of Google Data Engineer training course, participants will be able 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

Prerequisites

  • 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

Course Outline

Google Cloud Dataproc Overview2021-06-26T18:08:10+05:30
  • Creating and managing clusters.
  • Leveraging custom machine types and preemptible worker nodes
  • Scaling and deleting Clusters
Running Dataproc Jobs2021-06-26T18:09:54+05:30
  • Running Pig and Hive jobs.
  • Separation of storage and compute.
Integrating Dataproc with Google Cloud Platform2021-06-26T18:10:03+05:30
  • Customize cluster with initialization actions.
  • BigQuery Support.
Making Sense of Unstructured Data with Google’s Machine Learning APIs2021-06-26T18:10:09+05:30
  • 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 BigQuery2021-06-26T18:10:19+05:30
  • 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 Dataflow2021-06-26T18:10:36+05:30
  • 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 Learning2021-06-26T18:12:04+05:30
  • What is machine learning (ML)
  • Effective ML: concepts, types
  • ML datasets: generalization
Building ML Models with Tensorflow2021-06-26T18:12:12+05:30
  • Getting started with TensorFlow
  • TensorFlow graphs and loops + lab
  • Monitoring ML training
Scaling ML Models with CloudML2021-06-26T18:12:24+05:30
  • Why Cloud ML?
  • Packaging up a TensorFlow model
  • End-to-end training
Feature Engineering2021-06-26T18:12:32+05:30
  • Creating good features
  • Transforming inputs
  • Synthetic features
  • Preprocessing with Cloud ML
  • Building Resilient Streaming Systems on Google Cloud Platform
Architecture of Streaming Analytics Pipelines2021-06-26T18:12:43+05:30
  • Stream data processing: Challenges
  • Handling variable data volumes
  • Dealing with unordered/late data
Ingesting Variable Volumes2021-06-26T18:13:05+05:30
  • What is Cloud Pub/Sub?
  • How it works: Topics and Subscriptions
Implementing Streaming Pipelines2021-06-26T18:13:40+05:30
  • Challenges in stream processing.
  • Handle late data: watermarks, triggers, accumulation.
Streaming Analytics and Dashboards2021-06-26T18:13:47+05:30
  • Streaming analytics: from data to decisions
  • Querying streaming data with BigQuery
  • What is Google Data Studio?
High Throughput and Low-Latency with Bigtable2021-06-26T18:14:14+05:30
  • What is Cloud Spanner?
  • Designing Bigtable schema
  • Ingesting into Bigtable
2022-01-23T17:08:42+05:30

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