BigQuery
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.
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the portable Python dataframe library
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Modern and easy to use SQL client for MySQL, Postgres, SQLite, SQL Server, and more. Linux, MacOS, and Windows.
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Privacy and Security focused Segment-alternative, in Golang and React
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Logica is a logic programming language that compiles to SQL. It runs on Google BigQuery, PostgreSQL and SQLite.
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The leading data integration platform for ETL / ELT data pipelines from APIs, databases & files to data warehouses, data lakes & data lakehouses. Both self-hosted and Cloud-hosted.
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HTTP Archive's annual "State of the Web" report made by the web community
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Python x GCP x dbt x PowerBI project. I built a datawharehouse in Google BigQuery based on data I scraped from several chocolate makers and distributors websites in France. Such data were transformed in dbt and visualized in a PowerBI dashboard.
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Jun 8, 2024 - Jupyter Notebook
Fast, Simple and a cost effective tool to replicate data from Postgres to Data Warehouses, Queues and Storage
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Released May 19, 2010
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