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Professional Data Engineer Certification Course

Professional Data Engineer Training

The Professional Data Engineer is an instructor-led, hands-on certification training course. This course helps candidates prepare for the certification exam so that they can take the next step in their cloud career and demonstrate their competency in one of the most in-demand disciplines in the industry today.

The primary objective of this course is to prepare candidates for the Professional Data Engineer certification exam. Along the way, candidates consolidate their foundation in data engineering and machine learning, ensuring that by the end of the course, candidates learn to design and build data processing solutions, operationalise machine learning models, and gain a working knowledge of relevant GCP data processing tools and technologies.

This course is available in Melbourne, Sydney, Brisbane, Adelaide, Canberra, Perth, Hobart and throughout Australia Live Virtually.

There are no pre-requisites for taking this course

Candidates can achieve this certification by passing the following exam(s).

Professional Data Engineer

The certification exam can be registered and attempted within 3 months of course/module completion at our training centre on weekdays during normal business hours (excludes public holidays)

Professional Data Engineer material provided.

  • Design, build and operationalise data solutions
  • Process data streams in real-time
  • Efficiently store and access data in the cloud
  • Use GCP’s pre-trained AI APIs (vision, speech and text)
  • Train and operationalise ML models
  • Cloud and network administrators
  • Data engineers
  • Data and systems analysts

Selecting the appropriate storage technologies. Considerations include:

  • Mapping storage systems to business requirements
  • Data modelling
  • Trade-offs involving latency, throughput, transactions
  • Distributed systems
  • Schema design
  • Designing data pipelines. Considerations include:
  • Data publishing and visualisation (e.g., BigQuery)
  • Batch and streaming data (e.g., Dataflow, Dataproc, Apache Beam, Apache Spark and Hadoop ecosystem,    Pub/Sub, Apache Kafka)
  • Online (interactive) vs. batch predictions
  • Job automation and orchestration (e.g., Cloud Composer)
  • Designing a data processing solution. Considerations include:
  • Choice of infrastructure
  • System availability and fault tolerance
  • Use of distributed systems
  • Capacity planning
  • Hybrid cloud and edge computing
  • Architecture options (e.g., message brokers, message queues, middleware, service-oriented architecture, serverless functions)
  • At least once, in-order, and exactly once, etc., event processing
  • Migrating data warehousing and data processing. Considerations include:
  • Awareness of current state and how to migrate a design to a future state
  • Migrating from on-premises to cloud (Data Transfer Service, Transfer Appliance, Cloud Networking)
  • Validating a migration
  • Building and operationalising data processing systems
  • Building and operationalising storage systems. Considerations include:
  • Effective use of managed services (Cloud Bigtable, Cloud Spanner, Cloud SQL, BigQuery, Cloud Storage, Datastore, Memorystore)
  • Storage costs and performance
  • Life cycle management of data
  • Building and operationalising pipelines. Considerations include:
  • Data cleansing
  • Batch and streaming
  • Transformation
  • Data acquisition and import
  • Integrating with new data sources
  • Building and operationalising processing infrastructure. Considerations include:
  • Provisioning resources
  • Monitoring pipelines
  • Adjusting pipelines
  • Testing and quality control
  • Operationalising machine learning models
  • Leveraging pre-built ML models as a service. Considerations include:
  • ML APIs (e.g., Vision API, Speech API)
  • Customising ML APIs (e.g., AutoML Vision, Auto ML text)
  • Conversational experiences (e.g., Dialogflow)
  • Deploying an ML pipeline. Considerations include:
  • Ingesting appropriate data
  • Retraining of machine learning models (AI Platform Prediction and Training, BigQuery ML, Kubeflow, Spark ML)
  • Continuous evaluation
  • Choosing the appropriate training and serving infrastructure. Considerations include:
  • Distributed vs. single machine
  • Use of edge compute

Hardware accelerators (e.g., GPU, TPU)

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What is included with the Professional Data Engineer Course?

Logitrain course includes official certification exam fee.
Certificate of Attendance

Get a Certificate of Attendance to prove your commitment to learning

Logitrain is an Authorised Pearson VUE Test Centre.
Includes Practical Exercises

Course includes practical exercises to give you hands-on skills and confidence

Logitrain courses, programs and packages includes course material.
Includes Course Material

Course material in digital format is included for flexibility and ease of use

Logitrain courses, packages and programs includes sample practise questions.
Includes Practise Questions

Practise questions are provided for better understanding of the key concepts

Skilled Trainers
Instructor-led Training

Attend the course with an instructor at our training center or from anywhere

Logitrain courses include price beat guarantee.
Our Price Beat Guarantee

Relax, we will beat competitor’s advertised price in Australia. Our course has no extra costs

Dates, Locations & Prices
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The supply of this course/package/program is governed by our terms and conditions. Please read them carefully before enrolling, as enrolment is conditional on acceptance of these terms and conditions. Proposed dates are given, courses run subject to availability and minimum registrations.

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Book and pay for a course before 30 June and attend training until 31 August. Limited seats. Courses run subject to minimum bookings.
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