Complex system for data pipeline test automation

A system may execute a pipelined multiple-tier test stack to support migration of computing resources via a migratory data stream. Via the pipelined multiple-tier test stack, the system may perform extract, transform, and load operations on the migratory data stream. The extract, transform, and load operations may be used to identify applications that may undergo testing. At a generation tier of the pipelined multiple-tier test stack, the system may generate test scripts, which may be used to test the application. The tests may be validated by the system via a validation tier of the pipelined multiple-tier test stack. To govern the operations, the pipelined multiple-tier test stack may rely on a multi-point reference data model.

PRIORITY

This application claims priority to Indian Patent Application No. 202141009274 filed Mar. 5, 2021, titled Complex System for Data Pipeline Test Automation, and to Indian Patent Application No. 202141029354 filed Jun. 30, 2021, titled Complex System for Data Pipeline Test Automation, which are incorporated by reference in their entirety.

TECHNICAL FIELD

This disclosure relates to a complex system for data pipeline test automation.

BACKGROUND

The processing power, memory capacity, available disk space, and other resources available to computer systems have increased exponentially in recent years. Diverse computer systems are deployed worldwide in great numbers to host an immense number of data platforms running extremely diverse applications. Improvements in moving applications between systems and deployment environments will further advance the capabilities of these computer systems.

DETAILED DESCRIPTION

In various scenarios, a cloud computing system (or other computing system) may undergo the technical process of migration from one computing platform to another computing platform. A data pipeline may be used to stream data (e.g., as a migratory stream) from the source platform to the target platform using extract, transform, and load (ETL) operations. In various cases, consistent operation before and after (and/or improved operation after) migration may be dependent on validation of numerous computational components and/or massive quantities of data. In some cases, logic governing such operations may be complex. The complexity may present challenges in maintaining consistent forward operation where the data pipeline operates to migrate the computing system. The complexity may present challenges in reverse-referencing (e.g., back tracing) to identify an origin for an error when inconsistency is encountered.

In various implementations, a multi-point reference data model and/or multi-point reference placement model may be used to implement flexible and consistent forward operation. In some cases, this multi-point reference data/placement model may support initiating forward operation from any of various points in the streaming process. In various implementations, the multi-point reference data model may be used to support reverse-referencing to back-trace inconsistent operation (and/or validate virtually any type of operation). Dual support for forward-referencing and reverse-referencing at multiple operation points provides an improvement in the operation of hardware-based migration systems in the form of increased reliability in operation and faster (and more efficient) error tracing (e.g., through improved back tracing) when inconsistent operation is identified. Thus, the multi-point reference data model provides technological improvements over existing market solutions.

FIG.1shows a pipelined multiple-tier test stack100that may be implemented on migration circuitry. The operation of the migration circuitry may be governed by data validation logic and job validation logic, either of which may be subject to performance validation testing on the test stack100. The migration circuitry may carry out the operations of the pipelined multiple-tier test stack100. The pipelined multiple-tier test stack100may include an extraction tier110, which may handle a migratory data stream and or job detail manifest reception (e.g., extraction, transformation, and/or loading of data and/or job details); a script tier115, which may handle generation of test scripts (e.g., which may be based on enterprise rules); a test tier120which may handle test execution; a validation tier130, which may handle data-agnostic and/or job-agnostic validation of the data tests and/or operation-agnostic of the workflow tests; a presentation tier140which may handle presentation of outputs, parameters, options, controls or other interface elements to interfaces for operators and/or control systems (in some implementations the presentation tier may support one or more application programing interfaces (APIs) for integration with various client systems); various status check tiers150which may be interspersed among the other tiers to check the status of the system at various stages of operation. Operations at the various tiers may be triggered via operations at other tiers to support cascaded pipelined operation.

A stack may refer to a multi-tiered (or multi-layered) computer architecture that defines the interaction of software and hardware resources at the multiple layers. The Open Systems Interconnection (OSI) model is an example of a stack-type architecture. The tiers (e.g., layers) of a stack may pass data and hardware resources among themselves to facilitate data processing.

