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Introduction

Data engineers are responsible for finding trends in data sets and developing algorithms to help make raw data more useful to the enterprise. This IT role requires a significant set of technical skills, including a deep knowledge of SQL database design and multiple programming languages They collect, transform, and visualize data. The Data Engineer designs, builds, maintains, and troubleshoots data processing systems with a particular emphasis on the security, reliability, fault-tolerance,scalability, fidelity, and efficiency of such systems.

Reference: https://cloud.google.com/certification/data-engineer

Google Cloud Big Data & Machine Learning Fundamentals course

This course is a gateway to introduce you to Google Cloud's big data and different machine learning functions. However, to successfully pass this training, you have to attain one year of experience in SQL, extract transform, data modeling, machine learning, programming in Python, and load activities. So, the objectives of the course are the following:

  • Create ML models using BigQuery ML, APIs, and AutoML.
  • Hire BigQuery and Cloud SQL for interactive data analysis
  • Utilize Cloud SQL & Dataproc to migrate existing MySQL, Pig, Spark, or Hive workloads to Google Cloud
  • Recognize the purpose of the key Big data and Machine Learning products in Google Cloud

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Career Opportunities

The certified individuals can explore a variety of job opportunities. Some of the positions that they can take up include a Software Engineer, a Cloud Architect, a Data Engineer, a Sales Engineer, a Data Scientist, a Cloud Developer, and a Kubernetes Architect, among others. The salary outlook for these job roles is an average of $128,500 per annum.

Google Professional-Data-Engineer Exam Syllabus Topics:

SectionWeightObjectives
Designing data processing systems20%- Designing for regulatory and security requirements
  • 1. Implementing access control and data protection
  • 2. Ensuring data privacy and compliance
- Designing for business requirements
  • 1. Selecting appropriate storage solutions
  • 2. Designing for reliability and fault tolerance
  • 3. Designing for scalability and elasticity
Operationalizing machine learning models20%- Deploying and maintaining ML models
  • 1. Model serving and monitoring
  • 2. Optimizing model performance and cost
- Preparing data for ML
  • 1. Feature engineering and data preparation
  • 2. Handling structured and unstructured data
Maintaining and automating data workloads18%- Resource optimization
  • 1. Cost management and resource allocation
  • 2. Choosing appropriate compute and storage options
- Automation and repeatability
  • 1. Automating deployment and updates
  • 2. Implementing CI/CD for data systems
Building and operationalizing data processing systems25%- Deploying and managing systems
  • 1. Managing infrastructure and resources
  • 2. Monitoring and logging data processes
- Building data pipelines
  • 1. Ingesting data from various sources
  • 2. Transforming and cleaning data
  • 3. Orchestrating data workflows
Ensuring solution quality and reliability17%- Testing and validating data systems
  • 1. Data quality validation
  • 2. Performance and scalability testing
- Troubleshooting and optimization
  • 1. Diagnosing performance issues
  • 2. Optimizing queries and workloads

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