BigQuery

BigQuery is our primary data warehouse recommendation, and it's been that way for three years running. The serverless model means we don't scope infrastructure management into engagements — clients pay for what they query, storage is cheap, and there's no cluster to tune. For growth-stage companies that don't have a data engineering team, that's a massive advantage.

BigQuery is our primary data warehouse recommendation, and it's been that way for three years running. The serverless model means we don't scope infrastructure management into engagements — clients pay for what they query, storage is cheap, and there's no cluster to tune. For growth-stage companies that don't have a data engineering team, that's a massive advantage.

Where It Excels

The free tier is surprisingly generous — 10GB of storage and 1TB of queries per month. For small clients just getting started with data centralization, this means they can build a real data warehouse without any upfront cost. On-demand pricing at $6.25/TB queried is predictable and reasonable for growth-stage query volumes. We've had clients run sophisticated analytics on BigQuery for under $50/month.

BigQuery's integration with the Google Cloud ecosystem is seamless. If clients are on Google Workspace (most of ours are), adding BigQuery feels natural. The connection to Looker Studio (free) for basic dashboards, and the SQL interface that any analyst can use, means clients get value quickly. The streaming insert capability is also excellent for real-time data pipelines from n8n.

Where It Falls Short

On-demand pricing can surprise clients who write inefficient queries. A poorly-structured SELECT * on a multi-TB table can burn through budget fast. We always implement query cost controls and train clients on partitioning and clustering, but it's a real gotcha for teams new to BigQuery. Slot-based pricing (Editions) makes costs more predictable but the minimum commitment ($1,700/month for 100 slots) is too high for most growth-stage clients.

BigQuery's real-time capabilities, while improving, still lag behind Snowflake for sub-second query latency. If a client needs a real-time operational analytics layer, we sometimes pair BigQuery with a caching layer. The GCP console can also be overwhelming for non-technical clients — it's not as polished as some competing products.

How JMK Uses It

BigQuery is the data hub for most JMK Data Engineering engagements. We pipe data from Shopify, HubSpot, Klaviyo, Stripe, and other tools via n8n into BigQuery tables. Standard architecture: raw data lands in a staging dataset, dbt transforms it into clean analytical models, and Metabase or Looker Studio connects for visualization. We use BigQuery's scheduled queries for lightweight transformations when dbt is overkill for the engagement scope.

Who It's Right For

• Growth-stage companies that need a central data warehouse without hiring a data engineering team.
• Teams already on Google Cloud or Google Workspace — the ecosystem integration is a real advantage.
• Businesses with variable query loads that benefit from pay-per-query pricing rather than fixed compute costs.
• Companies building their first data stack who want to start small and scale without re-platforming.

Who Should Look Elsewhere

• Companies with strict data sovereignty requirements that prevent Google Cloud usage.
• Teams needing sub-second query latency for operational dashboards — consider Snowflake or a dedicated OLAP engine.
• Organizations heavily invested in AWS or Azure where cross-cloud egress costs would be significant.