Data Engineering on Google Cloud

Course Code: GO5975 / Duration: 4 Days

Course Overview

Learn how to design and build data processing systems.

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.

Virtual Learning
This interactive training can be taken from any location, your office or home and is delivered by a trainer. This training does not have any delegates in the class with the instructor, since all delegates are virtually connected. Virtual delegates do not travel to this course, Global Knowledge will send you all the information needed before the start of the course and you can test the logins.

How can I attend my course?

On-line from
your chosen location

At our dedicated
training facility

On-site at
your premises

Target 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

COURSE OBJECTIVES

In this course you will learn:
• 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

Serverless Data Analysis with BigQuery

• What is BigQuery
• Advanced Capabilities
• Performance and pricing

Serverless, Autoscaling Data Pipelines with Dataflow

Getting Started with Machine Learning

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

ML Architectures

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

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

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)

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

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

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)

Architecture of Streaming Pipelines

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

Stream Data and Events into PubSub

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

Build a Stream Processing Pipeline

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

High Throughput and Low-Latency with Bigtable

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

High Throughput and Low-Latency with Bigtable

What is Google Data Studio?
From data to decisions

COURSE 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

You will receive a full set of course notes
and all supporting materials for your course.

Hard Copy Delivered to your premises or Downloaded to a chosen device.

To book this course please call
+44 (0) 1444 410296 or email Info@kplknowledge.co.uk

Training and accreditation is provided through Global Knowledge