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Google Data Engineer Training in India
4.0
4/5
Price :
₹1,80,000
Category :
Management
Anil Bidari
Chief Consultant
Anil Bidari is a versatile trainer and consultant specializing in GitLab, AWS, Azure, Google, DevOps, Jenkins, Kubernetes, Ansible, Docker, Agile, and Machine Learning. His expertise drives successful technology adoption and implementation, benefiting organizations and individuals alike.
- Time Duration : 28 Hours - 4 Days
- Study Lecture : 20 Lectures
- Skill Level : Advance Course
- Certificate : Yes
- Training Delivery Mode : Online - Live Instructor Led training
OVERVIEW :
COURSE DESCRIPTION
COURSE SUMMARY
COURSE OBJECTIVE
COURSE DESCRIPTION
The Google Data Engineer Training is a comprehensive program designed to equip participants with the skills and knowledge required to excel as Google Certified Data Engineers. This training covers key concepts, tools, and techniques for designing, building, and managing data processing systems on the Google Cloud Platform (GCP). Participants will learn how to design data pipelines, transform and analyze data, build scalable data infrastructure, and implement data security and compliance measures. Through hands-on labs and real-world projects, attendees will gain practical experience in working with GCP data services, such as BigQuery, Dataflow, and Pub/Sub. This training is ideal for data engineers, database administrators, and professionals seeking to enhance their expertise in managing and analyzing data on GCP.
COURSE SUMMARY
The Google Data Engineer Training is a comprehensive program that focuses on providing participants with the skills and knowledge required to excel as Google Certified Data Engineers. This training covers key aspects of designing, building, and managing data processing systems on the Google Cloud Platform (GCP). Participants will learn how to design efficient data pipelines, transform and analyze data using GCP data services, and build scalable and reliable data infrastructure. Through hands-on labs and real-world projects, attendees will gain practical experience in working with GCP data services like BigQuery, Dataflow, and Pub/Sub. By completing this training, participants will be well-prepared to demonstrate their expertise in managing and analyzing data on the Google Cloud Platform.
COURSE OBJECTIVE
The objective of the Google Data Engineer Training is to provide participants with the knowledge and skills necessary to excel as Google Certified Data Engineers. The course aims to equip participants with a deep understanding of data engineering principles, tools, and techniques on the Google Cloud Platform (GCP). Participants will learn how to design and build data processing systems, transform and analyze data, and create scalable data infrastructure using GCP data services. Through hands-on labs and real-world projects, participants will develop the skills required to architect efficient data pipelines, implement data security and compliance measures, and optimize data performance on GCP. By the end of the training, participants will be well-prepared to pass the certification exam and showcase their expertise as Google Data Engineers.
Course Outline
1.1 Designing flexible data representations. Considerations include:
- future advances in data technology
- changes to business requirements
- awareness of current state and how to migrate the design to a future state
- data modeling
- tradeoffs
- distributed systems
- schema design
1.2 Designing data pipelines. Considerations include:
- future advances in data technology
- changes to business requirements
- awareness of current state and how to migrate the design to a future state
- data modeling
- tradeoffs
- system availability
- distributed systems
- schema design
- common sources of error (eg. removing selection bias)
1.3 Designing data processing infrastructure. Considerations include:
- future advances in data technology
- changes to business requirements
- awareness of current state, how to migrate the design to the future state
- data modeling
- tradeoffs
- system availability
- distributed systems
- schema design
- capacity planning
- different types of architectures: message brokers, message queues, middleware, service-oriented
2.1 Building and maintaining flexible data representations
2.2 Building and maintaining pipelines. Considerations include:
- data cleansing
- batch and streaming
- transformation
- acquire and import data
- testing and quality control
- connecting to new data sources
2.3 Building and maintaining processing infrastructure. Considerations include:
- provisioning resources
- monitoring pipelines
- adjusting pipelines
- testing and quality control
3.1 Analyzing data. Considerations include:
- data collection and labeling
- data visualization
- dimensionality reduction
- data cleaning/normalization
- defining success metrics
3.2 Machine learning. Considerations include:
- feature selection/engineering
- algorithm selection
- debugging a model
3.3 Machine learning model deployment. Considerations include:
- performance/cost optimization
- online/dynamic learning
4.1 Mapping business requirements to data representations. Considerations include:
- working with business users
- gathering business requirements
4.2 Optimizing data representations, data infrastructure performance and cost. Considerations include:
- resizing and scaling resources
- data cleansing, distributed systems
- high performance algorithms
- common sources of error (eg. removing selection bias)
5.1 Performing quality control. Considerations include:
- verification
- building and running test suites
- pipeline monitoring
5.2 Assessing, troubleshooting, and improving data representations and data processing infrastructure.
5.3 Recovering data. Considerations include:
- planning (e.g. fault-tolerance)
- executing (e.g., rerunning failed jobs, performing retrospective re-analysis)
- stress testing data recovery plans and processes
6.1 Building (or selecting) data visualization and reporting tools. Considerations include:
- automation
- decision support
- data summarization, (e.g, translation up the chain, fidelity, trackability, integrity)
6.2 Advocating policies and publishing data and reports.
7.1 Designing secure data infrastructure and processes. Considerations include:
- Identify and Access Management (IAM)
- data security
- penetration testing
- Separation of Duties (SoD)
- security control
7.2 Designing for legal compliance. Considerations include:
- legislation (e.g., Health Insurance Portability and Accountability Act (HIPAA), Children’s Online Privacy Protection Act (COPPA), etc.)
- audits
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