BigQuery topic
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.
BigQuery-Python
Simple Python client for interacting with Google BigQuery.
datastore-mapper
Appengine Datastore Mapper in Go
crisp-bigquery
Starter project with full stack BigQuery. Allows to overcome customisation restrictions imposed by pre-built dashboards and control data usage. Deploy your own cloud website hydrated by sample BigQuer...
bigquery-schema-generator
Generates the BigQuery schema from newline-delimited JSON or CSV data records.
bigquery_fdw
BigQuery Foreign Data Wrapper for PostgreSQL
sqlglot
Python SQL Parser and Transpiler
alphasql
AlphaSQL provides Integrated Type and Schema Check and Parallelization for SQL file set mainly for BigQuery
bigquery-emulator
BigQuery emulator server implemented in Go
bigquery-to-datastore
Export a whole BigQuery table to Google Datastore with Apache Beam/Google Dataflow
professional-services
Common solutions and tools developed by Google Cloud's Professional Services team. This repository and its contents are not an officially supported Google product.