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BigQuery User-Defined Functions and Extensibility

20 posts · May 2010 – Mar 2025

What this trail covers

Google BigQuery's capabilities have evolved to enhance extensibility and enable developers to perform more complex data analysis. Early efforts focused on the core BigQuery service and its SQL interface. Subsequent developments introduced User-Defined Functions (UDFs), allowing users to extend BigQuery's SQL with custom logic written in JavaScript and other languages. This enables more sophisticated data transformations and custom aggregations. This post introduces streaming inserts and time-based data loading, and the ability to connect BigQuery to Google Spreadsheets for data analysis and visualization.

All Google trails
  1. Leveraging BigQuery JSON for Optimized MongoDB Dataflow Pipelines- Google Developers BlogIntroduced native JSON support in Google Cloud Dataflow templates for MongoDB Atlas to BigQuery pipelines. This eliminates the need for complex data transformations, reducing latency, operational costs, and improving query performance by allowing direct loading and analysis of nested JSON data using BigQuery's JSON functions and BQML.
  2. Using BigQuery and Firebase Analytics to understand your mobile app- Google Developers BlogThis post details how to integrate Firebase Analytics data into BigQuery for deeper mobile app analysis. It outlines the process of setting up BigQuery export from Firebase Analytics and demonstrates how to query this data using BigQuery's SQL capabilities, including the use of User-Defined Functions (UDFs) for custom data manipulation and analysis. The post emphasizes the technical steps involved in connecting these two services and leveraging BigQuery for advanced analytics beyond what Firebase Analytics offers natively.
  3. Breaking the SQL Barrier: Google BigQuery User-Defined Functions- Google Developers BlogIntroduced the ability to load and query data using JavaScript User-Defined Functions (UDFs) in Google BigQuery. This allows for custom logic and complex data transformations directly within SQL queries, significantly enhancing BigQuery's analytical capabilities beyond standard SQL.
  4. Updates to Google BigQuery following Cloud Platform Live- Google Developers BlogThis post announces updates to Google BigQuery following Cloud Platform Live. Key updates include the ability to load and query data from Google Cloud Storage, and the introduction of JavaScript User-Defined Functions (UDFs) for more complex data analysis and transformations within BigQuery.
  5. Geoip geolocation with Google BigQuery- Google Developers BlogThis post details how to integrate GeoIP geolocation data into Google BigQuery. It explains how to use the GeoIP database and BigQuery's UDFs to perform lookups and enrich datasets with location information, enabling more sophisticated geographical analysis.
  7. Visualizing airport delay correlations with Google BigQuery and Maps API- Google Developers BlogThis post demonstrates the use of Google BigQuery and the Maps API to visualize correlations in airport delay data. It highlights the ability to join external datasets (like airport locations) with delay data and use BigQuery's analytical capabilities to uncover patterns. The use of UDFs is implied for data manipulation and potentially for integrating with mapping services.
  8. Google BigQuery goes real-time with streaming inserts, time-based queries, and more- Google Developers BlogIntroduced streaming inserts for BigQuery, enabling near real-time data ingestion. Added time-based queries to facilitate analysis of time-series data. This enhances BigQuery's capabilities for real-time analytics and operational reporting.
  9. How Safari Books Online uses Google BigQuery for business intelligence- Google Developers BlogThis post details how Safari Books Online uses Google BigQuery for business intelligence. They leverage BigQuery's ability to handle large datasets and perform complex queries to gain insights into their business. The article highlights how BigQuery enables them to analyze user behavior, sales data, and other critical business metrics, leading to informed decision-making and improved business strategies. It specifically mentions using BigQuery for reporting and analytics to understand customer engagement and content popularity.
  10. Google BigQuery new features: bigger, faster, smarter- Google Developers BlogIntroduced streaming inserts for BigQuery, enabling real-time data ingestion and analysis. Also detailed improvements to time-based data loading, enhancing the handling of time-series data. These features contribute to BigQuery's ability to be bigger, faster, and smarter for data analysis.
  11. BigQuery gets big new features to make data analysis even easier- Google Developers BlogIntroduced streaming inserts for BigQuery, allowing for near real-time data loading and analysis. Also introduced time-based data loading, enabling more efficient handling of time-series data.
  12. Got big JSON? BigQuery expands data import for large scale web apps- Google Developers BlogThis post introduces the ability to import large JSON files into BigQuery, specifically highlighting the expansion of data import capabilities for large-scale web applications. It details how BigQuery can now handle significant volumes of JSON data, implying improvements in data ingestion and processing for web-scale applications.
  13. How redBus uses BigQuery to master big data- Google Developers BlogThis post details how redBus leveraged BigQuery for big data management. It highlights their use of BigQuery for data warehousing, analysis, and reporting, enabling them to gain insights into their operations and customer behavior. The post emphasizes the scalability and performance benefits of using BigQuery for large datasets.
  14. Now in BigQuery: batch queries and a connector for Excel- Google Developers BlogThis post introduces batch queries for BigQuery, allowing for more efficient execution of multiple queries. It also announces a new connector for Microsoft Excel, enabling users to access and analyze BigQuery data directly within Excel spreadsheets. Additionally, it mentions the introduction of streaming inserts and time-based queries, expanding BigQuery's real-time data handling and analytical capabilities.
  15. Helping developers build more applications on Google BigQuery- Google Developers BlogIntroduced streaming inserts and time-based data for BigQuery, enhancing its extensibility and enabling more complex data analysis.
  16. Use Google BigQuery for your visual interactive dashboards- Google Developers BlogThis post introduces the capability of using Google BigQuery for visual interactive dashboards. It highlights how BigQuery can be used to power these dashboards, implying the use of its querying and data retrieval capabilities for visualization purposes. It also mentions streaming inserts and time-based data loading, suggesting advancements in data ingestion and management for real-time analytics.
  17. Google BigQuery brings Big Data analytics to all businesses- Google Developers BlogThis post announces the general availability of Google BigQuery, a fully managed, petabyte-scale data warehouse that enables businesses to run complex analytical SQL queries over large datasets. It highlights BigQuery's ability to handle large datasets and its integration with other Google services, making it accessible for businesses of all sizes.
  18. Google BigQuery Service: Big data analytics at Google speed- Google Developers BlogThis post announces the Google BigQuery Service, highlighting its ability to perform big data analytics at Google speed. It introduces the concept of a massively parallel processing (MPP) architecture for data warehousing and describes the service's capabilities for handling large datasets, including the introduction of streaming inserts and time-based data loading. The post emphasizes the service's performance and scalability for analytical queries.
  19. BigQuery, meet Google Spreadsheets- Google Developers BlogThis post introduces the ability to connect Google Spreadsheets to BigQuery, allowing users to export data from BigQuery to Spreadsheets for analysis and visualization. It also details the use of streaming inserts for near real-time data loading into BigQuery and the introduction of time-based data loading capabilities.
  20. BigQuery and Prediction API: Get more from your data with Google- Google Developers BlogThis post introduces the integration of the Prediction API with BigQuery, allowing users to leverage machine learning models directly within their BigQuery data. This enables more advanced data analysis and predictive capabilities by combining the power of BigQuery for data warehousing with the predictive power of the Prediction API.