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Become a GCP Data Engineer

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.

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What you'll learn

For everyone familiar with SQL, ETL, and a programming language

  • Design and build data processing system on GCP
  • Analyze data using GCP services
  • Train and deploy machine learning models
  • How to deal with structured, unstructured, and streaming data

The schedule


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

learning journey

Data Engineering Learning Journey

meet your trainer

Daniel van der Ende

Data Gandalf
Flexible delivery

The Right Format For Your Preferred Learning Style

In-Classroom & In-Company Training
Online, Instructor-Led Training
Hybrid and Blended Learning
Self-Paced Training
Get in touch with the experts

Have any questions?

Contact Giovanni Lanzani, our Managing Director of Learning and Development, if you want to know more. He’ll be happy to help you!

Call me back

You can reach him out by phone as well at +31 6 51 20 6163

Course: Data Engineering on Google Cloud Platform Training

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