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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Bruin is a data pipeline tool that is designed to be easy-to-use. It allows building data pipelines using SQL and Python, and has built-in data quality checks.
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the portable Python dataframe library
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Cloud Dataflow Google-provided templates for solving in-Cloud data tasks
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This Repo contain details related to Data Engineering tech stacks in GCP
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Data policy IN, dynamic view OUT: PACE is the Policy As Code Engine. It helps you to programatically create and apply a data policy to a processing platform like Databricks, Snowflake or BigQuery (or plain 'ol Postgres, even!) with definitions imported from Collibra, Datahub, ODD and the like.
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The open source high performance ELT framework powered by Apache Arrow
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May 31, 2024 - TypeScript
Database replication platform that leverages change data capture. Stream production data from databases to your data warehouse (Snowflake, BigQuery, Redshift) in real-time.
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May 31, 2024 - Go
Released May 19, 2010
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