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:
1. Designing data processing systems
1.1 Selecting the appropriate storage technologies.
- Mapping storage systems to business requirements (Google Documentation: Best practices for enterprise organizations)
- Data modeling (Google Documentation: Schema and data model, Data model)
- Tradeoffs involving latency, throughput, transactions (Google Documentation: Database consistency)
- Distributed systems (Google Documentation: Using clusters for large-scale technical computing in the cloud, choosing the right architecture for global data distribution)
- Schema design (Google Documentation: Designing your schema)
1.2 Designing data pipelines.
- Data publishing and visualization (e.g., BigQuery) (Google Documentation: Overview of Visual Profiling, Visualizing BigQuery data using Data Studio)
- Batch and streaming data (e.g., Cloud Dataflow, Cloud Dataproc, Apache Beam, Apache Spark and Hadoop ecosystem, Cloud Pub/Sub, Apache Kafka) (Google Documentation: Dataflow, Stream analytics)
- Online (interactive) vs. batch predictions (Google Documentation: Online versus batch prediction)
- Job automation and orchestration (e.g., Cloud Composer) (Google Documentation: Cloud Composer)
1.3 Designing a data processing solution.
- Choice of infrastructure
- System availability and fault tolerance (Google Documentation: Reliability, Overview of the high availability configuration)
- Use of distributed systems (Google Documentation: Using clusters for large-scale technical computing in the cloud, choosing the right architecture for global data distribution)
- Capacity planning (Google Documentation: Google Cloud Platform for Data Center Professionals: Compute)
- Hybrid cloud and edge computing (Google Documentation: Hybrid and multi-cloud architecture patterns)
- Architecture options (e.g., message brokers, message queues, middleware, service-oriented architecture, serverless functions) (Google Documentation: Pub/Sub)
- At least once, in-order, and exactly once, etc., event processing (Google Documentation: Exactly-once processing in Google Cloud Dataflow)
1.4 Migrating data warehousing and data processing.
- Awareness of current state and how to migrate a design to a future state (Google Documentation: Migration to Google Cloud: Assessing and discovering your workloads, Migration to Google Cloud: Getting started)
- Migrating from on-premises to cloud (Data Transfer Service, Transfer Appliance, Cloud Networking) (Google Documentation: CLOUD DATA TRANSFER)
- Validating a migration (Google Documentation: Migration to Google Cloud: Getting started)
2. Building and operationalizing data processing systems
2.1 Building and operationalizing storage systems.
- Effective use of managed services (Cloud Bigtable, Cloud Spanner, Cloud SQL, BigQuery, Cloud Storage, Cloud Datastore, Cloud Memorystore) (Google Documentation: Google Cloud Databases, Cloud Bigtable)
- Storage costs and performance (Google Documentation: Cloud Storage pricing, Best practices for Cloud Storage cost optimization)
- Lifecycle management of data (Google Documentation: Object Lifecycle Management)
