A Second-brain for SQL
PARA method
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With AI generating more SQL queries, we increasingly need tools to manage and keep track of these AI-generated SQL and metadata.
People can now generate more queries/views/tables but no one is organizing them properly.
This is why we are focusing on designing a space for your SQL scripts and keeping your queries/views/DDLs in order.
Example structure
Credit: workflowy
1. Projects
Specific SQL projects you're working on
alignment with goals and objectives
easier collaboration
streamlined workflow
When working on a project, you often need to execute a series of queries in a specific order. Organizing by project keeps these sequences intact and easily accessible.
2. Areas
Long-term responsibilities you want to manage over time.
consistent focus
routine tasks
long-term relevance
Organizing queries by ongoing business areas aids in routine tasks. It ensures you're not recreating or searching for the same queries on regular analyses.
3. Resources
A topics or interests that may be useful in the future.
Documentation on SQL syntax
Consistency across analyses
General-purpose or template queries
It's useful for referencing standard analysis patterns.
4. Archives
Archive SQL snippets, code examples, and solutions you encounter
Troubleshooting steps
Change tracking
Archived queries serve as a historical record
By moving older, less frequently used queries to an Archives section, you keep your active working areas uncluttered and focused on current tasks. This makes finding relevant, up-to-date queries faster and easier.
3 digits approach
Top level categories are
Example structure
000 - EDA
Getting to know the data by identifying patterns, understanding data distribution, and checking assumptions.
100 - Insights
Tie data observations to business-relevant questions
200 - Advanced analysis
Involves applying statistical or ML methods for segmentation, prediction, etc.
Structured analysis approach
Categorizing your data analyses into different levels can help structure your approach and derive actionable insights.Categorizing your data analyses into different levels can help structure your approach and derive actionable insights.
Here's how you might categorize data questions into EDA, Insights, and Advanced Analysis:
EDA (Facts): Establishes a foundation by understanding the data's basic characteristics.
Insights: Begins to tie data analyses to business-relevant questions.
Advanced Analysis: Focuses on applying statistical methods and models to drive decision-making. Often where the most value is extracted from data.
4 Levels of Data analytics
Descriptive Analysis: Focuses on describing the current state of affairs and answering "what happened?" Uses basic metrics and data visualization.
Diagnostic Analysis: Seeks to answer "why did it happen?" Involves more in-depth exploration of data to understand causes and relationships.
Predictive Analysis: Tries to forecast future outcomes, answering "what could happen?" Utilizes statistical models and machine learning.
Prescriptive Analysis: Answers "what should we do about it?" Involves using insights from all previous stages to inform decision-making and strategy.
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