The hottest Data Modeling Substack posts right now

And their main takeaways
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Top Technology Topics

My Definition of Data Modeling (for today)

Joe Reis • 530 implied HN points • 20 Jan 24
🕹 Technology Data Modeling AI Venture Capital Data Management
  1. Data modeling has various definitions by different experts and serves to improve communication, provide utility, and solve problems.
  2. A data model is a structured representation that organizes data for both humans and machines to inform decision-making and facilitate actions.
  3. Data modeling is evolving to consider the needs of machines, different use cases, and a wider range of modeling approaches for various situations.

Never mind the quality, feel the speed

imperfect offerings • 239 implied HN points • 02 Feb 24
🚌 Education Research AI Data Modeling Social Sciences
  1. The research economy is increasingly focused on speed over quality, especially with the rise of generative AI, which can have negative impacts on reproducibility and diverse fields of knowledge.
  2. Data models in research need to be carefully scrutinized for accuracy and not blindly relied upon, even in specialized areas like protein folding, climate science, or medical diagnostics.
  3. Speed and heuristics shouldn't overshadow the importance of deliberation, qualitative research, and embracing complexity in arriving at meaningful solutions to multidimensional problems.

Normalization Vs Denormalization - Taking A Step Back

SeattleDataGuy’s Newsletter • 612 implied HN points • 21 Nov 23
🕹 Technology Data Engineering Database Data Modeling Normalization
  1. Normalization structures data to reduce duplication and ensure integrity.
  2. Goals of normalization include eliminating redundancy, minimizing data mutation issues, and protecting data integrity.
  3. Denormalization introduces redundancy strategically to improve read performance, useful for reporting, analytics, and read-heavy applications.

Eventify everything - Data modeling for event data

timo's substack • 117 implied HN points • 06 Feb 24
🕹 Technology Data Modeling Data Analytics
  1. Data modeling for event data involves handling various source data and supporting diverse analysis use cases.
  2. Event data modeling can be organized into layers, from raw source data to consumption-ready data for analytics tools.
  3. Qualifying events to activities in event data modeling helps improve data usability and user experience in analytics tools.
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Data modeling brain dump, vol. I

Minimal Modeling • 393 implied HN points • 12 Oct 23
🕹 Technology Data Modeling
  1. The author has been sharing daily Twitter threads on data-related topics, which will continue as long as there is something to write about.
  2. The author is considering crossposting these threads to Substack Notes and is seeking feedback on reader interest.
  3. The author has found changing the cadence and format of their posts to be refreshing and an interesting experiment.

Is Kimball Still Relevant?

Joe Reis • 648 implied HN points • 22 Jul 23
🕹 Technology Data Modeling Open Source Hardware
  1. There are abundant tools and computing power available, but focusing on delivering business value with data is still crucial.
  2. Data modeling, like Kimball's dimensional model, remains relevant for effective analytics despite advancements in technology.
  3. Ignoring data modeling in favor of performance considerations can lead to a loss of understanding, business value, and overall impact.

The scope of the data modeling book

Minimal Modeling • 196 implied HN points • 07 Sep 23
🕹 Technology Data Modeling Database Design
  1. Data modeling involves layers like actual business domain, logical model, physical model, and database storage optimization.
  2. Focus primarily on the logical model and how it maps to the physical model for practical advice on table structures.
  3. Key areas within the scope of data modeling include basic logical model, handling either/or/or data, modeling polymorphic data, template repetitions, basic physical model, and secondary data.

Data Modeling 101 - Part 2

Data Engineering Central • 255 implied HN points • 10 Jul 23
🕹 Technology Data Modeling Indexes
  1. Data Modeling involves distinct approaches for relational databases and Lake Houses.
  2. Key concepts like logical normalization, business use case analysis, and physical data localization are crucial for effective data modeling.
  3. Understanding the 'grain' of the data, or the lowest level of detail in a record, is essential for a successful data model.

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.

Why product analytics is completely different than BI

timo's substack • 294 implied HN points • 28 Feb 23
🕹 Technology Data Analytics Product Management Business Intelligence Data Collection Data Modeling
  1. Marketing analytics, BI, and product analytics have different requirements for source data and data handling.
  2. Product analytics involves more exploration and pattern-finding compared to marketing analytics and BI.
  3. Adopting product analytics requires a different approach, mindset, and tool compared to traditional analytics setups.

Joe's Nerdy Rants #5

Joe Reis • 176 implied HN points • 17 Jun 23
🕹 Technology Data Modeling Artificial Intelligence Business Startups Programming
  1. Data professionals interpret the concept of 'model' in various ways, leading to confusion and inconsistency in the field.
  2. Establishing a shared understanding through high-level data modeling can promote consistent and reliable models in organizations.
  3. The use of AI tools in programming has become widespread, indicating a shift in the nature of programming but emphasizing the importance of understanding and verifying AI-generated code.

