Most of us are familiar with the apartment rental process. But what does it take to run a leasing office? In this article, we look at a data model designed to do just that.
AI Database Generator is a sophisticated tool that utilizes artificial intelligence and machine learning algorithms to automate the design and creation of database schemas.
autodb: Automatic Database Normalisation for Data Frames
Automatic normalisation of a data frame to third normal form, with the intention of easing the process of data cleaning. (Usage to design your actual database for you is not advised.) Originally inspired by the 'AutoNormalize' library for 'Python' by 'Alteryx' (<a href="https://github.com/alteryx/autonormalize" target="_top"https://github.com/alteryx/autonormalize/a>), with various changes and improvements. Automatic discovery of functional or approximate dependencies, normalisation based on those, and plotting of the resulting "database" via 'Graphviz', with options to exclude some attributes at discovery time, or remove discovered dependencies at normalisation time.
The Importance of Market Surveys in Student Housing - Radix Software
Move-ins are done. Students, eager to learn and enjoying their lives away from home, are roaming through your community’s halls. Your onsite teams are kicking
Wrapper around the Canadian Mortgage and Housing Corporation (CMHC) web interface. It enables programmatic and reproducible access to a wide variety of housing data from CMHC.
Apartment Market Surveys & Product Feedback: Real-World Notes from a 2x PropTech Entrepreneur | HelloData.ai
Over the past month, we finished a few pilots where the primary feedback was that our product was “too detailed” for on-site management teams. Here's how we found out why, and fixed the problem in 7 days.
In this tutorial, you will learn about PostgreSQL foreign key and how to add foreign keys to tables using foreign key constraints.
The following illustrates a foreign key constraint syntax:
[CONSTRAINT fk_name]
FOREIGN KEY(fk_columns)
REFERENCES parent_table(parent_key_columns)
[ON DELETE delete_action]
[ON UPDATE update_action]
In this syntax:
First, specify the name for the foreign key constraint after the CONSTRAINT keyword. The CONSTRAINT clause is optional. If you omit it, PostgreSQL will assign an auto-generated name.
Second, specify one or more foreign key columns in parentheses after the FOREIGN KEY keywords.
Third, specify the parent table and parent key columns referenced by the foreign key columns in the REFERENCES clause.
Finally, specify the desired delete and update actions in the ON DELETE and ON UPDATE clauses.
Since the primary key is rarely updated, the ON UPDATE action is infrequently used in practice. We’ll focus on the ON DELETE action.
PostgreSQL supports the following actions:
SET NULL
SET DEFAULT
RESTRICT
NO ACTION
CASCADE
You will learn about the PostgreSQL temporary table and how to manage it using the CREATE TEMP TABLE and DROP TABLE statements.
When to use temporary tables
Isolation of data: Since the temporary tables are session-specific, different sessions or transactions can use the same table name for temporary tables without causing a conflict. This allows you to isolate data for a specific task or session.
Intermediate storage: Temporary tables can be useful for storing the intermediate results of a complex query. For example, you can break down a complex query into multiple simple ones and use temporary tables as the intermediate storage for storing the partial results.
Transaction scope: Temporary tables can be also useful if you want to store intermediate results within a transaction. In this case, the temporary tables will be visible only to that transaction
Summarizing and Querying Data from Excel Spreadsheets Using eparse and a Large Language Model
Editor's Note: This post was written by Chris Pappalardo, a Senior Director at Alvarez & Marsal, a leading global professional services firm. The standard processes for building with LLM work well for documents that contain mostly text, but do not work as well for documents that contain tabular data (like spreadsheets). We wrote about our latest thinking on Q&A over csvs on the blog a couple weeks ago, and we loved reading Chris's exploration of working with csvs and LangChain using agents, chai