Become a Professional Data Engineer on GCP. Design data processing systems, build end-to-end data pipelines, analyze data and carry out machine learning. This course is part of Google’s Data Engineering track that leads to the Professional Data Engineer certificate.

This 4-day training offers a combination of presentations, demos, and hands-on labs. You will learn how to design data processing systems, build end-to-end data pipelines, analyze data, and carry out Machine Learning.

What You'll Learn

Google Cloud Dataproc Overview

Creating and managing clusters
Leverage custom machine types and preemptible worker nodes
Scaling and deleting Clusters
Lab: Creating Hadoop Clusters with Google Cloud Dataproc

Running Dataproc Jobs

Running Pig and Hive jobs
Separation of storage and compute
Lab: Running Hadoop and Spark Jobs with Dataproc
Lab: Submit and monitor jobs

Integrating Dataproc with Google Cloud Platform

Customize clusters with initialization actions
BigQuery Support
Lab: Leveraging Google Cloud Platform Services

Making Sense of Unstructured Data with Google’s Machine Learning APIs

Google’s Machine Learning APIs
Common ML Use Cases
Invoking ML APIs
Lab: Adding Machine Learning Capabilities to Big Data Analysis

Serverless Data Analysis With Big Query

What is BigQuery?
Queries and Functions
Lab: Writing queries in BigQuery
Loading data into BigQuery
Exporting data from BigQuery
Lab: Loading and exporting data
Nested and repeated fields
Querying multiple tables
Lab: Complex queries
Performance and pricing

Serverless, Autoscaling Data Pipelines With Dataflow

The Beam programming model
Data pipelines in Beam Python
Data pipelines in Beam Java
Lab: Writing a Dataflow pipeline
Scalable Big Data processing using Beam
Lab: MapReduce in Dataflow
Incorporating additional data
Lab: Side inputs
Handling stream data
GCP Reference architecture

Getting Started With Machine Learning

What is machine learning (ML)
Effective ML: concepts, types
ML datasets: generalization
Lab: Explore and create ML datasets

Building ML Models With Tensorflow

Getting started with TensorFlow
Lab: Using tf.learn.
TensorFlow graphs and loops + lab
Lab: Using low-level TensorFlow + early stopping
Monitoring ML training
Lab: Charts and graphs of TensorFlow training

Scaling ML Models With CloudML

Why Cloud ML?
Packaging up a TensorFlow model
End-to-end training
Lab: Run a ML model locally and on cloud

Feature Engineering

Creating good features
Transforming inputs
Synthetic features
Preprocessing with Cloud ML
Lab: Feature engineering

Architecture of Streaming Analytics Pipelines

Stream data processing: Challenges
Handling variable data volumes
Dealing with unordered/late data
Lab: Designing streaming pipeline

Ingesting Variable Volumes

What is Cloud Pub/Sub?
How it works: Topics and Subscriptions
Lab: Simulator

Implementing Streaming Pipelines

Challenges in stream processing
Handle late data: watermarks, triggers, accumulation
Lab: Stream data processing pipeline for live traffic data

Streaming Analytics and Dashboards

Streaming analytics: from data to decisions
Querying streaming data with BigQuery
What is Google Data Studio?
Lab: build a real-time dashboard to visualize processed data

High Throughput and Low-Latency With Bigtable

What is Cloud Spanner?
Designing Bigtable schema
Ingesting into Bigtable
Lab: streaming into Bigtable

Become a GCP Data Engineer

Are you a Developer responsible for managing Big Data transformations? Do you want to become a Professional on the Google Cloud Platform? Time to gain the knowledge and skills to prepare for your Google certificate. This training prepares you to design and build data processing systems on the Google Cloud Platform. You will learn how to analyze data and carry out Machine Learning. We cover structured, unstructured, and streaming data.

Data Engineering on GCP is perfect for

experienced developers responsible for extracting, loading, transforming, cleaning, and validating data, and designing pipelines and architectures for data processing. If you are responsible for creating and maintaining Machine Learning and statistical models, querying datasets, visualizing query results, and creating reports, you’re more than welcome to join too.

Before enrolling, you need to complete the Google Cloud Fundamentals: Big Data & Machine Learning course, or have basic proficiency with:

A common query language like SQL
Data modeling, extracting, transforming, and loading activities
Developing applications using a programming language like Python
Machine Learning and/or statistics

Professional Data Engineer

A Data Engineer should also be able to leverage, deploy, and continuously train pre-existing machine learning models.

Abilities Validated by the Certification

Design data processing systems
Build and operationalize data processing systems
Operationalize machine learning models
Ensure solution quality

Recommended Knowledge and Experience

3+ years of industry experience including 1+ years designing and managing solutions using GCP.

Instructor: Martijn van de Grift

Martijn vd Grift - Binx.io

Martijn is a Cloud consultant at Binx.io, where he specializes in creating solutions using GCP and AWS. He holds the most relevant technical certifications for both clouds. He has a great passion for IT and likes to work with the latest technologies. He loves to share this passion during training, and webinars, where he brings experience from assignments at companies including Booking.com, Weeronline, and ZorgDomein.