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---
title: Best Google BigQuery posts of 2024 | daily.dev
description: The most upvoted Google BigQuery posts of 2024, curated by the daily.dev community.
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og:description: The most upvoted Google BigQuery posts of 2024, curated by the daily.dev community.
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---

# Best of Google BigQuery — 2024

1. 1  
[](/posts/anvaka-map-of-github-inspirational-mapping-gfzepzeii "anvaka/map-of-github: Inspirational Mapping")  
Article  
![Avatar of hn](https://media.daily.dev/image/upload/t_logo,f_auto/v1/logos/hn)Hacker News · 2y  
anvaka/map-of-github: Inspirational Mapping  
This project maps over 400,000 GitHub projects, clustering them based on common stargazers. Using data from GitHub's public activity events, Jaccard Similarity, and AWS for processing, the map visualizes connections between repositories. The layout was computed with ngraph.forcelayout and rendered with maplibre. Clusters and labels were generated with the help of various tools, including ChatGPT. The project is open-source under the MIT license, with contributions welcome.  
52  
3
2. 2  
[](/posts/i-spent-5-hours-learning-how-google-manages-terabytes-of-metadata-for-bigquery--q1etmyej9 "I spent 5 hours learning how Google manages terabytes of metadata for BigQuery.")  
Article  
![Avatar of detlife](https://media.daily.dev/image/upload/s--8lmPEq7G--/f_auto/v1725038518/logos/detlife)Data Engineer Things · 2y  
I spent 5 hours learning how Google manages terabytes of metadata for BigQuery.  
Google BigQuery uses innovative techniques to manage massive amounts of metadata efficiently, treating it as crucial as the data itself. BigQuery's architecture includes Colossus for storage, Dremel for querying, and a dedicated shuffle service, all coordinated by Borg. Metadata is handled in a distributed manner using a unique columnar storage format called CMETA, improving efficiency and performance. Real-time data ensures physical query plans adapt dynamically for optimized results, while integrated metadata scans enhance query processing.  
51
3. 3  
[](/posts/bruin-data-ingestr-ingestr-is-a-cli-tool-to-copy-data-between-any-databases-with-a-single-command-s-ecqa9ehmy "bruin-data/ingestr: ingestr is a CLI tool to copy data between any databases with a single command seamlessly.")  
Article  
![Avatar of pointer](https://media.daily.dev/image/upload/t_logo,f_auto/v1/logos/65d4168582f14e0cb2b46693356e66f1)Pointer · 3y  
bruin-data/ingestr: ingestr is a CLI tool to copy data between any databases with a single command seamlessly.  
Ingestr is a CLI tool that allows you to copy data between databases with a single command, providing features such as incremental loading and easy installation.  
30  
1
4. 4  
[](/posts/unlock-the-power-of-data-analytics-with-free-google-services-n0bprqnyg "Unlock the Power of Data Analytics with Free Google Services")  
Article  
![Avatar of freecodecamp](https://media.daily.dev/image/upload/t_logo,f_auto/v1628412854/logos/freecodecamp)freeCodeCamp · 3y  
Unlock the Power of Data Analytics with Free Google Services  
Learn data analytics using free Google services like Google Sheets, Google BigQuery, Google Colab, and Google Looker Studio for data visualization.  
26
5. 5  
[](/posts/learn-sql-for-analytics-b7y4ymbmp "Learn SQL for Analytics")  
Article  
![Avatar of freecodecamp](https://media.daily.dev/image/upload/t_logo,f_auto/v1628412854/logos/freecodecamp)freeCodeCamp · 2y  
Learn SQL for Analytics  
Learn SQL for Analytics is an 11-hour course on the freeCodeCamp.org YouTube channel. It covers the fundamentals of SQL, includes practical exercises, and is suitable for beginners.  
16
6. 6  
[](/posts/how-to-build-a-rag-chat-app-with-agent-cloud-and-bigquery-jtyx3fge9 "How to Build a RAG Chat App With Agent Cloud and BigQuery")  
Article  
![Avatar of devto](https://media.daily.dev/image/upload/t_logo,f_auto/v1/logos/devto)DEV · 2y  
How to Build a RAG Chat App With Agent Cloud and BigQuery  
Learn how to build a RAG chat app using Agent Cloud and BigQuery. This comprehensive guide takes you through the process step by step, covering topics such as setting up BigQuery, creating a GCP service account key, running Agent Cloud locally, adding models, connecting to BigQuery as a data source, creating tools, agents, tasks, and conversation chat apps.  
15
7. 7  
[](/posts/machine-learning-made-simple-for-data-analysts-with-bigquery-ml-4z01pviyj "Machine Learning Made Simple for Data Analysts with BigQuery ML")  
Article  
![Avatar of kdnuggets](https://media.daily.dev/image/upload/t_logo,f_auto/v1/logos/kdnuggets)KDnuggets · 2y  
Machine Learning Made Simple for Data Analysts with BigQuery ML  
BigQuery ML democratizes machine learning for data analysts by enabling the creation and execution of ML models using SQL queries. It supports tasks such as predictive analytics, classification, recommendation engines, and anomaly detection without requiring knowledge of Python or R. BigQuery ML is scalable, integrated with data storage, fast, and cost-effective, making it ideal for analysts looking to add ML capabilities to their workflows. Key steps include data preparation, model selection, training, evaluation, and prediction.  
13
8. 8  
[](/posts/rust-in-production-for-fun-profit-1llehuxyu "Rust in production, for fun & profit")  
Article  
![Avatar of medium_js](https://media.daily.dev/image/upload/t_logo,f_auto/v1/logos/medium)Medium · 3y  
Rust in production, for fun & profit  
The author shares their experience of rewriting a legacy application in Rust for improved correctness and efficiency. They discuss the building blocks, feature parity, performance, documentation, middlewares, security, and deployment of the Rust application.  
12

[See all Google BigQuery archives](/tags/google-bigquery/best-of)

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