Built with BigQuery: How Sift delivers fraud detection workflow backtesting at scale
How Sift Delivers Fraud Detection Workflow Backtesting at Scale powered by BigQuery.
Systems | Development | Analytics | API | Testing
Modern Software Development For Today's Systems & Applications
Unlocking Retail Location Data with CARTO and BigQuery
Retail companies can easily visualize and analyze their geospatial data in BigQuery using the CARTO platform.
Shorten the path to insights with Aiven for Apache Kafka and Google BigQuery
Unify data from diverse sources and formats using Aiven Kafka's open source streaming. Analyze with BigQuery for swift, accurate insights.
Built with BigQuery and Google AI: How Glean enhances enterprise search quality and relevance for teams
Elements of the Google Data Cloud including BigQuery, DataFlow, and Vertex AI are behind Glean’s personalized enterprise search platform.
Getting started with Terraform and Datastream: Replicating Postgres data to BigQuery
Datastream, a serverless database replication service, has integrated with Terraform to enhance app modernization, data analytics and ML pipelines.
The Denodo Platform meets BigQuery
How Denodo Platform’s data integration and data management capabilities work seamlessly with BigQuery.
Built with BigQuery: Aible's serverless journey to challenge the cost vs. performance paradigm
Built with BigQuery: Aible's Serverless Journey to challenge the Cost vs. Performance Paradigm
Built with BigQuery: How BigQuery helps Leverege deliver business-critical enterprise IoT solutions at scale
Leverege uses BigQuery as a key component of its data and analytics pipeline to deliver innovative IoT solutions at scale.
How to use advance feature engineering to preprocess data in BigQuery ML
How to preprocess data using BigQuery ML.
Demystifying BigQuery BI Engine
BigQuery BI Engine is a fast, in-memory analysis system for BigQuery currently processing over 2 billion queries per month and growing. BigQuery has its roots in Google's Dremel system and is a data warehouse built with scalability as a goal. On the other hand BI Engine was envisioned with data analysts in mind and focuses on providing value on Gigabyte to sub-Terabyte datasets, with minimal tuning, for real time analytics and BI purposes.