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Is GCP data engineer certification worth it?

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Is GCP data engineer certification worth it?

Data Engineers enable corporations to integrate all of the fancy advanced analytics and insight generations that data science has to offer. All of this is accomplished by establishing trust and providing industry-wide access to accurate, reliable data at scale through sound data infrastructure and architecture. The job of a data engineer is crucial to the success of data-driven organizations, as they are responsible for building the foundation that enables other professionals, such as data analysts and data scientists, to work effectively with data. They need to have a strong understanding of the business requirements and be able to work closely with other stakeholders to design and build systems that meet those requirements.

The Google Cloud Certified Professional Data Engineer program can help you advance your career. Furthermore, the annual salary for a Google Cloud Certified Professional Data Engineer is estimated to be USD 132,900. This certification will undoubtedly assist you in making significant advancements in your professional life.

Let us know If GCP data engineer certification is worth it!

Please allow us to begin by knowing more about the GCP data engineer!

About GCP data engineer

The Google Cloud Professional Data Engineer exam is a certification exam that tests your knowledge and skills in designing, building, and managing data solutions on the Google Cloud Platform (GCP). The exam covers a wide range of topics, including:

  • Data storage solutions: This includes understanding various data storage options on GCP, such as Cloud Storage, BigQuery, and Cloud SQL.
  • Data processing: This includes knowledge of data processing solutions such as Cloud Dataflow, Cloud Dataproc, and Cloud Pub/Sub.
  • Data migration: This includes understanding how to migrate data from on-premises systems to GCP, as well as techniques for data archiving and disaster recovery.
  • Data analysis: This includes knowledge of data analysis tools such as BigQuery and Cloud Dataprep, as well as data visualization tools like Google Data Studio.
  • Security and compliance: This includes understanding security best practices, such as identity and access management, and knowledge of GCP’s compliance certifications, such as ISO 27001 and SOC 2.
  • Monitoring and logging: This includes understanding how to monitor and troubleshoot data pipelines on GCP, as well as how to use logging tools like Stackdriver Logging and Cloud Monitoring.

GCP Data Engineer exam is a rigorous and challenging certification exam that tests your knowledge and skills in designing, building, and managing data solutions on GCP. By passing the exam and obtaining the certification, you can demonstrate your expertise in data engineering and cloud computing, and advance your career in this field.

Exam Format

The Google Cloud Professional Data Engineer exam will consist of 50 questions and will last 2 hours. The questions on this exam, however, may be difficult to answer because they will be of multiple-choice and multiple select varieties. Furthermore, registration fees for this exam are $200 (plus applicable taxes) and are available in both English and Japanese.

However, if you do not pass the exam the first time, you have 14 days to retake it. If you fail the second time, you must wait 60 days before taking it again. Finally, if you fail the exam for the third time, you must wait 365 days before taking it again.

Prerequisites of the Exam

Prerequisites are an important part of any exam. The following are the requirements for becoming a Google Cloud Certified Professional Data Engineer:

  • The ideal candidate will be scalable and efficient.
  • He or she should be able to design and monitor data processing systems, with a focus on security.
  • Above all, a data engineer should be able to leverage and train pre-existing machine learning models on a continuous basis.

Course Outline: Google Cloud Professional Data Engineer

Take a glance at the topics that needed to be covered for the exam and you need to pay focus on

Section 1: Designing data processing systems (22%)

1.1 Designing for security and compliance. Considerations include:

1.2 Designing for reliability and fidelity. Considerations include:

1.3 Designing for flexibility and portability. Considerations include

1.4 Designing data migrations. Considerations include:

Section 2: Ingesting and processing the data (25%)

2.1 Planning the data pipelines. Considerations include:

2.2 Building the pipelines. Considerations include:

2.3 Deploying and operationalizing the pipelines. Considerations include:

Section 3: Storing the data (20%)

3.1 Selecting storage systems. Considerations include:

3.2 Planning for using a data warehouse. Considerations include:

  • Designing the data model (Google Documentation: Data model)
  • Deciding the degree of data normalization (Google Documentation: Normalization)
  • Mapping business requirements
  • Defining architecture to support data access patterns (Google Documentation: Data analytics design patterns)