Referring now toFIG.2, example data validation logic (DVL)200, which may be executed by the test stack100, is shown. The DVL200may receive a migratory data stream (202). The migratory data stream may include a data stream to support migration of one or more computing resources, such as that to support a migration between cloud providers and/or computing environments.

The DVL200may receive an extraction trigger indicating reception of the migratory data stream (204). For example upon receiving the data stream, the DVL200may generate (to support cascaded execution) an extraction trigger to indicate that extraction should begin. In some cases, the extraction trigger may be manually provided through a control interface. The DVL200may then pass the extraction trigger to the extraction tier to initiate extraction-transform-load (ETL) operations on the data in response to the extraction trigger.

Via the ETL operations, the DVL200may determine one or more applications in the migratory data stream (206). The DVL200may reverse-reference an enabled-listing252of a multi-point reference data model250to determine whether the selected application is present. When the selected application is present on the enabled-listing252, the DVL200may continue on to other applications within the data stream. When the selected application is not present on the enabled-listing252, the DVL200may cause (initiate generation of) a generation trigger responsive to the selected application (208).

The multi-point reference data model250may include an enabled-listing252of validated applications. The multi-point reference data model250may further include a script module254to support generation of test scripts. The validation module256may track validation requirements. The multi-point reference data model250may further include application configuration data in an application configuration module258. The interaction of the modules may be governed by a workflow management module260which may operate as a portion of the DVL200. In some implementations, workflow platforms such as Airflow or Google Cloud Platform may be used implement workflow management.

The DVL200may pass the generation trigger to a script tier115of the test stack. Responsive to the generation trigger and at the script tier, the DVL200may forward-reference the script module254of the multi-point reference data model250to identify a test condition for the selected application (210). The test condition may include one or more factors (e.g., data structure requirements, data handling requirements, data form requirements, or other requirements) which the DVL200may test (and then validate) before marking the selected application as enabled. The DVL200may generate a test script for the selected application responsive to the test condition (212). The test script may include instructions for testing the relevant factors. Upon generating the test script, the DVL200may cause a test trigger to initiate operation of the test tier (214).

At the test tier120, the DVL200may execute the test script and generate a return with a specific data-type (216). For example, the test script may perform the selected application on data (e.g., enterprise data, dummy data, or other data) to generate the return. The specific data-type may result from the execution of the selected application and/or the data-type of the data that was input into the selected application. In some cases, to continue cascaded operation, the DVL200may cause generation of a validation trigger to initiate operation of the validation tier (218).

At the validation tier130, the DVL200may forward-reference the validation module256of the multi-point reference data model250to identify a data-agnostic validation-grouping including the specific data-type (220). The data-agnostic validation-grouping may include a set of data-types that may be validated using a data-agnostic validation common to the members of the data-agnostic validation-grouping. For example, the data-agnostic validation-grouping may include a comparison of a form of the data (e.g., the presence/non-presence of changes, columns, or other forms) with a template. Membership within the data-agnostic validation-grouping may indicate that one or more data-agnostic validations may be used on the data.

In various implementations, data-agnostic validations may include presence analyses that determine validity based on the presence of particular data in the result. In various implementations, data-agnostic validations may include absence analyses that determine validity based on the absence of particular data in the result. In various implementations, data-agnostic validations may include fetch analyses that determine validity based on whether a data fetch operation occurred. In various implementations, data-agnostic validations may include dimension change analyses that determine validity based on slowly changing dimension analyses. Data-agnostic validation-groupings may be selected based on the relevance of the particular data-agnostic validation to the specific data-type of the result of the application being tested.

