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AI Database Generator
AI Database Generator
AI Database Generator is a sophisticated tool that utilizes artificial intelligence and machine learning algorithms to automate the design and creation of database schemas.
·databasesample.com·
AI Database Generator
Rentometer: Rentometer API Docs
Rentometer: Rentometer API Docs
Get a quick rent estimate by address or zip code with Rentometer. Compare rental rates and comps to ensure you're pricing your property right.
·rentometer.com·
Rentometer: Rentometer API Docs
autodb: Automatic Database Normalisation for Data Frames
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.
·cran.r-project.org·
autodb: Automatic Database Normalisation for Data Frames
Access, retrieve, and work with CMHC data.
Access, retrieve, and work with CMHC data.
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.
·mountainmath.github.io·
Access, retrieve, and work with CMHC data.
HelloData - Full Product Demo (6-3-2024)
HelloData - Full Product Demo (6-3-2024)
Power your multifamily rent surveys with real-time data on over 25M units nationwide, sourced entirely from property websites and public data sources.
·youtu.be·
HelloData - Full Product Demo (6-3-2024)
PostgreSQL Foreign Key
PostgreSQL Foreign Key
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
·neon.tech·
PostgreSQL Foreign Key
PostgreSQL Copy Table: A Step-by-Step Guide
PostgreSQL Copy Table: A Step-by-Step Guide
In this tutorial, you will learn how to copy an existing table to a new one using various PostgreSQL copy table statements.
To copy a table completely, including both table structure and data, you use the following statement: CREATE TABLE new_table AS TABLE existing_table;
·neon.tech·
PostgreSQL Copy Table: A Step-by-Step Guide
PostgreSQL Temporary Table
PostgreSQL Temporary Table
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
·neon.tech·
PostgreSQL Temporary Table
Summarizing and Querying Data from Excel Spreadsheets Using eparse and a Large Language Model
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
·blog.langchain.dev·
Summarizing and Querying Data from Excel Spreadsheets Using eparse and a Large Language Model