You can’t answer questions that cross departments when the data lives in separate systems.
Your sales, marketing and operations numbers sit in different systems, and someone pulls them together by hand. By the time the report is ready, it’s out of date or the totals don’t match.
BigQuery is Google Cloud’s serverless data warehouse, so there are no servers for you to run. We design and build BigQuery pipelines that bring data from across your business into a single analytics layer.
BigQuery data warehouse design
Pipelines that load data from your other systems
A clean-up layer built with Dataform or dbt
Cost control for queries and storage
We identify the databases, software tools and files you want to analyze together.
Tables get structured around the questions you need to answer, with query and storage costs in mind.
Data flows in through Dataflow, Fivetran, Airbyte or custom code, and Dataform or dbt cleans it up.
Looker Studio, Power BI, Tableau or your other BI tool connects to the warehouse, and we tune queries to keep costs predictable.
We work on GCP for real production workloads across data analytics, compute and Kubernetes, and we know where the platform’s real strengths and sharp edges live. On BigQuery, that means watching query costs as closely as the data. We are honest about when GCP is the right choice and when another cloud would fit better.
It is if you have data from several sources to analyze together and want a serverless option with no infrastructure to manage. It scales from small projects to very large workloads.
Storage is cheap. Queries are charged by the amount of data scanned, unless you use reserved capacity. We tune queries and storage to keep costs predictable and low.
Yes. BigQuery connects natively with Looker Studio, Looker, Tableau, Power BI and most other BI tools.
Yes. We build pipelines that load data from databases, software tools and files, using Dataflow, Fivetran, Airbyte or custom code depending on your stack.
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