Responsive to the validation trigger, the DVL200may reverse reference the validation module256of the multi-point reference data model250to determine whether the validation condition indicated a success for the data-agnostic validation (222). When the validation condition does not indicate a success, the DVL200may forgo addition of the selected application to the enabled-listing252of the multi-point reference data model. Additionally or alternatively, the DVL200may generate an error message indicating the failure for the selected application (e.g., for display at a control interface generated at the presentation tier, as discussed below). Responsive to a successful validation, the DVL200may add the selected application to the enabled-listing252of the multi-point reference data model (224). Additionally or alternatively, the DVL200may generate a success message indicating the success for the selected application (e.g., for display at a control interface generated at the presentation tier, as discussed below).

The DVL200may further implement operations at a status check tier150, which may request status information (e.g., success/failure information, throughput information, performance data, progress data, and/or other status information) from the other tiers.

In some implementations, the DVL200may implement a control interface142at the presentation tier140. The control interface may be used to receive operator instructions and/or feedback responsive to the status information. Further, the control interface142may display error messages and/or success messages in response to validations. In some cases, the control interface142may be dynamically rendered to allow for context specific displays of options and information for operator overseeing a computing resource migration.

Referring now toFIG.3, example job validation logic (JVL)300, which may be executed by the test stack100, is shown. The JVL300may receive a job detail manifest (302). The job detail manifest may include job details to support orchestration of a migration of one or more computing resources, such as that to support a migration between cloud providers and/or computing environments.

The JVL300may receive an extraction trigger indicating reception of the job detail manifest (304). For example upon receiving the job detail manifest, the JVL300may generate (to support cascaded execution) an extraction trigger to indicate that extraction operations job detail manifest should begin. The JVL300may then pass the extraction trigger to the extraction tier to initiate extraction-transform-load (ETL) operations, e.g., such as metadata extraction, schedule extraction, execution log extraction, and/or other extractions, on the job detail manifest in response to the extraction trigger.

Via the ETL operations, the JVL300may determine one or more job placements in the job detail manifest (306). For example, the JVL300may determine when, how often, at what speed, with what resources, and/or under what other conditions a selected migration job may be performed. The JVL300may reverse-reference an enabled-listing352of a multi-point reference placement model350to determine whether the selected job placement is present. When the selected job placement is present on the enabled-listing, the JVL300may continue on to other job placements within the manifest. When the selected job placement is not present on the enabled-listing, the JVL300may cause (initiate generation of) a generation trigger responsive to the selected job placement (308).

The multi-point reference placement model350may include an enabled-listing352of validated placements. The multi-point reference placement model350may further include a script module354to support generation of test scripts. The validation module356may track validation requirements. The interaction of the modules may be governed by a workflow management module360which may operate as a portion of the JVL300.

The JVL300may pass the generation trigger to a script tier115of the test stack. Responsive to the generation trigger and at the script tier, the JVL300may forward-reference a script module354of the multi-point reference placement model350to identify a test condition for the selected job placement (310). The test condition may include one or more factors (e.g., timing requirements, performance requirements, data form requirements, or other requirements) which the JVL300may test (and then validate) before marking the selected application as enabled. The JVL300may generate a test script for the selected job placement responsive to the test condition (312). The test script may include instructions for testing the relevant factors. Upon generating the test script, the JVL300may cause a test trigger to initiate operation of the test tier (314).

At the test tier120, the JVL300may execute the test script and generate a return with a specific job placement (316). For example, the test script may place the job within a specific execution context (e.g., schedule, number of run times, specific assignment of execution resources, and/or other context) to generate the return. In some cases, to continue cascaded operation, the JVL300may cause generation of a validation trigger to initiate operation of the validation tier (318).

At the validation tier130, the JVL300may forward-reference the validation module356of the multi-point reference placement model350to identify a job-agnostic validation-grouping including the specific job placement (320). The job-agnostic validation-grouping may include a set of job placements (e.g., execution contexts) that may be validated using a job-agnostic validation common to the members of the job-agnostic validation-grouping. For example, the job-agnostic validation-grouping may include a comparison of a scheduling of the job (e.g., when a job is executed, the order in which a job is executed, the frequency at which the job is executed, and/or other scheduling factors) with a template. Membership within the job-agnostic validation-grouping may indicate that one or more job-agnostic validations may be used on the specific job placement.

