Course: Google Data Engineer

$549.00
$664.29 incl. vat

duration: 24 hours |

Language: English (US) |

access duration: 90 days

In this online Google Data Engineer training we will handle all facets of the certificate: Professional Data Engineer. A professional data engineer enables data-driven decision making by collecting, transforming and visualizing data.

The Data Engineer analyzes data to understand business results, builds statistical models to support decision making and creates machine learning models for automating and simplifying important business processes.

The training demonstrates the following:

  • Build and maintain data structures and databases;
  • Design of data processing systems;
  • Analyze data and make machine learning possible;
  • Modeling business processes for analysis and optimization;
  • Designing in the field of reliability;
  • Visualize data / data to underpin policy;
  • Design for security and compliance.

Result

You are familiar with the role of Data Engineer on the Google Cloud Platform. You are also prepared for the official Google exam: Professional Data Engineer.

Prerequisites

No specific prior knowledge required.

Target audience

Data analist

Google Data Engineer

24 hours

Google Cloud Platform Fundamentals

Google Cloud Platform offers several solutions to streamline any enterprise while keeping costs low. This course covers these benefits, including how to navigate GCP and choose between the various data processing products it provides.

Google Cloud Platform Storage and Analytics

Course_Description=Google Cloud Platform (GCP) offers several services for analytics and storage. This course introduces the storage, compute, and analytics concepts and how they are tied together. TargetAudience=Data professionals who are responsible for provisioning and optimizing big data solutions, and data enthusiasts getting started with Google Cloud Platform Duration=01:13

Deeper through GCP Analytics and Scaling

Deeper through GCP Analytics and Scaling

GCP Network Data Processing Models

You can create and manage an assortment of data processes and network models using GCP. This course will go through the various types, including using a GPU and TensorFlow to create and manage GPU and instances.

Google Cloud Dataproc

GCP provides fully managed cloud services for running Apache Spark and Hadoop. This course will introduce you to the concepts of cluster management with Dataproc, including machine types and workers.

Dataproc Architecture

Dataproc can be used to perform several operations when integrating platforms, including Pig and Hive. This course will dig further into Dataproc architecture while introducing the use of Pig and Hive.

Continued Dataproc Operations

Executing Dataproc implementations with big data can provide a variety of methods. This course will continue the study of Dataproc implementations with Spark and Hadoop using the cloud shell and introduce BigQuery PySpark REPL package.

Implementations with BigQuery for Big Data

Implementations with BigQuery for Big Data

Fundamentals of Big Query

Course_Description=BigQuery is an essential tool for any data analyst using Google Cloud Platform. This course will focus on BigQuery and describe several operations including multiple table queries, nested and repeated fields and building BigQueries. TargetAudience=Data professionals who are responsible for provisioning and optimizing big data solutions, and data enthusiasts getting started with Google Cloud Platform Duration=01:20

APIs and Machine Learning

APIs and Machine Learning

Dataflow Autoscaling Pipelines

Apache Beam, Cloud Dataflow, and Cloud Dataprep can be used to create data pipelines. In this course, you will learn how areas of Beam, Apache Beam SDK, Cloud Dataflow, and Cloud Dataprep assist in pipeline management.

Machine Learning with TensorFlow and Cloud ML

Machine Learning with TensorFlow and Cloud ML

GCP Engineering and Streaming Architecture

Feature engineering can be an essential tool in applied machine learning when enhancing a dataset. In this course, you will learn about concepts of feature engineering, including areas of streaming architecture and implementations.

Streaming Pipelines and Analytics

Streaming data pipelines require operations that are very different from batch operations. This course introduces data stream operations, analytics, and the Google Data Studio visualization tool.

GCP Big Data and Security

Complex operations require more managed and secure systems. This course will explore the use of Cloud Spanner and Bigtable to create more complex data service configurations and managing secure data infrastructure.

We offer several optional training products to enhance your learning experience. If you are planning to use our training course in preperation for an official exam then whe highly recommend using these optional training products to ensure an optimal learning experience. Sometimes there is only a practice exam or/and practice lab available.

Optional practice exam (trial exam)

To supplement this training course you may add a special practice exam. This practice exam comprises a number of trial exams which are very similar to the real exam, both in terms of form and content. This is the ultimate way to test whether you are ready for the exam. 

Optional practice lab

To supplement this training course you may add a special practice lab. You perform the tasks on real hardware and/or software applicable to your Lab. The labs are fully hosted in our cloud. The only thing you need to use our practice labs is a web browser. In the LiveLab environment you will find exercises which you can start immediatelyThe lab enviromentconsist of complete networks containing for example, clients, servers,etc. This is the ultimate way to gain extensive hands-on experience. 

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frequently asked quesions

What is included?

Duration 24 hours
Language English (US)
Certificate of participation Yes
Online access 90 days
Monitor Progress Yes
Award Winning E-learning Yes
Mobile ready Yes

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