More on false links

Minimal Modeling • 98 implied HN points • 24 Jul 23
🕹 Technology Data Modeling Database Management
  1. In modeling, consider defining links based on specific sentence structures, like anchor, verb, anchor.
  2. Carefully distinguish between false links and actual links to avoid modeling mistakes.
  3. Identifying and managing different types of links can prevent confusion and improve database accuracy.

Everyday examples of mundane low-stakes data modeling fails

Data: Made Not Found (by danah) • 51 implied HN points • 13 Jun 23
🕹 Technology Data Modeling Supply Chains Customer Service Delivery Services
  1. Focusing on low-stakes data modeling failures is important to understand how algorithms are shaping minor aspects of our lives.
  2. Supply chains and service-based businesses are facing challenges from flawed data modeling, affecting customers, workers, and businesses.
  3. Everyday interactions like car rentals and food delivery are revealing flaws in data modeling, leading to frustration and distrust in brands.

Normalizing User Data

ciamweekly • 2 HN points • 26 Feb 24
🕹 Technology Data Modeling Normalization Performance optimization
  1. Data modeling involves the choice between normalizing data and using denormalized data, each with its own strengths and tradeoffs.
  2. Normalized data leads to less data duplication and easier data updates, but may result in challenges with historical data and performance.
  3. CIAM systems, along with IAM and directory systems, normalize user data to centralize customer information, providing benefits like easy querying and centralized authentication, but also introducing challenges like session handling and updating data across systems.

ChatGPT Learns Fintech

Chaos Engineering • 5 implied HN points • 24 Feb 23
🕹 Technology AI Fintech Machine Learning Neural Networks Data Modeling
  1. ChatGPT can learn some superficial aspects of finance but needs explicit training to become a financial expert.
  2. For ChatGPT to learn fintech, a hybrid architecture combining its pretrained model with a specific ML model optimized for financial tasks is necessary.
  3. Improving ChatGPT's understanding of finance requires training it on structured financial data and updating its architecture to process dense, numeric data.

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.

Joe's Nerdy Rants #6

Joe Reis • 2 HN points • 24 Jun 23
🕹 Technology Data Modeling
  1. Data modeling needs to adapt to modern business workflows and technologies.
  2. There is a need to address the underlying issues in databases and data warehouses before implementing AI solutions.
  3. Practices like conceptual and logical data modeling should be revitalized and made simpler and more iterative.

OLTP vs OLAP: The Core of Data Miscommunication

Data Products • 2 HN points • 23 Jun 23
🕹 Technology Data Infrastructure Data science Data Systems Data Modeling
  1. The difference between OLTP and OLAP systems can cause miscommunication among data producers and consumers.
  2. OLTP systems focus on serving end users quickly with specific product features, while OLAP systems handle complex analytics by scanning large amounts of data.
  3. Empathy and communication between OLTP and OLAP teams are crucial to building scalable data products.

A model of everything

Simplicity is SOTA • 0 implied HN points • 17 Jul 23
🕹 Technology Data Modeling Machine Learning
  1. A model of everything predicts final and intermediate goals of a company, is causal, and covers significant inputs.
  2. Foundational choices in building a model of everything include deciding the scope, complexity of relationships, and optimization strategy.
  3. Financial forecasting often involves models of everything, built in spreadsheets, but may not work well for machine learning models.

Ghosts of Diffusion

Cybernetic Forests • 0 implied HN points • 21 Aug 22
🕹 Technology AI Image Generation Data Modeling Machine Learning
  1. AI-generated images are similar to spirit photography from the 19th century, evoking a mystical connection to new technologies
  2. Diffusion models like DALLE2 differ from GANs by stripping images to noise and then reconstructing them, learning how images become noise and reverting them back
  3. DALLE2 creates images by finding patterns in noise, showing that the foundation of every image is arbitrary, like a dream, and that the AI is not really creating art but tracing possibilities in decay

What is Surgical Fine-Tuning and Why You Should Care

Three Data Point Thursday • 0 implied HN points • 13 Jul 23
🕹 Technology AI Data Modeling Deep Learning
  1. Surgical fine-tuning in ML makes algorithms better suited for specific business contexts through precise changes, an advancement over regular fine-tuning.
  2. Entity-centric data modeling marries ML feature engineering with data engineering, improving data operations for companies.
  3. Estimating efforts for ML projects can be simplified by considering the cost of delay and the real-time requirement of the algorithm.