Job-agnosticism may be a feature of tests that may be applied to jobs and/or job placements that are independent of details specific to individual jobs or job placements. In other words, job-agnostic validations provide the flexibility of allowing reuse on a variety of different jobs in a variety of different execution contexts.

In various implementations, job-agnostic validations may include comparing a scheduled number of run times with an expected number of run times. In various implementations, job-agnostic validations may include comparing identifiers for one or more scheduled runs. For example, the comparison may include presence or absence comparison (e.g., versus a template) for the identifiers. In various implementations, job-agnostic validations may include a performance validation based on one or more performance metrics (e.g., throughput metrics, processing speed metrics, memory utilization, and/or other metrics).

Responsive to the validation trigger, the JVL300may reverse reference the validation module356of the multi-point reference placement model350to determine whether the validation condition indicated a success for the job-agnostic validation (322). When the validation condition does not indicate a success, the JVL300may forgo addition of the selected job placement to the enabled-listing of the multi-point reference placement model. Additionally or alternatively, the JVL300may generate an error message indicating the failure for the selected job placement (e.g., for display at a control interface generated at the presentation tier140, as discussed below). Responsive to a successful validation, the JVL300may add the selected job placement to the enabled-listing352of the multi-point reference placement model (324). Additionally or alternatively, the JVL300may generate a success message indicating the success for the selected job placement (e.g., for display at a control interface generated at the presentation tier140, as discussed below).

The JVL300may further implement operations at a status check tier150, which may request status information (e.g., success/failure information, throughput information, performance data, progress data, and/or other status information) from the other tiers.

In some implementations, the JVL300may implement a control interface142at the presentation tier140. The control interface may be used to receive operator instructions and/or feedback responsive to the status information. Further, the control interface142may display error messages and/or success messages in response to validations.

FIG.4shows an example execution environment (EE)400for implementing the pipelined multiple-tier test stack. The EE400may include system logic414to support data migration and/or job placement. The system logic414may include processors416, memory420, and/or other circuitry, which may be used to implement the example DVL200and/or the example JVL300, which may provide software support to implement the various tasks performed by the pipelined multiple-tier test stack100. Thus, in some cases, the system logic414may act as the migration circuitry.

The memory420may be used to store parameters422and/or model templates424used in the pipelined multiple-tier test stack. The memory420may further store rules421that may facilitate model management and/or the execution of other tasks.

The memory420may further include applications and structures, for example, coded objects, templates, or one or more other data structures to facilitate model management, pipelined multiple-tier test stack operation, and/or the execution of other tasks. The EE400may also include one or more communication interfaces412, which may support wireless, e.g. Bluetooth, Wi-Fi, WLAN, cellular (3G, 4G, LTE/A), and/or wired, ethernet, Gigabit ethernet, optical networking protocols. The communication interface412may support communication, e.g., through the communication tier as network interface circuitry, with data sources or resources used to facilitate model management, pipelined multiple-tier test stack operation, and/or the execution of other tasks. Additionally or alternatively, the communication interface412may support secure information exchanges, such as secure socket layer (SSL) or public-key encryption-based protocols for sending and receiving private data. The EE400may include power management circuitry434and one or more input interfaces428.

The EE400may also include a user interface418that may include man-machine interfaces and/or graphical user interfaces (GUI). The GUI may be used to present interfaces, such as those generated at the presentation tier140, and/or options to facilitate model management, pipelined multiple-tier test stack100operation, and/or the execution of other tasks.

Example Implementations

Various implementations have been specifically described. However, many other implementations are also possible. For example, the example implementations included below are described to be illustrative of various ones of the principles discussed above. However, the examples included below are not intended to be limiting, but rather, in some cases, specific examples to aid in the illustration of the above described techniques and architectures. The features of the following example implementations may be combined in various groupings in accord with the techniques and architectures describe above.

