The hottest Semantic Layers Substack posts right now

And their main takeaways
Category
Top Technology Topics

Standard Semantics

davidj.substack • 95 implied HN points • 01 Nov 23
🕹 Technology Data Modeling Semantic Layers APIs Standardization
  1. Having a standard interface for semantic layers is crucial to prevent failure and ensure compatibility among different layers.
  2. SQL APIs offered by semantic layers may not be truly SQL, leading to potential confusion and challenges in querying data.
  3. Supporting REST HTTP interfaces for semantic layers enables a broader range of use cases, including data applications for internal and external purposes.

The Modern Data Stack is Dead, Long Live the Modern Data Stack - Part 2

davidj.substack • 107 implied HN points • 26 Jul 23
🕹 Technology Data Stack Orchestration Consumption Semantic Layers
  1. The modern data stack is evolving with new tools and options for data architecture.
  2. Key trends include the focus on data ingestion and telemetry, improved orchestration tools, and advancements in compute engines.
  3. Data consumption is being enhanced through self-serve AI capabilities, BI tools, and free-form analyst tools, all sitting on a semantic layer.

Semantic Superiority - Part 3

davidj.substack • 83 implied HN points • 05 Apr 23
🕹 Technology Semantic Layers BI Tools Open Source APIs
  1. Semantic layers are crucial for governance, security, accessibility, and developer experience benefits in data analytics.
  2. Standalone semantic layers offer more flexibility and serve multiple use cases compared to semantic layers built into BI tools.
  3. Different standalone semantic layer options like Cube, AtScale, dbt/MetricFlow, and Looker Modeller provide unique features and cater to varying needs in data modeling and analytics.

Beyond YAML

Making Things • 1 implied HN point • 06 Nov 23
🕹 Technology Programming Languages Data Modeling Semantic Layers Feedback loops
  1. Many semantic layers are built with YAML for its readability and quick setup, but it can lead to a poor developer experience.
  2. YAML lacks immediate feedback for complex expressions, forcing users into a guessing game when writing configurations.
  3. Implementing a real programming language instead of just a configuration DSL can provide instant feedback and support complex data modeling.
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