Skip to content

Google Data Engineer Training in Singapore and India

Sale Sale
Original price Rs. 232,000.00
Original price Rs. 232,000.00 - Original price Rs. 232,000.00
Original price Rs. 232,000.00
Current price Rs. 180,000.00
Rs. 180,000.00 - Rs. 180,000.00
Current price Rs. 180,000.00
Overview
Google Data Engineer Training by Cloud Enabled Pte Ltd in Singapore and India.
Course Summary
Course Objectives
Course Pre- requisites
Course Duration
  • 28 hours - 4 days
Course Outline

Section 1: Designing data processing systems

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

Section 2: Building and maintaining data structures and databases

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

Section 3: Analyzing data and enabling machine learning

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

Section 4: Modeling business processes for analysis and optimization

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)

Section 5: Ensuring reliability

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

Section 6: Visualizing data and advocating policy

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.

 

Section 7: Designing for security and compliance

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
Training Delivery Mode

Online - Live Instructor Led training 

Due to Covid - we dont engage classroom training till situations are ok

Got Questions

Please email to info@thecloudenabled.com and we will be happy to help

This course is designed , developed and delivered by Cloud Enabled Pte Ltd