Data Engineering on Google Cloud Platform

Price
$3,600.00 USD

Duration
4 Days

 

Delivery Methods
Virtual Instructor Led
Private Group

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Course Overview

Get hands-on experience with designing and building data processing systems on Google Cloud. This course uses lectures, demos, and hand-on labs to show you how to design data processing systems, build end-to-end data pipelines, analyze data, and implement machine learning. This course covers structured, unstructured, and streaming data.

Course Objectives

  • Design and build data processing systems on Google Cloud Platform.
  • Leverage unstructured data using Spark and ML APIs on Cloud Dataproc.
  • 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.
  • Enable instant insights from streaming data

Who Should Attend?

Developers responsible for handling their organization's data
  • Top-rated instructors: Our crew of subject matter experts have an average instructor rating of 4.8 out of 5 across thousands of reviews.
  • Authorized content: We maintain more than 35 Authorized Training Partnerships with the top players in tech, ensuring your course materials contain the most relevant and up-to date information.
  • Interactive classroom participation: Our virtual training includes live lectures, demonstrations and virtual labs that allow you to participate in discussions with your instructor and fellow classmates to get real-time feedback.
  • Post Class Resources: Review your class content, catch up on any material you may have missed or perfect your new skills with access to resources after your course is complete.
  • Private Group Training: Let our world-class instructors deliver exclusive training courses just for your employees. Our private group training is designed to promote your team’s shared growth and skill development.
  • Tailored Training Solutions: Our subject matter experts can customize the class to specifically address the unique goals of your team.

Course Prerequisites

  • Basic proficiency with a common query language such as SQL.
  • Experience with data modeling and ETL (extract, transform, load) activities.
  • Experience with developing applications using a common programming language such as Python.
  • Familiarity with machine learning and/or statistics.

Agenda

1 - Introduction to Data Engineering

  • Explore the role of a data engineer.
  • Analyze data engineering challenges.
  • Intro to BigQuery.
  • Data Lakes and Data Warehouses.
  • Demo: Federated Queries with BigQuery.
  • Transactional Databases vs Data Warehouses.
  • Website Demo: Finding PII in your dataset with DLP API.
  • Partner effectively with other data teams.
  • Manage data access and governance.
  • Build production-ready pipelines.
  • Review GCP customer case study.
  • Lab: Analyzing Data with BigQuery.

2 - Building a Data Lake

  • Introduction to Data Lakes.
  • Data Storage and ETL options on GCP.
  • Building a Data Lake using Cloud Storage.
  • Optional Demo: Optimizing cost with Google Cloud Storage classes and Cloud Functions.
  • Securing Cloud Storage.
  • Storing All Sorts of Data Types.
  • Video Demo: Running federated queries on Parquet and ORC files in BigQuery.
  • Cloud SQL as a relational Data Lake.
  • Lab: Loading Taxi Data into Cloud SQL.

3 - Building a Data Warehouse

  • The modern data warehouse.
  • Intro to BigQuery.
  • Demo: Query TB+ of data in seconds.
  • Getting Started.
  • Loading Data.
  • Video Demo: Querying Cloud SQL from BigQuery.
  • Lab: Loading Data into BigQuery.
  • Exploring Schemas.
  • Demo: Exploring BigQuery Public Datasets with SQL using INFORMATION_SCHEMA.
  • Schema Design.
  • Nested and Repeated Fields.
  • Demo: Nested and repeated fields in BigQuery.
  • Lab: Working with JSON and Array data in BigQuery.
  • Optimizing with Partitioning and Clustering.
  • Demo: Partitioned and Clustered Tables in BigQuery.
  • Preview: Transforming Batch and Streaming Data.

4 - Introduction to Building Batch Data Pipelines,

  • EL, ELT, ETL.
  • Quality considerations.
  • How to carry out operations in BigQuery.
  • Demo: ELT to improve data quality in BigQuery.
  • Shortcomings.
  • ETL to solve data quality issues.