FIG.5shows show example cascaded operation500of the test stack100. Various tiers may generate triggers that initiate execution of later tiers. Status check tiers150may be interspersed among operation of the other tiers. In various implementations, a master configuration file502may serve as the basis for generating the multi-point reference data model and multi-point reference placement model for operation of the test stack100. Accordingly, the master configuration file502may be invoked to execute either job testing and/or data testing. Additionally or alternatively, the master configuration file502may provide details to support performance validation.

FIG.6shows an example operational flow600that may be used to coordinate operation of the DVL200. The migration data is obtained from the data sources602. The migration data is processed used operational data604, e.g., from a master configuration file. Tests based on the operational data are then executed606and then validated608. The control interface is then used to report610results from the validations and tests. Workflow management612(e.g., as part of the DVL200and/or JVL300may coordinate the operations).

FIG.7shows an example data-agnostic validation platform700. The validation tier702obtains (e.g., via action at other tiers) data from sources data warehouses704and/or Data APIs (application programming interfaces)706. The validation tier702obtains test scenarios (e.g., from the master configuration file or other configuration sources) to produce the validated results708.

FIG.8shows an example job-agnostic validation platform800. The extraction tier802obtains \ job details and configuration data804(e.g., from the master configuration file to determine job placements). The test tier806provide execution results for validation at the validation tier808, which output the validated results810.

FIG.9shows an example performance validation platform900. The platform may use a streaming system902. The simulated stream may be monitored by the validation tier906to produce validated performance results910related to data operations and/or job placement.

FIG.10shows an example control interface1000for data validation. The example control interface1000interface includes control windows of selection of the migratory data stream (e.g., ETL Name)1002, identification of the source files1004for the migratory data stream, and the target file location1006where the files will migrated. The example control interface1000may include a field for indication of the test script1008. In some cases, a dialogue box1010may be used to present updates to status information such as errors or success indications.

FIG.11shows an example control interface1100for orchestration validation. The example control interface1100interface includes control windows of selection of the job detail information (e.g., ETL Name)1102and the test result storage location1104. The example control interface1100may include a field for indication of the test script1108. In some cases, a dialogue box1110may be used to present updates to status information such as errors or success indications.

FIG.12shows an example control interface1200for orchestration validation. The example control interface1200interface includes control windows of selection of the migratory data file/job detail information (e.g., ETL Name)1202and the test result storage location1204. The example control interface1200may include a field for indication of the test script1208. In some cases, a dialogue box1210may be used to present updates to status information such as errors or success indications.

FIG.13shows a second example operational flow1300that may be used to coordinate operation of the DVL200and/or JVL300. The migration data is obtained from the data sources1302. The migration data is processed for intake via ETL tools1304(e.g., at the extraction tier). Test documents1306(which may include a master configuration file) may provide operational data for orchestration or other operational data. The testing may be performed under coordination by workflow management1308(e.g., as a portion of the DVL200and/or JVL300). The testing may performed using various testing components. For example, a file to database (DB) population1352component may be used which enables data validation when the data source is a file and target is a database. For example, a DB to DB validation1354component may be used which enables data validation when the data source is a database and target is a database. For example, a file to file validation1356component may be used which enables data validation when the data source is a data file and target is a data file. File to DB1352, DB to DB1354, and File to File1356components may use the DVL200to coordinate data-agnostic validations of the data migration.

In various scenarios, the testing may include orchestration validation1358, which may be governed by the JVL300. Additionally or alternatively, performance validations1360and/or user interface (UI) validations1364may be implemented using the performance/UI controls of the DVL200and/or JVL300. A test data generator1362may be used to generate synthetic data (which may be fed to the ETL tools1304) for use in testing. Additionally or alternatively, the test data generator1362may be used to generate bad data to test validation sensitivity (e.g., the ability to detect data that should be denied validation).