2.2 Building and operationalizing pipelines.
- Data cleansing (Google Documentation: Cleanse Tasks)
- Batch and streaming (Google Documentation: Dataflow, Dataflow Under the Hood)
- Transformation (Google Documentation: Transform Basics)
- Data acquisition and import (Google Documentation: Best practices for importing and exporting data, CLOUD DATA TRANSFER)
- Integrating with new data sources (Google Documentation: Introduction to external data sources)
2.3 Building and operationalizing processing infrastructure. Considerations
- Provisioning resources (Google Documentation: Provisioning Overview, Infrastructure as code)
- also, Monitoring pipelines (Google Documentation: Using Monitoring for Dataflow pipelines, Using the Dataflow monitoring interface)
- furthermore, Adjusting pipelines (Google Documentation: Updating an existing pipeline)
- moreover, Testing and quality control (Google Documentation: DevOps tech: Continuous testing)
3. Operationalizing machine learning models
3.1 Leveraging pre-built ML models as a service. Considerations
- ML APIs (e.g., Vision API, Speech API) (Google Documentation: Vision AI, Cloud Vision)
- Customizing ML APIs (e.g., AutoML Vision, Auto ML text) (Google Documentation: AutoML Vision)
- Conversational experiences (e.g., Dialogflow) (Google Documentation: Dialogflow)
3.2 Deploying an ML pipeline. Considerations
- Ingesting appropriate data (Google Documentation: Data lifecycle)
- also, Retraining of machine learning models (Cloud Machine Learning Engine, BigQuery ML, Kubeflow, Spark ML) (Google Documentation: Getting started with Kubeflow Pipelines, AI Platform)
- furthermore, Continuous evaluation (Google Documentation: Continuous evaluation)
3.3 Choosing the appropriate training and serving infrastructure. Considerations
- Distributed vs. single machine (Google Documentation: Choosing the right architecture for global data distribution, Specifying machine types or scale tiers)
- also, Use of edge compute (Google Documentation: Google Cloud IoT)
- Hardware accelerators (e.g., GPU, TPU) (Google Documentation: Cloud Tensor Processing Units (TPUs))
3.4 Measuring, monitoring, and troubleshooting machine learning models. Considerations
- Machine learning terminology (e.g., features, labels, models, regression, classification, recommendation, supervised and unsupervised learning, evaluation metrics) (Google Documentation: Machine Learning Glossary, Introduction to BigQuery ML)
- Impact of dependencies of machine learning models (Google Documentation: Building a Serverless Machine Learning Model, Machine learning workflow)
- Common sources of error (e.g., assumptions about data) (Google Documentation: Common error guidance)
4. Ensuring solution quality
4.1 Designing for security and compliance. Considerations
- Identity and access management (e.g., Cloud IAM) (Google Documentation: Identity and Access Management)
- Data security (encryption, key management) (Google Documentation: Encryption at rest in Google Cloud)
- Ensuring privacy (e.g., Data Loss Prevention API) (Google Documentation: Cloud Data Loss Prevention (DLP) API)
- Legal compliance (e.g., Health Insurance Portability, and Accountability Act (HIPAA), Children’s Online Privacy Protection Act (COPPA), FedRAMP, General Data Protection Regulation (GDPR)) (Google Documentation: Google Cloud Security and Compliance, Google Cloud & the General Data Protection Regulation (GDPR))
4.2 Ensuring scalability and efficiency. Considerations
- Building and running test suites (Google Documentation: Community Tutorials, Deploying to Cloud Run)
- Pipeline monitoring (e.g., Stackdriver) (Google Documentation: Using Monitoring for Dataflow pipelines)
- also, Assessing, troubleshooting, and improving data representations and data processing infrastructure (Google Documentation: Data preprocessing for machine learning: options and recommendations)
- furthermore, Resizing and autoscaling resources (Google Documentation: Autoscaling groups of instances)
4.3 Ensuring reliability and fidelity. Considerations
- Performing data preparation and quality control (e.g., Cloud Dataprep) (Google Documentation: Dataprep by Trifacta)
- Also, Verification and monitoring (Google Documentation: Cloud Monitoring)
- furthermore, Planning, executing, and stress testing data recovery (fault tolerance, rerunning failed jobs, performing retrospective re-analysis) (Google Documentation: Disaster recovery planning guide)
- Choosing between ACID, idempotent, eventually consistent requirements (Google Documentation: Balancing Strong and Eventual Consistency with Datastore)
4.4 Ensuring flexibility and portability. Considerations
- Mapping to current and future business requirements (Google Documentation: Best practices for enterprise organizations)
- Designing for data and application portability (e.g., multi-cloud, data residency requirements) (Google Documentation: Hybrid and multi-cloud patterns and practices)
- Data staging, cataloguing, and discovery (Google Documentation: Data Catalog overview)
Let us now move to the meat of this article –
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.
- Google Cloud Platform Documentation
- Official Google Cloud Certified Professional Data Engineer Study Guide
- Technical Guides
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.