5 - Executing Spark on Cloud Dataproc

  • The Hadoop ecosystem.
  • Running Hadoop on Cloud Dataproc.
  • GCS instead of HDFS.
  • Optimizing Dataproc.
  • Lab: Running Apache Spark jobs on Cloud Dataproc.

6 - Serverless Data Processing with Cloud Dataflow

  • Cloud Dataflow.
  • Why customers value Dataflow.
  • Dataflow Pipelines.
  • Lab: A Simple Dataflow Pipeline (Python/Java).
  • Lab: MapReduce in Dataflow (Python/Java).
  • Lab: Side Inputs (Python/Java).
  • Dataflow Templates.
  • Dataflow SQL.

7 - Manage Data Pipelines with Cloud Data Fusion and Cloud Composer

  • Building Batch Data Pipelines visually with Cloud Data Fusion.
  • Components.
  • UI Overview.
  • Building a Pipeline.
  • Exploring Data using Wrangler.
  • Lab: Building and executing a pipeline graph in Cloud Data Fusion.
  • Orchestrating work between GCP services with Cloud Composer.
  • Apache Airflow Environment.
  • DAGs and Operators.
  • Workflow Scheduling.
  • Optional Long Demo: Event-triggered Loading of data with Cloud Composer, Cloud Functions, Cloud Storage, and BigQuery.
  • Monitoring and Logging.
  • Lab: An Introduction to Cloud Composer.

8 - Introduction to Processing Streaming Data

  • Processing Streaming Data.

9 - Serverless Messaging with Cloud Pub/Sub

  • Cloud Pub/Sub.
  • Lab: Publish Streaming Data into Pub/Sub.

10 - Cloud Dataflow Streaming Features

  • Cloud Dataflow Streaming Features.
  • Lab: Streaming Data Pipelines.

11 - High-Throughput BigQuery and Bigtable Streaming Features

  • BigQuery Streaming Features.
  • Lab: Streaming Analytics and Dashboards.
  • Cloud Bigtable.
  • Lab: Streaming Data Pipelines into Bigtable.

12 - Advanced BigQuery Functionality and Performance

  • Analytic Window Functions.
  • Using With Clauses.
  • GIS Functions.
  • Demo: Mapping Fastest Growing Zip Codes with BigQuery GeoViz.
  • Performance Considerations.
  • Lab: Optimizing your BigQuery Queries for Performance.
  • Optional Lab: Creating Date-Partitioned Tables in BigQuery.

13 - Introduction to Analytics and AI

  • What is AI?.
  • From Ad-hoc Data Analysis to Data Driven Decisions.
  • Options for ML models on GCP.

14 - Prebuilt ML model APIs for Unstructured Data

  • Unstructured Data is Hard.
  • ML APIs for Enriching Data.
  • Lab: Using the Natural Language API to Classify Unstructured Text.

15 - Big Data Analytics with Cloud AI Platform Notebooks

  • Whats a Notebook.
  • BigQuery Magic and Ties to Pandas.
  • Lab: BigQuery in Jupyter Labs on AI Platform.

16 - Production ML Pipelines with Kubeflow

  • Ways to do ML on GCP.
  • Kubeflow.
  • AI Hub.
  • Lab: Running AI models on Kubeflow.

17 - Custom Model building with SQL in BigQuery ML

  • BigQuery ML for Quick Model Building.
  • Demo: Train a model with BigQuery ML to predict NYC taxi fares.
  • Supported Models.
  • Lab Option 1: Predict Bike Trip Duration with a Regression Model in BQML.
  • Lab Option 2: Movie Recommendations in BigQuery ML.

18 - Custom Model building with Cloud AutoML

  • Why Auto ML?
  • Auto ML Vision.
  • Auto ML NLP.
  • Auto ML Tables.
 

Upcoming Class Dates and Times

Sep 9, 10, 11, 12
8:00 AM - 4:00 PM
ENROLL $3,600.00 USD

Virtual: Online - CST
               
 



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