#SQLFamily, please tell me a few things that you think could be done better in a #TSQL or #SQLServer book.
Any level: Beginner, Intermediate, Advanced
Trying to find if there are any gaps. Thanks!
Plz retweet.
Build an E2E SQL Analyst App on @streamlit 📊
The Na2SQL repo by @HarshadSurya1c is a complete starter kit for you to build an LLM-powered structured analytics app over your SQL database.
Building a well-functioning text-to-SQL app requires a lot more features than just the prompt! This repo includes the following:
1️⃣ An interactive database viewer
2️⃣ Displays both the intermediate SQL queries as well as output tables
3️⃣ Works over multiple tables
4️⃣ Core orchestration logic with @llama_index
This is an extension of @CarolineFrasca 's excellent @streamlit LlamaPack, which allows you to build an app in one line of code.
Check out the repo: https://t.co/q6GwzESHj0
Blog: https://t.co/U4CAFOIUzh
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@bolu_lasun Feedback:
1) use a different font color than the chart element, black or dark grey maybe
2) resize two charts to align with the ones below them
@EdsWonder Feedback: keep related data points close together.
1) you can move the numbers to the empty space at the top
2) bring "Leavers by Gender" bar chart below "% of Leavers" pie chart
3) bring "% of Promotion" pie chart next to "Total Promotions" bar chart
@Sun_Ever_shines 1) red is danger/bad/negative ... use different color for good/positive
2) use consistent color for the same data point e.g. Answered Calls is red or blue in different charts
3) use different colors for resolved/unresolved
Help me make this go viral! Do you know about SQuizL, essentially Wordle for #SQL. It's from #Oracle, but it's fun for the entire #SQLfamily, whatever your #database. I hope you'll check it out and let your followers know about it. https://t.co/5VD0LjQgvu
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This is going to change databases!
There are 1.7 million deployments of PostgreSQL worldwide. This makes it one of the world's most popular relational database management systems.
The team behind @postgresml reached out to me and showed me their open-source extension, PostgresML.
It's pretty cool!
You can use this extension to train a model on text and tabular data using SQL queries.
This example trains a model using XGBoost using a query!
select * from pgml.train('Handwritten Digit Image Classifier',
algorithm => 'xgboost',
'classification',
'pgml.digits',
'target'
);
The extension integrates Hugging Face 🤗 Transformers. You can use it to run NLP models right into the database.
Here is an example to determine the sentiment of some text:
select pgml.transform(
task => 'text-classification',
inputs => ARRAY[
'This product didn't work for our family.',
'This has been the best purchase of the year!'
]
) as positivity;
I had never seen this before.
You can train and run supervised and unsupervised algorithms. You can run pre-trained models. Machine Learning and AI right at the database level!
Here is the GitHub repository: https://t.co/WOmAy6DE55
This post is sponsored by @postgresml and their fully managed cloud service. You can use them in addition to their open-source library. Horizontal scalability and support for JavaScript, Python, and Rust.
Machine learning is now getting everywhere.
How long before this is part of every database management system out there?
PostgreSQL: releases a major version every year.
MySQL: jumps from 5.7 to 8.0 for no reason.
MariaDB: jumps from 5.5 to 10.0 to show it's better than MySQL.
ClickHouse: ties version number to a year.
SQLite: stays at 3.x since 1847.
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