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5 Reasons Why SQL is Still the Most Accessible Language for New Data Scientists

ODSC - Open Data Science

Though both are great to learn, what gets left out of the conversation is a simple yet powerful programming language that everyone in the data science world can agree on, SQL. But why is SQL, or Structured Query Language , so important to learn? Let’s start with the first clause often learned by new SQL users, the WHERE clause.

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Citus 12: Schema-based sharding for PostgreSQL

Hacker News

What if you could automatically shard your PostgreSQL database across any number of servers and get industry-leading performance at scale without any special data modelling steps? And if you want to see demos of some of this functionality, be sure to join us for the livestream of the Citus 12.0 Updates page. Let’s dive in!

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How to Talk to Your Data with ChatGPT, Snowflake, & dbt

phData

From there, that question is fed into ChatGPT along with dbt data models that provide information about the fields in the various tables. From there, ChatGPT generates a SQL query which is then executed in the Snowflake Data Cloud , and the results are brought back into the application in a table format.

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Visualizing graph data without a graph database

Cambridge Intelligence

When you design your data model, you’ll probably begin by sketching out your data in a graph format – representing entities as nodes and relationships as links. Working in a graph database means you can take that whiteboard model and apply it directly to your schema with relatively few adaptations. age > 50 AND p2.gender

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The Ascent of ChatGPT

ODSC - Open Data Science

Since its release on November 30, 2022 by OpenAI , the ChatGPT public demo has taken the world by storm. It is the latest in the research lab’s lineage of large language models using Generative Pre-trained Transformer (GPT) technology. This could be achieved through the use of a NoSQL data model, such as document or key-value stores.

Database 124
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Building a Machine Learning Feature Platform with Snowflake, dbt, & Airflow

phData

Setup The demo is available in this repo. Creating an end-to-end feature platform with an offline data store, online data store, feature store, and feature pipeline requires a bit of initial setup. Creating the Feature Store This demo uses Feast as the feature store, Snowflake as the offline store, and Redis as the online store.

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Implementing Knowledge Bases for Amazon Bedrock in support of GDPR (right to be forgotten) requests

AWS Machine Learning Blog

For this example, we created a bucket with versioning enabled with the name bedrock-kb-demo-gdpr. Select the uploaded file and from Actions dropdown and choose the Query with S3 Select option to query the.csv data using SQL if the data was loaded correctly. After you create the bucket, upload the.csv file to the bucket.

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