(GCP) Google Professional Data Engineer is a certification offered by Google Cloud that validates the skills and knowledge of professionals in designing, building, and managing data processing systems on the Google Cloud Platform (GCP).
A Google Professional Data Engineer holds the responsibility of planning, creating, and overseeing data processing systems on Google Cloud Platform (GCP). They possess advanced skills in utilizing GCP tools and services to build, launch, and uphold data processing solutions that are both highly scalable and well-protected.
Course Outline
Google Cloud Certified Professional Data Engineer: Glossary
Here are some terms and definitions that may be useful for someone preparing for the Google Cloud Certified Professional Data Engineer certification exam:
- Data Lake: A centralized repository for storing all your structured and unstructured data at any scale.
- Data Warehouse: A large, centralized repository for storing and managing structured data from multiple sources.
- BigQuery: Google’s serverless, highly-scalable cloud data warehouse that allows you to analyze and query large datasets using SQL.
- Cloud Storage: A scalable, fully-managed object storage service that allows you to store and access data from anywhere.
- Understanding Cloud Dataflow: A fully-managed service for building batch and streaming data pipelines that can process data in real time.
- Cloud Pub/Sub: A fully-managed messaging service that provides access for sending and receiving messages between independent applications.
- Understanding Cloud Composer: A fully-managed service for building and managing workflows on Google Cloud.
- Cloud Dataproc: A fully-managed service for running Apache Hadoop and Apache Spark clusters on Google Cloud.
- Understanding Cloud SQL: A fully-managed relational database service that allows you to run databases on Google Cloud.
- Cloud Spanner: A globally distributed, horizontally-scalable relational database service that allows you to run mission-critical applications on Google Cloud.
- Understanding Cloud Bigtable: A fully-managed NoSQL database service that allows you to store and manage large datasets in real time.
- Cloud ML Engine: A fully-managed service for creating and deploying machine learning models.
- Cloud Vision API: A machine learning-based image recognition service that allows you to label and categorize images.
- Understanding Cloud Natural Language API: A machine learning-based service that allows you to extract insights from unstructured text.
- Cloud Speech-to-Text API: A machine learning-based service that allows you to transcribe speech in real time.
Google Cloud Certified Professional Data Engineer: Study Guide
Getting ready for the Google Professional Data Engineer certification exam involves having a solid grasp of the exam’s content and hands-on experience with creating data solutions on Google Cloud Platform (GCP). Here are some guidelines to assist you in your exam preparation:
- Review the Exam Guide: To start your exam preparation, go through the exam guide offered by Google. This guide lays out the subjects included in the exam and the expertise and understanding needed to succeed. Take your time to study the guide carefully and highlight any sections that require extra attention in your studies.
- Get Hands-on Experience: The best way to prepare for the exam is to gain practical experience working with GCP. Sign up for a GCP account and start working with the various GCP services such as Compute Engine, Cloud Storage, BigQuery, etc.
- Take Online Courses: You can find numerous online courses that address the subjects and abilities needed for the exam. Google provides both free and paid courses on the Google Cloud Platform, accessible through the Google Cloud Learning Center. Additionally, online learning platforms like Coursera, Udemy, and Pluralsight provide GCP courses as well.
- Read the Documentation: Google provides extensive documentation on each of its GCP services. Make sure to read the documentation for each service and understand how it can be used to build data solutions.
- Join Online Communities: Participate in online communities like Reddit, Stack Overflow, and Google Cloud community forums to seek advice and gain knowledge from individuals who have already completed the exam. These communities are also great sources for valuable insights and recommendations to help you get ready for the exam.
What makes Google Data Engineer Certification exam difficult?
The Google Professional-Data-Engineer certification exam is widely recognized and known to be quite challenging. This certification is at an advanced level and can open doors to prestigious job positions within reputable organizations. As a result, the difficulty level of the Google Cloud Certified Professional Data Engineer exam is relatively high. It’s regarded as one of the most respected and sought-after IT certification exams, but it’s also acknowledged as being quite demanding. The challenge lies in the extensive range and depth of knowledge that Google expects candidates to possess.
In essence, the Google Data Engineer Certification exam is considered tough due to the need for a deep understanding of a wide array of technical concepts and technologies, coupled with the ability to apply this knowledge practically on the Google Cloud Platform. Candidates are required to demonstrate their skills in real-world scenarios within a limited timeframe, which adds to the complexity of the exam.
Expert’s Know-How
Remember achieving the Google Certified Professional Data Engineer certification is not a piece of cake. In other words, it involves in-depth knowledge and understanding of GCP offerings. Also, as the market grows, the value of certification grows. However, with some effort and focus, it is possible to achieve the certification.
Once you complete your preparation for Google data engineer certification exam, after that you have to practice and measure your score.
