Data Engineering on Google Cloud Platform

COURSE OUTLINE:

Description

This four-day instructor-led class provides you with 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, you 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.

Audience

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

  • Completed Google Cloud Fundamentals- Big Data and Machine Learning course #8325 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

Learning Objectives

  • 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

1. Serverless Data Analysis with BigQuery

  • What is BigQuery
  • Advanced Capabilities
  • Performance and pricing

2. Serverless, Autoscaling Data Pipelines with Dataflow

3. Getting Started with Machine Learning

  • What is machine learning (ML)
  • Effective ML: concepts, types
  • Evaluating ML
  • ML datasets: generalization

4. Building ML Models with Tensorflow

  • Getting started with TensorFlow
  • TensorFlow graphs and loops + lab
  • Monitoring ML training

5. Scaling ML Models with CloudML

  • Why Cloud ML?
  • Packaging up a TensorFlow model
  • End-to-end training

6. Feature Engineering

  • Creating good features
  • Transforming inputs
  • Synthetic features
  • Preprocessing with Cloud ML

7. ML Architectures

  • Wide and deep
  • Image analysis
  • Embeddings and sequences
  • Recommendation systems

8. Google Cloud Dataproc Overview

  • Introducing Google Cloud Dataproc
  • Creating and managing clusters
  • Defining master and worker nodes
  • Leveraging custom machine types and preemptible worker nodes
  • Creating clusters with the Web Console
  • Scripting clusters with the CLI
  • Using the Dataproc REST API
  • Dataproc pricing
  • Scaling and deleting Clusters

9.�Running Dataproc Jobs

  • Controlling application versions
  • Submitting jobs
  • Accessing HDFS and GCS
  • Hadoop
  • Spark and PySpark
  • Pig and Hive
  • Logging and monitoring jobs
  • Accessing onto master and worker nodes with SSH
  • Working with PySpark REPL (command-line interpreter)

10.�Integrating Dataproc with Google Cloud Platform

  • Initialization actions
  • Programming Jupyter/Datalab notebooks
  • Accessing Google Cloud Storage
  • Leveraging relational data with Google Cloud SQL
  • Reading and writing streaming Data with Google BigTable
  • Querying Data from Google BigQuery
  • Making Google API Calls from notebooks

11.�Making Sense of Unstructured Data with Google�s Machine Learning APIs

  • Google�s Machine Learning APIs
  • Common ML Use Cases
  • Vision API
  • Natural Language API
  • Translate
  • Speech API

12.�Need for Real-Time Streaming Analytics

  • What is Streaming Analytics?
  • Use-cases
  • Batch vs. Streaming (Real-time)
  • Related terminologies
  • GCP products that help build for high availability, resiliency, high-throughput, real-timestreaming analytics (review of Pub/Sub and Dataflow)

13.�Architecture of Streaming Pipelines

  • Streaming architectures and considerations
  • Choosing the right components
  • Windowing
  • Streaming aggregation
  • Events, triggers

14.�Stream Data and Events into PubSub

  • Topics and Subscriptions
  • Publishing events into Pub/Sub
  • Subscribing options: Push vs Pull
  • Alerts

15.�Build a Stream Processing Pipeline

  • Pipelines, PCollections and Transforms
  • Windows, Events, and Triggers
  • Aggregation statistics
  • Streaming analytics with BigQuery
  • Low-volume alerts

16.�High Throughput and Low-Latency with Bigtable

  • Latency considerations
  • What is Bigtable
  • Designing row keys
  • Performance considerations

17.�High Throughput and Low-Latency with Bigtable

  • What is Google Data Studio?
  • From data to decisions