Google BigQuery for Enterprise Analytics: Getting Started

Google BigQuery is a serverless, highly scalable analytics data warehouse that lets you run SQL queries across petabytes of data in seconds - without managing any infrastructure.

For enterprises looking to centralise analytics from ERP, CRM, and operational systems, BigQuery offers a compelling alternative to traditional data warehouses. Here is how to get started.

Why BigQuery

• Serverless: No clusters to provision, no indexes to tune, no storage to manage. You pay for queries and storage, not idle infrastructure.
• Speed: Columnar storage and distributed processing deliver sub-second query times on datasets that would bring traditional databases to a halt.
• Standard SQL: Your team already knows SQL. No proprietary query language to learn.
• ML built in: BigQuery ML lets you build and deploy machine learning models using SQL syntax.

Architecture: How It Fits Your Stack

BigQuery sits at the analytics layer of your architecture. Data flows in from source systems (ERP, CRM, IoT, SaaS applications) via ETL/ELT pipelines and lands in BigQuery for analysis.

Common integration patterns:

• ERP data: Extract from Dynamics 365 via Data Factory or custom API connectors, load into BigQuery for cross-system analytics.
• Data lake + BigQuery: Store raw data in a data lake (Cloud Storage), then query it directly from BigQuery via external tables - no duplication needed.
• BI layer: Connect BigQuery to Looker Studio or Power BI for dashboards and self-service reporting.

Cost Optimisation Tips

• Use partitioned tables (by date) to limit the data scanned per query
• Cluster tables by frequently filtered columns
• Use flat-rate pricing if your query volume is predictable and high
• Set up query quotas per user or project to prevent runaway costs

At DynamicUnit, we implement BigQuery-based analytics solutions that connect to ERP, CRM, and operational data sources. From architecture design to data migration to dashboard delivery.

Start with BigQuery →