The Data Load Controller lets you load and unload data without worrying about the underlying implementation details.Documentation Index
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Overview
The Data Load Controller supports managing data within Atoti Market Risk. It provides an interface between the ETL (Extract, Transform Load) layer and the management of what data is loaded into Atoti Market Risk. There are three main parameters in this interface (all of which are configurable):- Operation: the operation the DLC performs. Typically, this is load or unload.
- Topics: the list of data sources defined in the ETL for which to perform the operation. Topic aliases are used to easily specify groups of topics.
- Scope: a filter on the data to be loaded. For example, to load/unload data for a particular as-of date.
Initial load
Starting up the application triggers the initial loading of data. This is managed programmatically within Atoti Market Risk, with Spring beans initiating the loading of topics and scopes at startup. For more information, see Initial Load.Data Orchestrator
Beyond the initial data load, the data orchestrator is a custom external component that is responsible for managing (loading and unloading) data in Atoti Market Risk. For example, the same process that copies the data ready for Atoti Market Risk to load can also use the REST service to tell the Data Load Controller to trigger the loading of the data into Atoti Market Risk.REST Service
The DLC provides a REST service which the data orchestrator (or other external component) can use to manage the loading and unload of data in Atoti Market Risk. The DLC REST API has two endpoints:- execute: for loading and unloading data.
- status: to check on a previous command.
Execute
Forexecute, requests use POST and send JSON objects with the
following fields:
Operation: ’load’ or ‘unload’.
- load: load the data according to topic and scope.
- unload: delete data from the datastore according to topic and scope. This doesn’t depend on how or when the data was loaded.
This doesn’t delete reference data that was added as part of the data normalization. For example, if you delete sensitivities, the corresponding risk-factors are not removed.