Trusted AI & data pipelines.

Pipelines are built visually in Bluemetrix Data Manager and run on Spark underneath. Analysts and engineers work in the same builder: add a source, drop in transformation, tokenization and quality steps, and choose a destination. Engineers can open any step and extend it in code when a pipeline needs custom logic. Your team gets Spark’s scale and speed, and Spark expertise becomes optional rather than a hiring requirement.

Data Manager deploys inside your existing environment and runs on your Spark clusters, so pipelines execute next to the data. It runs on AWS, Azure and Google Cloud, on-prem alongside Cloudera platform clusters, and across hybrid estates with one pipeline definition for both. Records stay in your cloud account or data centre from ingest to delivery. Tokenization happens in-pipeline with SecureToken, which is vaultless, so there is no central token store for data to travel to. For regulated and sovereign environments, every processing step stays inside the jurisdiction you choose.

Quality rules run on ingest, as data enters the pipeline, and at any later step you add them. Each rule checks completeness, validity or schema conformance (for example, Customer.Email matches a valid format, or Order.Amount is greater than zero). Records that pass continue downstream. Records that fail are flagged and separated from the main flow with the failed rule attached, so engineers can inspect, fix and reprocess them. Dashboards, applications and AI models only ever receive data that has passed the checks.

Wherever your data already lives: public cloud, on-prem, hybrid, or isolated and sovereign environments. Because the pipeline definition is portable, a pipeline built for an on-prem cluster today can move to the cloud tomorrow with the same logic, protection policies and quality rules.

Hand-coded Spark gives engineers full control, and every pipeline becomes its own codebase. Tokenization, quality tests and lineage tracking get rebuilt by hand, pipeline by pipeline, and live in scattered repos. Bluemetrix Data Manager keeps Spark as the processing engine and moves those controls into the platform. Every pipeline inherits the same tokenization, quality and lineage from day one, and engineers spend their time on the logic that is unique to the business.

Every tool in a stitched stack has its own configuration, its own policy model and its own view of the data. The risk sits in the “handoff gap”: the moment data passes from the ingestion tool to the transformation tool to the security tool, each one holding a partial picture of what came before. A field tokenized in one path can arrive in clear text through another. Lineage breaks at every vendor boundary. Bluemetrix Data Manager runs ingestion, transformation, tokenization, quality and lineage as one workflow, so a policy defined once applies at every step and the record of processing stays continuous from source to destination.

SecureToken runs as a step in the pipeline itself. AI-powered discovery identifies PII and PHI fields as data arrives, and protection policies replace them with format-preserving tokens before the data moves on. A tokenized email still looks like an email and a tokenized card number keeps its length and format, so joins, analytics and downstream applications keep working. Authorised users and systems can detokenize according to policy. Everyone else, including AI agents, works with tokens.

“Schema drift” is one of the most common reasons pipelines break quietly: a new column, a renamed field or a changed data type upstream, and downstream reports start returning the wrong numbers. Bluemetrix Data Manager detects schema changes at ingest and flags them before they reach downstream systems. Each change is recorded against the pipeline’s lineage, so teams can see exactly when the source changed and which destinations it affects. Engineers review the change, update the field mapping, and the pipeline carries on.

Yes. Bluemetrix Data Manager records lineage automatically for every pipeline run: the source, each transformation, the protection policy applied to each field, the quality results and the destination. For audits under DORA, BCBS 239 or GDPR, teams can show exactly how a given dataset was processed from source to delivery, from a single place.