The implementations may be distributed as circuitry, e.g., hardware, and/or a combination of hardware and software among multiple system components, such as among multiple processors and memories, optionally including multiple distributed processing systems. Parameters, databases, and other data structures may be separately stored and managed, may be incorporated into a single memory or database, may be logically and physically organized in many different ways, and may be implemented in many different ways, including as data structures such as linked lists, hash tables, arrays, records, objects, or implicit storage mechanisms. Programs may be parts (e.g., subroutines) of a single program, separate programs, distributed across several memories and processors, or implemented in many different ways, such as in a library, such as a shared library (e.g., a Dynamic Link Library (DLL)). The DLL, for example, may store instructions that perform any of the processing described above or illustrated in the drawings, when executed by the circuitry.

Various implementations have been specifically described. However, many other implementations are also possible. Table 1 includes examples.

TABLE 1ExamplesA1. A method including:at network interface circuitry, receiving a migratory data stream;using migration circuitry executing a pipelined multiple-tier test stack:at an extraction tier of the test stack:receiving an extraction trigger indicating reception of the migratory datastream; andresponsive to the extraction trigger performing an extract-transform-load(ETL) operation on the migratory data stream to determine a selectedapplication present among multiple applications in the migratory datastream;reverse-referencing an enabled-listing of a multi-point reference data model todetermine whether the selected application is present;when the selected application is not present on the enabled-listing, causing ageneration trigger responsive to the selected application;at a script generation tier of the test stack:responsive to the generation trigger, forward-referencing a script moduleof the multi-point reference data model to identify a test condition for theselected application;responsive to the test condition, generating a test script for the selectedapplication; andgenerating a test trigger;at a test tier of the test stack, executing the test script responsive to the testtrigger to generate a return of a specific data-type;causing a validation trigger responsive to execution of the test script;at a validation tier of the test stack:forward-referencing a validation module of the multi-point reference datamodel to identify a data-agnostic validation-grouping including thespecific data-type;responsive to the validation trigger, executing a data-agnostic validationon the return to identify a validation condition, the data-agnosticvalidation assigned to the data-agnostic validation-grouping, the data-agnostic validation independent of a format specific to the specificdata-type; andreverse-referencing the validation module of the multi-point referencedata model to determine that the validation condition indicated asuccess for the data-agnostic validation; andresponsive to the success, adding the selected application to the enabled-listing of the multi-point reference data model.A2. The method of example A1 or any other example in this table, where the multi-point reference data model supports forward-referencing, reverse-referencing, orboth at multiple points within the multi-point reference data model.A3. The method of example A2 or any other example in this table, where forward-referencing includes referencing the multi-point reference data model to determinean action to support advancement of validation.A4. The method of example A2 or any other example in this table, where reverse-referencing includes referencing a current result to interpret a previous result.A5. The method of example A1 or any other example in this table, where:the test stack further includes a presentation tier; andthe method further includes generating an control interface configured to receive aselection of the multi-point reference data model;selection of a test script; orboth.A6. The method of example A1 or any other example in this table, where the data-agnostic validation is independent of a format specific to the specific data-type due adata generic analysis.A7. The method of example A6 or any other example in this table, where the datageneric analysis includes a source-target comparison analysis including acomparison of entries to factor out data type dependence.A8. The method of example A6 or any other example in this table, where the datageneric analysis includes a performance validation based on a performance metricfor execution of the test script.A9. The method of example A6 or any other example in this table, where the datageneric analysis includes absence analysis including a determination of validitybased on an absence of particular data in the specific data-type.A10. A method including:at network interface circuitry, receiving a job detail manifest;using migration circuitry executing a pipelined multiple-tier test stack:at an extraction tier of the test stack:receiving a extraction trigger indicating reception of the job detailmanifest; andresponsive to the extraction trigger performing metadata extractionoperation on the job detail manifest to determine a selected jobplacement present among multiple job placements in the job detailmanifest;reverse-referencing an enabled-listing of a multi-point reference placementmodel to determine whether the selected job placement is present;when the selected job placement is not present on the enabled-listing, causinga