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Location: On-Premises vs. Cloud

  • On-Premises:
    • Company owns and maintains hardware and software.
    • High CapEx (capital expenses) for hardware and data centers.
    • Limited flexibility and scalability.
  • Cloud:
    • Cloud provider (e.g., AWS, Azure, GCP) manages hardware and infrastructure.
    • OpEx (operational expenses) with pay-as-you-go pricing.
    • High flexibility, scalability, and ease of updates.
  • Hybrid:
    • Some components on-premises, others on the Cloud.
    • Useful for companies with regulatory or security constraints.
  • Trend: Most companies are moving to Cloud-first or Cloud-only solutions.

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Cost Optimization

  1. Total Cost of Ownership (TCO):
    • Includes direct costs (e.g., salaries, AWS bills) and indirect costs (e.g., downtime, lost productivity).
    • CapEx vs. OpEx:
      • CapEx: Upfront costs for hardware (common in on-premises systems).
      • OpEx: Ongoing operational costs (common in Cloud systems).
    • Cloud Advantage: Lower TCO due to flexible, pay-as-you-go pricing.
  2. Total Opportunity Cost of Ownership (TOCO):
    • Cost of lost opportunities by choosing one tool over others.
    • Example: Choosing Data Stack A excludes the benefits of Data Stack B.
    • Mitigate TOCO by building flexible, loosely coupled systems.
  3. FinOps:
    • Optimize costs while maximizing revenue.
    • Use Cloud-based services with pay-as-you-go pricing and modular options.

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Build vs. Buy

  • Build from Scratch:
    • Only recommended if no existing solution meets your needs.
    • High cost and effort (undifferentiated heavy lifting).
  • Open-Source:
    • Free to use but requires expertise to implement and maintain.
    • Best for teams with bandwidth and technical skills.
  • Managed Open-Source:
    • Vendor-managed versions of open-source tools.
    • Reduces maintenance burden.
  • Proprietary Solutions:
    • Commercial tools with licensing fees.
    • Best for small teams or when open-source options are insufficient.
  • Recommendation:
    • Start with open-source or managed open-source.
    • Use proprietary solutions if necessary.

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Serverless vs. Server-Based

  1. Server-Based:
    • You manage the server (e.g., Amazon EC2 instances).
    • Responsible for updates, scaling, and security.
    • Best for complex, high-compute workloads.
  2. Containerized:
    • Lightweight, modular units (e.g., Docker) that package code and dependencies.
    • Easier to manage than full servers but still requires some infrastructure setup.
  3. Serverless:
    • No server management (e.g., AWS Lambda, AWS Glue).
    • Automatically scales, with pay-as-you-go pricing.
    • Best for simple, discrete tasks.
    • Limitations: Execution frequency, concurrency, and duration.
  • Recommendation:
    • Start with serverless for simplicity.
    • Use containers (e.g., Kubernetes) for more complex workloads.

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Undercurrents in Tool Selection

  1. Security:
    • Choose tools from reputable sources.
    • Implement authentication and encryption.
    • Avoid tools with suspicious components (e.g., spyware).
  2. Data Management:
    • Ensure tools comply with data governance and privacy regulations (e.g., GDPR).
    • Verify data quality and protection against breaches.
  3. DataOps:
    • Look for tools with strong automation and monitoring features.
    • Understand the provider’s Service Level Agreement (SLA) for reliability and availability.
  4. Data Architecture:
    • Choose tools with modularity and interoperability.
    • Ensure flexibility and loose coupling between components.
  5. Orchestration:
    • Popular tools: Apache Airflow, Prefect, Dagster, Mage.
    • Choose based on your architecture goals and team expertise.
  6. Software Engineering:
    • Avoid undifferentiated heavy lifting (hard work that doesn’t add value).
    • Prefer open-source or managed open-source tools over proprietary solutions.

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Key Takeaways

  1. Cloud-First Approach: Build on the Cloud for flexibility, scalability, and cost efficiency.
  2. Cost Optimization: Minimize TCO and TOCO by choosing flexible, pay-as-you-go solutions.
  3. Build vs. Buy: Prefer open-source or managed open-source tools over building from scratch.
  4. Serverless vs. Server-Based: Use serverless for simple tasks and containers for complex workloads.
  5. Undercurrents: Consider security, data management, DataOps, architecture, orchestration, and software engineering when choosing tools.

Source: DeepLearning.ai data engineering course.

Data ArchitectureRequirements Gathering