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Sprngy Platform Documentation Guide
Release 2.0.0
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This document is intended for:
SPRNGY Administrators
SPRNGY Developers
SPRNGY Architects
This document is a walkthrough to create and edit an Ingest Model through the Sprngy UI.
Pre-requisites:
The list of tools required to run/develop in the UI are as follows:
Visual Studio Code (Developers only)
Python 3.8.10
Flask 1.1.4
Node 14.17.6
npm 7.24.1
R 3.6.3
Sprngy Libraries
Run Analytic Workload
Run Analytic Workloads page let’s the user apply analytics on the processed data. For successfully running an analytic workload, analytical model along with the data on the source location specified in the analytical model is required.
Home Screen:
The image below is the Home screen of the UI. From the side menu open 'Workload Management' option, then select 'Run Analytic Workloads’ option.
Step 1 -
After clicking on the Run Analytic Workload menu option below screen will be displayed.
On this page, select the module and entity for which you want to run an analytic workload. Select the layer on which the processing is to be applied, that is from RDBMS to RDL(raw data lake)/BDL(business data lake) or from RDL to BDL. You can also select if it is an end of day run or and intra day run.
On the right there is a Model status block, that gives information if the meta, ingest and analytical models are saved to the system.
Here, you can either click 'Submit' to run the workload or create a scheduled workload, by clicking on the scheduled workload toggle, to run at a certain interval.
Scheduled Workloads
For scheduling workloads there are two type - Daily, Custom.
In Daily, the workload will run daily at the given time.
In Custom type scheduled workload you can select if you wish to run the selected workload every week, every month or every year.
After selecting the required option click on submit button to initiate the workload processing or schedule creation in scheduled workload case.