generation trigger responsive to the selected job placement;at a script generation tier of the test stack:responsive to the generation trigger, forward-referencing a script moduleof the multi-point reference placement model to identify a test conditionfor the selected job placement;responsive to the test condition, generating a test script for the selectedjob placement; andgenerating a test trigger;at a test tier of the test stack, executing the test script responsive to the testtrigger to generate a return of a specific placement;causing a validation trigger responsive to execution of the test script;at a validation tier of the test stack:forward-referencing a validation module of the multi-point referenceplacement model to identify a job-agnostic validation-groupingincluding the specific placement;responsive to the validation trigger, executing a job-agnostic validationon the return to identify a validation condition, the job-agnosticvalidation assigned to the job-agnostic validation-grouping, the job-agnostic validation independent of an actual schedule specific to thespecific placement; andreverse-referencing the validation module of the multi-point referenceplacement model to determine that the validation condition indicated asuccess for the job-agnostic validation; andresponsive to the success, adding the selected job placement to the enabled-listing of the multi-point reference placement model.A11. The method of example A10 or any other example in this table, where themulti-point reference placement model supports forward-referencing, reverse-referencing, or both at multiple points within the multi-point reference placementmodel.A12. The method of example A11 or any other example in this table, where forward-referencing includes referencing the multi-point reference placement model todetermine an action to support advancement of validation.A13. The method of example A11 or any other example in this table, where reverse-referencing includes referencing a current result to interpret a previous result.A14. The method of example A10 or any other example in this table, where:the test stack further includes a presentation tier; andthe method further includes generating an control interface configured to receive aselection of the multi-point reference placement model;selection of a test script; orboth.A15. The method of example A10 or any other example in this table, where the job-agnostic validation is independent of an execution context specific to the specificplacement due a job generic analysis.A16. The method of example A15 or any other example in this table, where the jobgeneric analysis includes comparing a scheduled number of run times to anexpected number of run times.A17. The method of example A15 or any other example in this table, where the jobgeneric analysis includes comparing identifiers for one or more scheduled runs.A18. The method of example A15 or any other example in this table, where the jobgeneric analysis includes a performance validation based on a performance metricfor execution of the test script.A19. A product including:machine-readable media other than a transitory signal;instructions stored on the machine-readable media configured to, when executed,cause a machine to:at network interface circuitry, receive a migratory data stream;using migration circuitry execute a pipelined multiple-tier test stack:at an extraction tier of the test stack:receive an extraction trigger indicating reception of the migratorydata stream; andresponsive to the extraction trigger perform an extract-transform-load (ETL) operation on the migratory data stream to determinea selected application present among multiple applications inthe migratory data stream;reverse-reference an enabled-listing of a multi-point reference datamodel to determine whether the selected application is present;when the selected application is not present on the enabled-listing,cause a generation trigger responsive to the selected application;at a script generation tier of the test stack:responsive to the generation trigger, forward-reference a scriptmodule of the multi-point reference data model to identify a testcondition for the selected application;responsive to the test condition, generate a test script for theselected application; andgenerate a test trigger;at a test tier of the test stack, execute the test script responsive to thetest trigger to generate a return of a specific data-type;cause a validation trigger responsive to execution of the test script;at a validation tier of the test stack:forward-reference a validation module of the multi-point referencedata model to identify a data-agnostic validation-groupingincluding the specific data-type;responsive to the validation trigger, execute a data-agnosticvalidation on the return to identify a validation condition, thedata-agnostic validation assigned to the data-agnosticvalidation-grouping, the data-agnostic validation independent ofa format specific to the specific data-type; andreverse-reference the validation module of the multi-pointreference data model to determine that the validation conditionindicated a success for the data-agnostic validation; andresponsive to the success, add the selected application to the enabled-listing of the multi-point reference data model.A20. The product of example A19 or any other example in this table, where themulti-point reference data model is configured to support forward-reference,reverse-reference, or both at multiple points within the multi-point reference datamodel.B1. A method including:at network interface circuitry, receiving a migratory data stream;using migration circuitry executing a pipelined multiple-tier test stack:at an extraction tier of the