Google Cloud handles AI and machine learning, data analytics, compute services and database management for wealth-management firms. Milemarker connects to it and brings data files, bigquery datasets and storage objects into a data environment your firm owns and governs — not a copy that lives inside someone else's platform.
Overview.
Google Cloud handles AI and machine learning, data analytics, compute services and database management for wealth-management firms. Like every system in your stack it holds a slice of the firm — gemini enterprise, AI threat defense, BigQuery and cloud SQL — under its own export limits, retention rules and terms governing what you may do with your own information. Milemarker starts with that question rather than with a pipeline. We help you establish what your Google Cloud agreement permits you to extract, store and reuse, then land data files, bigquery datasets and storage objects over whichever connection method fits your setup into a Snowflake environment your firm owns and administers, normalized into a model that stays stable even when the systems around it change. From there Google Cloud data is no longer a silo. It joins your custodial, CRM and planning data under one set of rules — row-level entitlements, PII masking and SOC 2 Type II controls you set, rather than the scope any one vendor happens to offer.
How it works.
We start by reviewing what Google Cloud holds for your firm and what your agreements permit you to export, retain and reuse. Data then moves over whichever connection method fits your setup — data files, bigquery datasets and storage objects — and is normalized into a model your firm owns inside its own Snowflake environment. Access is governed there: row-level entitlements, PII masking and audit trails applied per user and per field. From that point Google Cloud data can be combined with any other source you hold the rights to use.


