100+ SQL Scripts - PostgreSQL, MySQL, Google BigQuery, MariaDB, AWS Athena. DevOps / DBA / Analytics / performance engineering. Google BigQuery ML machine learning classification.
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Updated
Jul 29, 2023 - Shell
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.
100+ SQL Scripts - PostgreSQL, MySQL, Google BigQuery, MariaDB, AWS Athena. DevOps / DBA / Analytics / performance engineering. Google BigQuery ML machine learning classification.
Code samples to go with InfoQ article
An example Dataform project to load and transform the publicly available dataset from H&M Group into a format which could be imported into Discovery AI for Retail or Vertex AI Search and Conversation, , allowing you to train a retail recommendations model.
An example Dataform project to load and transform the publicly available dataset from IMDB.
Business model representation automation
Copy table from one dataset to another in google big query using bash script
The Data Pipeline using Google Cloud Dataproc, Cloud Storage and BigQuery
Download meteorological information from the Spanish agency (AEMET) and upload it to BigQuery
dbt: write nothing, generate (almost) everything.
Export BigQuery data to Cloud SQL
An example Dataform project which will use the publicly available Movielens dataset to demonstrate how to upload your product catalog and user events into either the Google Cloud Retail API or Google Cloud Discovery Engine and train a personalised product recommendation model.
Released May 19, 2010