Fully managed, serverless enterprise data warehouse with built-in features like machine learning, geospatial analysis, and business intelligence.
Google BigQuery is a fully managed, serverless data warehouse that enables scalable analytics over massive datasets. With built-in machine learning, streaming ingestion, and ANSI SQL support, BigQuery empowers teams to deliver insights fast without managing infrastructure.
With BigQuery, organizations can simplify their architecture, reduce operational overhead, and accelerate analytics and AI projects.
The goal of Data Vault modeling is to adapt to rapidly changing business requirements and support the agile, incremental development of analytics platforms. The hub, link, and satellite structure is highly extensible and granular, allowing changes in design and ELT logic to be implemented without disrupting existing pipelines.
BigQuery’s architecture with its separation of storage and compute, ANSI SQL interface, and ability to handle structured, semi-structured, and streaming data fits perfectly with the Data Vault approach. BigQuery supports native functions such as SHA256 that can be used to create Data Vault hash keys.
VaultSpeed helps you model the Data Vault and delivers the data structures and ELT logic required for BigQuery.
The platform combines both data-driven and model-driven methods:
For the Landing and Integration layers, VaultSpeed provides no-code Data Vault automation. The Data Vault pattern delivers consistency and traceability without requiring custom code.
For the Presentation layer, VaultSpeed’s Template Studio allows you to create any structure needed, including dimensional models, flattened tables, or aggregated datasets.
Use VaultSpeed’s Flow Management Control (FMC) add-on module to ensure that all data pipelines are executed at the right time and in the correct sequence. Deploy and schedule workflows using Google Cloud Composer, Apache Airflow, or your preferred orchestration tool.
To build reliable analytics and AI solutions, you first need trusted and continuously updated data. VaultSpeed ensures that BigQuery receives governed, historical, and current datasets on an ongoing basis. When specifications change, only the analytics or application logic needs to be rebuilt while the Data Vault layer maintains all historical and business context.
VaultSpeed supports both batch and streaming data ingestion patterns for BigQuery.
When streaming is required, VaultSpeed can generate:
This approach keeps your Data Vault up to date so that BigQuery analytics always run on the most current available data.