The hottest Database Design Substack posts right now

And their main takeaways
Category
Top Technology Topics

Relational modeling challenge

Minimal Modeling • 811 implied HN points • 02 Feb 25
🕹 Technology Data Modeling Programming Languages Software Development Database Design
  1. A key goal in data modeling is to make sure invalid data states cannot be created. This means designing systems where incorrect data combinations are impossible.
  2. The challenge presented involves creating a way to track daily coffee consumption while preventing contradictory data entries, like recording that a user both had coffee and was coffee-free on the same day.
  3. Using common database features, the task is to develop a solution that complies with standard relational model rules, avoiding the use of tricks like JSON data types or triggers.

Issue #14 - The Forgotten Guiding Role of Data Modelling

The Data Ecosystem • 659 implied HN points • 14 Jul 24
🕹 Technology Data science Data Management Information Systems Business Intelligence Database Design
  1. Data modeling is like a blueprint for organizing information. It helps people and machines understand data, making it easier for businesses to make decisions.
  2. There are different types of data models, including conceptual, logical, and physical models. Each type serves a specific purpose and helps bridge business needs with data organization.
  3. Not having a structured data model can lead to confusion and problems. It's important for organizations to invest in good data modeling to improve data quality and business outcomes.

Historically, 4NF explanations are needlessly confusing

Minimal Modeling • 608 implied HN points • 05 Dec 24
🕹 Technology Database Design Data Structures Software Development Information Systems
  1. Fourth Normal Form (4NF) is mainly about creating simple two-column tables to link related data, like teachers and their skills. This straightforward design is often overlooked in favor of complex definitions.
  2. Many explanations of 4NF start with confusing three-column tables and then break them down into simpler forms. This approach makes it harder for learners to grasp the concept quickly and effectively.
  3. The term 'multivalued dependency' can be simplified to just mean a list of unique IDs. You don’t really need to focus on this term to design good database tables; it's more of a historical detail.

Normalization is not enough anymore.

System Design Classroom • 559 implied HN points • 23 Jun 24
🕹 Technology Database Design Data processing Software Development Performance optimization Information Systems
  1. Normalization is important for organizing data and reducing redundancy, but it's not sufficient for today's data needs. We have to think beyond just following those strict rules.
  2. De-normalization can help improve performance by reducing complex joins in large datasets. Sometimes, it makes sense to duplicate data to make queries run faster.
  3. Knowing when to de-normalize is key, especially in situations like data warehousing or when read performance matters more than write performance. It's all about balancing speed and data integrity.

Podcast guest appearance

Minimal Modeling • 202 implied HN points • 23 Dec 24
🕹 Technology Software Development Database Design Data Management Programming
  1. The podcast discussed database design and Minimal Modeling for almost two hours. It shared valuable insights on how to create better database structures.
  2. The speaker is open to appearing on other podcasts and is willing to talk about topics like data documentation and software development processes.
  3. There's a recent podcast episode available, but it is in Russian, limiting its audience. If you need help with databases, the speaker is approachable.
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I spent 7 hours reading another paper to understand more about Snowflake's internal. Here's what I found.

VuTrinh. • 79 implied HN points • 02 Mar 24
🕹 Technology Data Engineering Cloud Computing System Architecture Database Design
  1. Snowflake has a unique design with three main layers: storage, virtual warehouse, and cloud service. This structure helps manage data efficiently and ensures high availability.
  2. The system uses a special ephemeral storage for temporary data during queries, which allows for quick access and less strain on the overall system. This helps with performance and reduces network load.
  3. Snowflake is designed for flexibility, allowing it to adapt resources based on customer needs and workloads. This elasticity helps provide better performance and efficiency.

The scope of the data modeling book

Minimal Modeling • 202 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.

Designing database for Google Calendar, drafts

Minimal Modeling • 101 implied HN points • 18 Feb 24
🕹 Technology Database Design
  1. The author is working on a detailed text about designing a database for a product similar to Google Calendar, following concepts from a specific book.
  2. The current text has four sections with more to come and will also become a chapter in a book, with chapters shared via a mailing list.
  3. Feedback is sought, especially from those new to database modeling, on the outlined topics like all-day events, time-based events, and more.

Book update

Minimal Modeling • 101 implied HN points • 21 Nov 23
🕹 Technology Database Design
  1. The author is working on a book about Database Design using a Minimal Modeling approach
  2. The course focused on logical modeling, secondary data, and table design strategies
  3. Logical modeling helps address complex questions and lays the foundation for successful table design

Sentinel-free schemas: a thought experiment

Minimal Modeling • 16 HN points • 20 Dec 23
🕹 Technology Data Management Database Design Data Integrity Data processing
  1. NULL values in databases create compatibility issues and add complexity to conditional operations
  2. Sentinel values, like empty strings or placeholders, are similar to NULL values and can lead to incorrect results
  3. Creating sentinel-free schemas involves separating attributes into individual tables and explicitly defining reasons for missing data

Master Dynamic Queries in Spring JPA

Curious Devs Corner • 0 implied HN points • 13 Jul 24
🕹 Technology Software Development Programming Data Management Web Development Database Design
  1. You can create fully dynamic queries in Spring JPA based on user input. This allows users to choose which columns to select and how to group them.
  2. When using 'group by', all non-aggregated columns from the select statement must be included in the group clause. Otherwise, you'll get an error.
  3. Using the Java Persistence Criteria API can help effectively manage these dynamic queries and avoid common issues.

Harnessing Data Architecture: Practical Models and SQL Solutions

DataSketch’s Substack • 0 implied HN points • 26 Mar 24
🕹 Technology Data Engineering Database Design Software Development Information Systems Data Analytics
  1. Creating effective data models is crucial for businesses to organize and use their data efficiently.
  2. Different industries like eCommerce, healthcare, and retail have unique data needs that can be addressed with tailored database solutions.
  3. Understanding SQL and how to create tables and relationships helps in developing strong data architecture.

Data Modeling for Data Engineering

DataSketch’s Substack • 0 implied HN points • 18 Mar 24
🕹 Technology Data science Data Engineering Database Design Information Systems Data Modeling
  1. Data modeling is like creating a map for organizing and finding data easily. It helps keep everything tidy and accessible.
  2. There are three types of data models: conceptual, logical, and physical, each serving different levels of detail in planning data structure.
  3. A practical example is organizing a library, where the models help define books, authors, and loans, ensuring everything links and works smoothly.

Minimal Modeling: processes and deliverables

Minimal Modeling • 0 implied HN points • 09 Jan 24
🕹 Technology Database Design Documentation
  1. Creating a logical model from textual descriptions helps database novices and streamlines schema design.
  2. Designing physical table schemas based on the logical model involves making choices and adapting to business changes.
  3. Extracting a logical model from a real-world database using minimal modeling can improve onboarding and communication in a lightweight, collaborative way.