Define a set of files once and reuse it across transfers
Stop hunting down and reselecting the same files on several servers and storage systems every time you transfer. Define a set of files once in a Dataset in INNORIX Studio, as devices and paths, then have different Flows reference that same Dataset. Separating where the source files live from the per-target transfer job keeps repeating distribution and collection work more consistent.
This shows one Dataset reused across two different transfer jobs. Unlike guide 8, which groups folders on several devices into one collection target, or guide 17, which collects by condition, this video concentrates on using a file set you defined once in several jobs without setting it up again. A Dataset is not a store that replicates the source files onto another server: it is a logical file set that references the connected devices and paths.
Wie es aufgebaut ist
- Prepare the devices and paths the Dataset will cover.
Confirm in Studio Devices that the servers and storage you want are connected, and check the file paths and access permissions on those devices.
- Create the Dataset.
In Datasets, create a new Dataset, give it a name, then add the devices and folder paths you need. Where you need file selection conditions, set the conditions your product supports.
- Review the files the Dataset covers.
Go through the Dataset makeup and its file list to confirm the intended files are the ones referenced. Creating a Dataset does not in itself copy or transfer any file.
- Use the same Dataset in different Flows.
Configure the first Flow to deliver to the analysis server and the second to the archive server, and set the same Dataset as the source for both. Follow the way your Studio version references a Dataset.
- Run each Flow and verify the results.
In Runs, confirm the two transfers are recorded as separate runs, and check the files and the transfer result on each target server. Before you go live, test what a change to the Dataset does to the Flows that already use it.
Wo es eingesetzt wird
- One source reused for analysis and archiving
Quality measurement files across several production servers are defined as a single Dataset, then used in common by the Flow that sends to the analysis server and the Flow that sends to the long-term archive.
- Different delivery schedules per team
The same month-end closing file set goes to the finance analysis team right after the working day and to the audit archive server on an overnight schedule. The source definition is shared while the schedule and destination are managed separately.
- Project output to several destinations
The approved output files spread across the design servers are grouped into a Dataset and used by different Flows sending to the verification environment and the collaboration server.
- Repeated data verification work
When the same source file set goes to the development, test and verification environments, there is no need to name the source devices and folders again each time.
- Common reports across several sites
The report folders on the regional servers are defined as a Dataset and reused for the central consolidation work and for a separate backup and archiving job. The storage rules per target are managed in each Flow.
- Managing the source as the work grows
Even as more Flows reference an existing Dataset, the definition of the file set and the purpose of each transfer job stay managed apart. When the source makeup changes, identify the affected Flows before applying it.
Anwendungsleitfäden
Häufig gestellte Fragen
Is there a solution that manages files scattered across servers as one set and reuses it across transfers?
Dataset in INNORIX Studio defines several devices and paths as a logical file set. Using a Dataset you have built across several Flows lets you design distribution and collection work without configuring the same source locations again for every transfer job.
Does creating a Dataset copy the files to a central store automatically?
No. A Dataset is a logical construct that references the files and paths on connected devices. Files actually move when a Flow that uses that Dataset runs. Creating a Dataset and transferring files are two separate things.
Can one Dataset be used by several Flows that send to different servers?
Yes. The Dataset acts as the shared source while the target device, storage path and schedule of each Flow are configured separately. The actual steps for linking a Dataset to a Flow, and how far that is supported, have to be confirmed in the Studio version you use.
How is a Dataset different from selecting a folder directly as a Flow source?
Naming a single folder directly sets the source location on that one Flow. A Dataset is a unit of configuration for grouping several devices and paths under one name and reusing it, which is useful when several jobs share a file set.
If I change a Dataset's folders or file conditions, do the Flows using it change automatically?
How a Dataset is referenced and when a change takes effect have to be confirmed against the product implementation and your settings. Editing a shared Dataset can affect several jobs, so the safe route is to identify the Flows referencing it before the change and verify the outcome with a test run.
Can a Dataset be used to gather files from several devices onto one server?
Defining the paths on several devices as a Dataset and using it in a collection Flow is worth considering. The actual collection direction, filename conflict handling, storage paths and per-device permissions have to be configured separately. The detailed procedure for grouping folders across devices is covered in usage guide 8.
If two Flows use the same Dataset at once, does the source file get stored twice?
A Dataset does not store files itself. Running each Flow can, however, send the files to two different destinations. Duplicate files and overwrite policy at each destination have to be confirmed from that Flow's settings and from the actual run results.
What does using a Dataset give me when I run transfers repeatedly?
Sharing one definition of the source devices and paths across several transfer jobs cuts duplicated configuration and helps keep operations consistent. Destination, schedule and results stay in the Flow and in Runs, so managing the file set and managing the runs can be kept apart.