Data Engineering on Google Cloud

Name: Data Engineering on Google Cloud
Price: 2195.00 KRW

Gain a hands-on introduction to designing and building data processing systems on Google Cloud with this four-day instructor-led course.

4 day course

Supporting material

Google Cloud Partner of the Year

Private

A private training session for your team. Groups can be of any size, at a location of your choice including our training centres.

Jellyfish has recently been named a Google Cloud Specialisation Partner of the Year. This title recognises our commitment to provide world-leading Cloud-based Training solutions that help our clients succeed.

We've been selected by Google to facilitate the delivery of this four-day course. All of our trainers are experienced practitioners, so you can learn with total confidence.

Through a combination of presentations, demos, and hands-on labs, you will learn how to design data processing systems, build end-to-end data pipelines, analyse data and carry out machine learning. The course covers structured, unstructured, and streaming data.

This course will be run over four consecutive days, and is delivered as a private training session that can be at run at a location of your choice.

Course overview

Who should attend:

This course 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, visualising query results and creating reports

Walk away with the ability to:

Design and build data processing systems on Google Cloud
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

Prerequisites

To get the most of out of this course, you should have:

Completed Google Cloud Fundamentals: Big Data & Machine Learning course 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 as Python
Familiarity with Machine Learning and / or statistics

Course agenda

Module 1: Introduction to Data Engineering

Explore the role of a data engineer
Analyse 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 GC customer case study
Lab: Analysing Data with BigQuery

Module 2: Building a Data Lake

Introduction to Data Lakes
Data Storage and ETL options on Google Cloud
Building a Data Lake using Cloud Storage
Optional Demo: Optimising 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

Module 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

Module 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

Module 5: Executing Spark on Cloud Dataproc

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

Module 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

Module 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 Google Cloud 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

Module 8: Introduction to Processing Streaming Data

Processing Streaming Data

Module 9: Serverless Messaging with Cloud Pub / Sub

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

Module 10: Cloud Dataflow Streaming Features

Cloud Dataflow Streaming Features
Lab: Streaming Data Pipelines

Module 11: High-Throughput BigQuery and Bigtable Streaming Features

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

Module 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: Optimising your BigQuery Queries for Performance
Optional Lab: Creating Date-Partitioned Tables in BigQuery

Module 13: Introduction to Analytics and AI

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

Module 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

Module 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

Module 16: Production ML Pipelines with Kubeflow

Ways to do ML on Google Cloud
Kubeflow
AI Hub
Lab: Running AI models on Kubeflow

Module 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

Module 18: Custom Model building with Cloud AutoML

Why Auto ML?
Auto ML Vision
Auto ML NLP
Auto ML Tables

Book this course

Call our sales team today

82 64 10 80 10