test stack:receiving an extraction trigger indicating reception of the migratory datastream; andresponsive to the trigger performing an extract-transform-load (ETL)operation on the migratory data stream to determine a selectedapplication present among multiple applications in the migratory datastream;reverse-referencing an enabled-listing of a multi-point reference data model todetermine whether the selected application is present;when the selected application is not present on the enabled-listing, causing ageneration trigger responsive to the selected application;at a script generation tier of the test stack:responsive to the generation trigger, forward-referencing a script moduleof the multi-point reference data model to identify a test condition for theselected application;responsive to the test condition, generating a test script for the selectedapplication; andgenerating a test trigger;at a test tier of the test stack, executing the test script responsive to the testtrigger to generate a return of a specific data-type;causing a validation trigger responsive to execution of the test script;at a validation tier of the test stack:forward-referencing a validation module of the multi-point reference datamodel to identify a data-agnostic validation-grouping including thespecific data-type;responsive to the validation trigger, executing a data-agnostic validationon the return to identify a validation condition, the data-agnosticassigned to the data-agnostic validation-grouping, the data-agnosticvalidation independent of a format specific to the specific data-type;andreverse referencing the validation module of the multi-point referencedata model to determine that the validation condition indicated asuccess for the data-agnostic validation; andresponsive to the success, adding the selected application to the enabled-listing of the multi-point reference data model.B2. The method of example B1 or any other example in this table, where the multi-point reference data model supports forward-referencing and/or reverse-referencingat multiple points within the multi-point reference data model, where:forward-referencing includes referencing the multi-point reference data model todetermine an action, data item, operation, or other entry to support advancement oftesting and/or validation;reverse-referencing includes referencing a result (or model result) to interpret aprevious result or outcome of an action taken by the migration circuitry, where:optionally, forward-referencing and/or reverse-referencing allows for forwardoperation and/or back-tracing of the operation of the migration circuitry.B3. The method of any of the examples in this table, where the execution of thetest stack on the migration circuitry is governed by migration logic.B4. The method of any of the examples in this table, where the test stack includesone or more additional tiers, where, optionally, the one or more additional tiersinclude status checks for test operations and/or validation operations.B5. The method of example 4 or any of the examples in this table, where the oneor more additional tiers include a presentation tier that supports generation ofinterface elements for operator control and/or monitoring.B6. The method of any of the examples in this table, where the data-agnosticvalidation is independent of a format specific to the specific data-type due a datageneric analysis, where:optionally, the data generic analysis includes a source-target comparisonanalysis that compares entries to factor out data type dependence;optionally, the data generic analysis includes a presence analysis thatdetermines validity based on a presence of particular data in the result;optionally, the data generic analysis includes an absence analysis thatdetermines validity based on an absence of particular data in the result;optionally, the data generic analysis includes an fetch analysis that determinesvalidity based on an whether a data fetch operation occurred;optionally, the data generic analysis includes a fetch analysis that determinesvalidity based on an whether a data fetch operation occurred;optionally, the data generic analysis includes dimension change analysis thatdetermines validity based on a slowly changing dimension analysis.B7. A system including circuitry configured to implement the method of any of theexamples in this table, where:optionally, the circuitry is included within a mobile device;optionally, the circuitry is included within computing hardware (localized,distributed, virtualized, serverless, and/or other computing platform).B8. A product including:machine-readable media; andinstructions stored on the machine-readable media, the instructions configured tocause a machine to execute the method of any of the examples in this table, where:optionally, the machine-readable media is non-transitory;optionally, the machine-readable media is other than a transitory signal; andoptionally, the instructions are executable.B9. A method including implementing any of or any combination of the featuresdescribed in the disclosure.B10. A system including circuitry configured to implement any of or any combinationof the features described in the disclosure.B11. A product including:machine-readable media; andinstructions stored on the machine-readable media, the instructions configured tocause a machine to implement any of or any combination of the features describedin the disclosure, where:optionally, the machine-readable media is non-transitory;optionally, the machine-readable media is other than a transitory signal; andoptionally, the instructions are executable.

Headings and/or subheadings used herein intended only to aid the reader with understanding described implementations.