A series of instructive and educational notebooks organized by topic areas.
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Updated
Dec 27, 2023
Google BigQuery enables companies to handle large amounts of data without having to manage infrastructure. Google’s documentation describes it as a « serverless architecture (that) lets you use SQL queries to answer your organization's biggest questions with zero infrastructure management. BigQuery's scalable, distributed analysis engine lets you query terabytes in seconds and petabytes in minutes. » Its client libraries allow the use of widely known languages such as Python, Java, JavaScript, and Go. Federated queries are also supported, making it flexible to read data from external sources.
📖 A highly rated canonical book on it is « Google BigQuery: The Definitive Guide », a comprehensive reference.
Another enriching read on the subject is the inside story told in the article by the founding product manager of BigQuery celebrating its 10th anniversary.
A series of instructive and educational notebooks organized by topic areas.
A collection of R notebooks to analyze data from the Digital Optimization Group Platform
This is a study project. I get analytics/ML examples from Kaggle and use different technologies to re-implement them.
Notebooks for GCP services
Sample queries and visualizations using Google BigQuery in a Python notebook.
This AI Platform notebook guides you through the process of from building a k-means clustering models for market segmentation using BigQuery ML to evaluation using Davies-Bouldin index
This repository contains all practice notebooks with which I performed hands-on labs in Google Cloud Training Program's "Cloud ML-AI Track"
GCP_Data_Enginner
Tellery lets you build metrics using SQL and bring them to your team. As easy as using a document. As powerful as a data modeling tool.
🍟 a notebook sql client. what you get when have a lot of sequels.
Released May 19, 2010