Claude AI and other feedback

[WolfgangFahl]

I asked Claude AI:
check the curriculum whether it is up - to date - give a list of missing learning goals or topics and show topics that might be outdated

To evaluate whether the curriculum is up-to-date, I'll review the content and compare it to current trends and practices in data engineering and architecture. Here's my analysis:

The curriculum appears to be relatively recent and comprehensive, covering many key aspects of modern data architectures and engineering practices. However, there are a few areas that could potentially be expanded or updated:

Potentially missing or underrepresented topics:

    Data Mesh: While this is covered in Section 10, it could be expanded to include more practical implementation details and challenges.
    DataOps: This is briefly mentioned in LZ 11-9, but could be given more emphasis as a key practice in modern data engineering.
    Data Observability: This concept goes beyond traditional monitoring and could be added as a separate learning goal.
    AI/ML Ops: While ML is mentioned, the specific challenges of operationalizing ML models in data pipelines could be addressed.
    Real-time analytics: This could be expanded upon, especially in the context of streaming data architectures.
    Data Contracts: While mentioned, this could be expanded given its increasing importance in data mesh and other distributed data architectures.
    Data Discovery and Cataloging: These topics could be given more attention, especially in the context of large, distributed data ecosystems.
    Polyglot Persistence: The idea of using multiple data storage technologies for different use cases could be more explicitly addressed.
    Ethical considerations in data engineering: This could include topics like algorithmic bias, data privacy beyond just compliance, and responsible AI.
    Cloud-native data architectures: While cloud is mentioned, more specific patterns for cloud-native data architectures could be included.

Topics that might be considered slightly outdated or could be de-emphasized:

    The heavy focus on traditional data warehousing concepts (like star and snowflake schemas) could be balanced with more modern approaches.
    The distinction between OLTP and OLAP systems, while still relevant, is becoming blurred in some modern architectures. This could be noted.
    The section on ETL vs ELT could be updated to reflect the increasing prevalence of ELT in modern data stacks.

Overall, the curriculum appears to be quite current, covering many important aspects of modern data engineering and architecture. The suggested additions and updates would help to keep it at the cutting edge of the field.

Here are some key aspects i would love to see covered:

Information Hiding and Dependency Inversion

• LZ 2-1 and LZ 2-2 cover reference architectures, which inherently involve information hiding and dependency inversion principles.
• LZ 10-3 to LZ 10-6 discuss Domain Ownership and Data as a Product, which align with these software engineering concepts.

Treating Queries as First-Class Citizens

• LZ 11-6 on Data Quality and LZ 11-7 on Data Contracts implicitly treat data assets (including queries) as first-class citizens. Having queries as first class citizens that are implemented in a platform independent way would be worthwhile to dicuss. Current DATA infrastructure does not support this idea well.
• LZ 11-8 on Metadata further supports this concept by emphasizing the importance of data about data.

Parameterization and Abstraction (e.g Query Execution Context independent Named Parameterized Queries)

• LZ 4-4 on Konnektoren introduces the concept of parameterized access to data sources.
• LZ 9-5 on Building Blocks of Data Pipelines implies a level of abstraction and parameterization in data processing.

Error Handling and Reporting

• While not explicitly covered, error handling is implicit in several LZs, particularly in LZ 11-9 on Operational Aspects.
• LZ 11-4 on Data Access and Permissions touches on the importance of proper system responses, which could include error reporting. This should ideally be technology and vendor independent but mostly is not so it's something Software Architects need to be aware of.

Agile and Iterative Approaches

• LZ 11-9 mentions DataOps, which incorporates agile methodologies into data engineering.
• The overall structure of the curriculum, moving from basic concepts to more complex ones, implies an iterative learning approach.

Query Execution Context and Environment Considerations

• LZ 2-3 on Architecture Decisions based on Reference Architectures covers the importance of considering the execution context.
• LZ 5-6 on Optimization and Scaling directly addresses how different environments affect data system performance.

Version Control and Change Management

• LZ 5-5 on Data Versioning introduces the concept of managing changes over time, which is fundamental to version control.
• LZ 11-2 on Data Governance implies the need for change management processes.

Metadata Management

• LZ 11-8 explicitly covers Metadata, emphasizing its importance in data engineering.
• LZ 4-7 on Metadata Ingestion further reinforces the curriculum's focus on this crucial software engineering concept.