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tool#Data#Platform#Cloud Computing#Data Analysis

Google Cloud Platform

Google Cloud Platform is a suite of cloud computing services provided by Google.

Google Cloud Platform offers a variety of services, including computing power, storage, and data analytics, to help businesses scale and optimize their applications.
Established
Medium

Classification

  • Medium
  • Technical
  • Technical
  • Advanced

Technical context

Firebase for app backendBigQuery for data analysesCloud Functions for serverless applications

Principles & goals

Security must be prioritized.Flexibility in architecture is essential.Cost efficiency in resource utilization.
Build
Enterprise, Domain

Use cases & scenarios

Compromises

  • Security threats from cyber attacks.
  • Unexpected costs from underutilized resources.
  • Dependency on a single vendor.
  • Conduct regular security audits.
  • Regularly update cost overview.
  • Plan scaling requirements.

I/O & resources

  • Access to Google Cloud account
  • Provide data sources
  • Set up development environment
  • Deployed cloud resources
  • Analyzed data
  • Generated AI model

Description

Google Cloud Platform offers a variety of services, including computing power, storage, and data analytics, to help businesses scale and optimize their applications. It provides a flexible and secure infrastructure for developing modern software solutions.

✔

  • Increased scalability.
  • Reduced operational costs.
  • Faster time to market.

✖

  • Dependence on internet connectivity.
  • Potential data privacy issues.
  • Complexity of cloud architecture.

  • User Engagement

    Measurement of interaction and usage of the application.

  • Application Response Time

    Time taken by the application to respond to user requests.

  • Operational Costs

    Total expenses for operating cloud services.

E-Commerce Application with Google Cloud

An example of developing an e-commerce application using Firebase and Google Cloud Storage.

User Behavior Data Analysis

Analyze user behavior on a platform using Google Data Studio.

AI Model for Forecasts

Implementation of an AI model trained with Google Cloud ML Engine.

1

Analyze needs and resources

2

Choose cloud services

3

Application testing and feedback iterations

⚠️ Technical debt & bottlenecks

  • Outdated dependencies in code.
  • Non-optimized databases.
  • Lack of automation of processes.
Slow network connections.Difficulties in integration.Unclear pricing structure.
  • No monitoring of costs and usage.
  • Using outdated services.
  • Insufficient consideration of compliance requirements.
  • Too high dependency on a single cloud provider.
  • Lack of planning for peak loads.
  • Underestimating training needs.
Knowledge of cloud technologiesProgramming skills (e.g., Python, Java)Data analysis skills
Scalability of infrastructure.Security requirements of the application.Integration with existing systems.
  • • Compliance with data protection regulations.
  • • Technical infrastructure must be in place.
  • • Training of employees necessary.