3.3 Using a data lake. Considerations include

3.4 Designing for a data mesh. Considerations include:

Section 4: Preparing and using data for analysis (15%)

4.1 Preparing data for visualization. Considerations include:

4.2 Sharing data. Considerations include:

4.3 Exploring and analyzing data. Considerations include:

  • Preparing data for feature engineering (training and serving machine learning models)
  • Conducting data discovery (Google Documentation: Discover data)

Section 5: Maintaining and automating data workloads (18%)

5.1 Optimizing resources. Considerations include:

5.2 Designing automation and repeatability. Considerations include:

5.3 Organizing workloads based on business requirements. Considerations include:

5.4 Monitoring and troubleshooting processes. Considerations include:

5.5 Maintaining awareness of failures and mitigating impact. Considerations include:

Is GCP data engineer certification worth it?

Whether the Google Cloud Professional Data Engineer certification is worth it depends on several factors, including your career goals, current role, and experience in the field. Some of the benefits of obtaining the GCP Data Engineer certification include:

  • Validation of skills: The GCP Data Engineer certification demonstrates to employers and clients that you have a deep understanding of Google Cloud Platform and its data engineering capabilities.
  • Career advancement: The certification can help you advance your career, by showcasing your expertise in data engineering and cloud computing.
  • Increased earning potential: According to industry data, certified data engineers can command higher salaries compared to their non-certified counterparts.
  • Access to job opportunities: Having the GCP Data Engineer certification can increase your visibility to potential employers and open up new job opportunities.
  • Improved credibility: The GCP Data Engineer certification provides third-party validation of your skills and knowledge, improving your credibility with employers, clients, and peers.
  • In-demand skills: Data engineering is a highly in-demand field, and the GCP Data Engineer certification validates your skills and knowledge in designing and building data processing systems on GCP, which is a highly desirable skillset.
  • Access to exclusive resources: As a certified GCP Data Engineer, you gain access to exclusive resources, such as training and networking opportunities, that can help you stay up-to-date with the latest trends and technologies in the field.
  • Higher salary potential: Individuals with GCP Data Engineer certification may command higher salaries compared to non-certified individuals due to their specialized skills and knowledge.

Overall, the GCP Data Engineer certification can be a valuable investment if you are looking to advance your career in data engineering and cloud computing. However, it’s important to evaluate your own goals and priorities to determine if the certification aligns with your professional aspirations.

Let us now move to some of the resources that can help you ace the exam –

Data Engineering on Google Cloud Platform

This four-day instructor-led course introduces participants to designing and building data pipelines on the Google Cloud Platform. Candidates learn the process of designing a data system through a combination of presentations, demos, and hands-on labs. They also learn and build end-to-end data pipelines, analyze data, and derive insights. This course covers everything from structured to unstructured to streaming data.

Access Google Cloud Platform here.

Hands-on practice!

Because this exam assesses technical skills related to job profiles. Hence Hands-on experience is the best way to prepare for the exam. If candidates feel the need for additional experience or practice after completing the training program, we strongly advise them to use the hands-on labs available on Qwiklabs. They are also available on the GCP free tier for assessing candidates’ knowledge and skills.

 Access Hands-on experience here!

Additional resources

When it comes to certification exams such as Google Cloud Certified Professional Data Engineer, the more learning resources available, the better the outcome. In the same vein, if the candidate requires more in-depth knowledge and wants to critically acknowledge their Google Cloud Platform components. As a result, we’ve provided you with two Quick links to additional resources.

Practice tests

Finally, it’s time to assess oneself. Take it from us: self-evaluation is the final step to success. As a result, Google Cloud Certified Professional Data Engineer Practice Exams are all that you require. You should practice as much as you can. It not only helps you understand where you are lacking, but it also ensures you are improving your skills. So, continue to take as many practice tests as you can. FOR MORE PRACTICE TESTS, CLICK HERE!

Conclusion

Practice exams are the most effective and useful way to determine your level of preparedness. The Google Cloud Certified Professional Data Engineer Practice Exams will assist you in identifying areas of weakness in your preparation and reducing your chances of making mistakes in the future. After finishing a topic, practicing for the test will reveal your weaknesses and reduce your chances of making mistakes on exam day. To ensure thorough revision, begin taking full-length practice exams after learning a specific topic.

Anandita Doda
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