Current platform delivery

Google Cloud engineering

A Google Cloud Partner with a Data & Analytics competency, delivering BigQuery migration, Dataflow pipelines, Vertex AI serving, and the cost and governance controls that keep them running.

Review a Google Cloud workload See case studies

The operating moment

The migration plan looks clean until week three, when a downstream report disagrees with its source and nobody can say which is right. Equivalence, cost, and ownership decide whether the target environment ever becomes the primary one.

Google Cloud Partner: Select tier

Move the workload, prove the data, hand over the keys.

A Google Cloud migration succeeds when three things are true at once: the workload performs at predictable cost, the data is provably equivalent to the source, and your team can operate the result without the migration team. BluePi engineers deliver all three, from BigQuery and Dataproc estates to Dataflow pipelines and Vertex AI serving.

SmartMigrate speeds up estate discovery, conversion, review, and reconciliation where it fits the source and target technologies. The migration record keeps every exception owned and resolved, so cutover is a decision rather than a leap.

When this is the right starting point

A Cloudera, Hadoop, or legacy warehouse estate blocks analytics and AI work
BigQuery costs surprise you every month and nobody owns the query patterns
A migration was scoped but the inventory and dependencies are still unclear
Models are ready to serve and the platform underneath them is not

Good fit

One operating workflow has a named owner, a measurable baseline, and users who can judge whether the result improves.

Poor fit

The request is capacity-only staffing, an unowned demonstration, or a broad transformation without a first decision and finish condition.

Evidence produced during delivery

Baseline and decision definition
Data and system-boundary map
Evaluation or reconciliation result
Runbook and ownership transfer

What BluePi delivers on Google Cloud

Migration, pipelines, and AI serving delivered as complete workloads, not service configurations. Every engagement ends with a system your team can operate and a record showing the data matches.

BigQuery architecture, migration, and cost control
Dataflow pipelines and change data capture
Vertex AI serving with evaluation and monitoring
Governance, observability, and runbooks your team owns

Why teams choose BluePi for Google Cloud

Google Cloud partners are easy to find. What is harder to find is a partner that carries the whole workload: the platform build, the data validation, the cutover, and the operating handover. BluePi engineers work inside your team from inventory to the first month of stable operation, and the Data & Analytics competency means the platform work connects to real analytics and AI outcomes rather than ending at infrastructure.

Proof from live migrations

The PeopleStrong migration moved a Cloudera estate to BigQuery with SmartMigrate, verified by source-to-target reconciliation. The Google Cloud migration case study records a staged analytical workload move with rollback available at every stage. Both name the constraint, the validation method, and the result.

Start with one operating workflow.

We will review the owner, the baseline, the data path, the system boundary, and the route to go-live.

Review a Google Cloud workload