Serverless Analytics in BigQuery
BigQuery runs analytics without servers to size or manage, so teams can query data from across the business in one place and train models directly with BigQuery ML.
Cloud Solutions
Google Cloud
Turn Your Data Into Decisions on Google Cloud
Companies that want their cloud to revolve around data often pick Google Cloud for BigQuery, Looker, and Vertex AI. We migrate workloads onto the platform, bring your data together in BigQuery, and build the analytics and AI applications that use it.
What We Deliver
Where We Make the Difference
The value of Google Cloud shows up once data from across the business lives in one place. We build a secure foundation with VPC networking, IAM, and Cloud Armor, then consolidate data in BigQuery and add governed reporting and machine learning on top.
Governed Reporting in Looker
We build LookML semantic models that define each metric once, giving business users self-service Looker dashboards on BigQuery that match what finance and operations report.
Vertex AI and Gemini
We build machine learning and generative AI solutions on Vertex AI and Gemini, alongside Document AI, for document processing, demand forecasting, and anomaly detection.
Modernize on GKE
Applications and databases move to Google Kubernetes Engine, Cloud Run, and Cloud SQL, cutting the time your team spends patching and scaling infrastructure.
Our Approach
How We Deliver Google Cloud
Step 1: Design the Landing Zone
We design your Google Cloud organization, projects, and landing zone, choosing between Compute Engine, GKE, and managed services based on performance, isolation, and cost needs.
Step 2: Secure the Foundation
Shared VPC design, IAM roles, organization policies, and Cloud Armor rules are in place before the first workload is deployed, so security is built in from the start.
Step 3: Migrate and Consolidate Data
Applications move in planned waves, and data from operational systems lands in BigQuery through monitored pipelines built with Dataflow or Datastream.
Step 4: Activate and Operate
Looker dashboards and Vertex AI use cases go live on the consolidated data, while ongoing monitoring and cost reviews keep the platform efficient.
Why choose Google Cloud over Azure or AWS?
Its main strength is data and analytics, especially BigQuery and Vertex AI. If your priority is consolidating data and building AI on it, Google Cloud is a strong fit, and we will tell you plainly if another cloud suits your applications better.
Can we use BigQuery without moving everything to Google Cloud?
Yes. Many organizations keep applications on another cloud or on-premises and send data to BigQuery through pipelines. We design those pipelines so reporting stays current without moving the applications themselves.
Do we need Looker if we already use Power BI?
Not necessarily. Power BI connects to BigQuery well, and we can build on it if your users already know it. Looker makes the most sense when you want metric definitions governed centrally in LookML.
How do you keep BigQuery costs under control?
We choose between on-demand and capacity-based pricing based on your query patterns, partition and cluster large tables, and set quotas and budget alerts so heavy queries do not cause surprises.
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