Today, many analyses still happen manually: engineers load recorded data into desktop tools, run scripts, and compile results by hand. As data volumes grow and analysis must run across fleets, sites, and programs, this becomes slow, inconsistent, and hard to reproduce.
Merlin brings automated measurement data analysis to where the data lives. It orchestrates server-side jobs, applies the same workflow logic repeatedly, and produces standardized outputs that can be compared across tests and time.
Merlin/SAM is a technology-independent analysis framework that integrates into test data management workflows. It is built to automate analysis, improve comparability of results, and provide process transparency through job tracking and data lineage.
Merlin stands for test data analysis automation and is best paired with test data management systems based on ASAM ODS.
Engineers often run the same validations and reports repeatedly, which consumes time and leads to inconsistent outcomes. Merlin standardizes these workflows and runs them server-side—well suited for large test sets, fleet analysis, and recurring checks that are impractical on individual workstations. Jobs can be queued, scheduled, monitored, and triggered automatically by system events.
Merlin provides open interfaces to build customer-specific workflow templates that can be executed by users or automated processes. Beyond predefined stages and tasks, teams can add custom scripts and components to calculate channels, generate reports, create derived values, produce plots, or package artifacts. The result is a reusable workflow library that scales across teams and projects.
Workflows define how results are produced; jobs are the executed instances with concrete inputs such as measurement IDs, thresholds, and parameters. Merlin makes execution traceable by tracking configuration, progress, and outputs—so results stay reproducible and comparable. Outputs can include new channels, derived values, generated documents, or structured KPI results that remain linked to the correct context.
Test Data Analysis Automation with Merlin/SAM
Merlin connects user-developed algorithms with governed test data and turns them into executable jobs. Each job combines configuration (for example measurement selection and thresholds), input references, and an execution target—then runs on dedicated workers instead of on an engineer’s machine.
Merlin supports different runtime environments depending on the job, such as Python, Java, MATLAB Runtime/Server, Apache Spark, or other execution services. Jobs can be triggered interactively (Merlin UI in ASAMCommander), ad hoc (API), or automatically through connected services such as importers or notification mechanisms.
Administration and responsibility stay clear: administrators register and maintain analysis programs and workflows, while end users typically trigger jobs and consume results in the correct data context.
Merlin separates orchestration from execution: analysis runs on dedicated workers that can scale independently as demand grows. This makes it easier to add compute capacity, introduce additional processing technologies, and run many jobs in parallel without coupling analysis performance to the core data server. Access control and security policies remain governed by the surrounding Test Data Management and server environment.
Merlin runs algorithms in their native environments instead of forcing teams into a single technology stack. Jobs can execute in Python, Java, MATLAB Runtime/Server, Apache Spark, or other execution services, while Merlin keeps orchestration, scheduling, logging, and result collection consistent. This lets teams reuse proven scripts and tools and still operationalize them as controlled, repeatable server-side workflows.
Merlin supports analysis patterns where results must be derived from time-bounded events and summarized into KPIs or labels. Jobs can detect events, compute KPIs for those intervals, and store results in formats suitable for downstream consumption—such as JSON for indexing services. This enables repeatable labeling and KPI production across large data sets and makes results easier to search, compare, and report.
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