Certified GCP Data Engineer professional is a globally recognized and valued certification focusing on honing an individual's skills to make data-driven decisions by optimizing the obtained data. This Certification course helps pupils in enabling to learn the concepts of Data Science from scratch. Master your skills with Wissenhive by studying important concepts.
The certification expertly demonstrates how to efficiently collect, transform, and visualize data for generating valuable insights with the aim to render practical understanding to design, develop, manage, and troubleshoot the data processing systems while emphasizing and maintaining the crucial aspects of the system, including scalability, reliability, fault-tolerance, security, fidelity, and efficiency.
International industry expertise at your disposal as you deep-dive into the research topic and sector of
Professional Data Engineers enable data-driven decision-making by collecting, transforming, and publishing data. A Data Engineer should be able to design, build, operationalize, secure, and monitor data processing systems with a particular emphasis on security and compliance; scalability and efficiency; reliability and fidelity; and flexibility and portability.
Machine Learning Engineer
Mapping storage systems to business requirements
Trade-offs involving latency, throughput, transactions
Data publishing and visualization (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)
Choice of infrastructure
System availability and fault tolerance
Use of distributed systems
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
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
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
Batch and streaming
Data acquisition and import
Integrating with new data sources
Testing and quality control
Leveraging pre-built ML models as a service
ML APIs (e.g., Vision API, Speech API)
Customizing ML APIs (e.g., AutoML Vision, Auto ML text)
Conversational experiences (e.g., Dialogflow)
Ingesting appropriate data
Retraining of machine learning models (AI Platform Prediction and Training, BigQuery ML, Kubeflow, Spark ML)
Distributed vs. single machine
Use of edge compute
Hardware accelerators (e.g., GPU, TPU)
Machine learning terminology (e.g., features, labels, models, regression, classification, recommendation, supervised and unsupervised learning, evaluation metrics)
Impact of dependencies of machine learning models
Common sources of error (e.g., assumptions about data)
Designing for security and compliance
Identity and access management (e.g., Cloud IAM)
Data security (encryption, key management)
Ensuring privacy (e.g., Data Loss Prevention 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))
Building and running test suites
Pipeline monitoring (e.g., Cloud Monitoring)
Assessing, troubleshooting, and improving data representations and data processing infrastructure
Resizing and autoscaling resources
Performing data preparation and quality control (e.g., Dataprep)
Verification and monitoring
Planning, executing, and stress testing data recovery (fault tolerance, rerunning failed jobs, performing retrospective re-analysis)
Choosing between ACID, idempotent, eventually consistent requirements
Mapping to current and future business requirements
Designing for data and application portability (e.g., multicloud, data residency requirements)
Data staging, cataloging, and discovery
Who should take this course?
The Google Cloud Professional Data Engineer Certification is ideal for IT professionals who are already working or want to make a career as a senior or professional in
There are certain prerequisites needed for taking the Google Cloud Professional Data Engineer Certification, Wissenhive and Google recommend having
To achieve the Google Cloud Certified Professional Data Engineer certification, the candidates need to pass an exam that is conducted by Google. This exam can be taken remotely with an online proctoring facility or it can be taken from the designated test centres across the world.
The important details regarding the exam are given below:
The questions in the exam belong to different topics including the following:
The candidates appearing in this exam should align themselves with the aforementioned topics and ensure a strong understanding of these skills. However there is no information on the minimum passing score officially from Google, but as per the successful candidates, you must score at least 70% to pass the exam.
Google has put up a dedicated certification for IT professionals who intend to be data engineers over the Google Cloud Platform. It is crucial for you to go with the Professional Data Engineer certification because data analytics and big data analytics are considered the lifeline of any organization or business. Therefore, it is important for individuals to master this technology to take up data-oriented initiatives for helping the business thrive.
For successfully implementing the big data or data initiatives, you will need more proficient skills than being a data analyst or data scientist. You will initially need the skills of a data architect who will be designing the entire framework of data management for your organization. Following that, you will need data engineers to build that designed framework and bring the data pipelines into action for deriving business value out of collected data.
Therefore, if you are willing to pursue your career in the field of data management and optimization, then this certification is a good supportive qualification for you. Getting an edge with this profession demands ideal certification, and Google Professional Data Engineer is an ideal pick for you.
While preparing for this certification, you will gain your skills of using the right tools from the open-source ecosystem of big data. Along with that, you also need to have theoretical and practical knowledge of Python, Scala, or Java to clear this examination with good grades and on the first attempt.
For clearing the certification exam, you need to be aware of the important areas to consider while you prepare. This certification is going to test your abilities in the areas such as:
1. Designing Data Processing Systems
2. Building & Operationalizing Data Processing Systems
3. Operationalizing Machine Learning Models
4. Ensuring Solution Quality
These are the areas in which you need to direct your preparation strategies. Moreover, these are the topics on which the certification exam will be based so as to test your skills. Therefore, make sure you put up your efforts towards adapting the right learning methods in order to get your concepts clear and take on the examination confidently.
The average salary for a Google Professional Data Engineer in the USA is around $147,000 per year. It is a whooping pay-out for this profession that gives clarity on the career-oriented scope. The lowest entry-level salary of a Google Data Engineer is $141,375 per year, whereas the highest pay-out for experienced data engineers is recorded to be $175,000 per year.
Therefore, you can conclude that Google Data Engineer certification is the best you can pursue to obtain a high-paying job. With experience over time, the salary will also hike gradually.
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