Data lifecycle management for seamless source-to-destination data movement, next-gen analytics and AI integration.
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Parsing engine and interactive mapper.
Precision parsing, mapping, transformation & health data analytics.
Data lifecycle management for seamless source-to-destination data movement, next-gen analytics and AI integration.
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Automated data movement for faster time-to-insights.
Consolidated data for improved accessibility.
Structured data for reporting, analytics and model training.
Data lifecycle management for seamless source-to-destination data movement, next-gen analytics and AI integration.
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Data lifecycle management for seamless source-to-destination data movement, next-gen analytics and AI integration.
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Engineers
Months
TechVariable developed a connector engine, making it possible to extract customizable user-defined data from any data source. The connector supports the OData sources, including Microsoft Azure, SharePoint, Office 365, Salesforce, and Tableau. It also allows data extraction from non-OData sources like JIRA, REST, and SOAP APIs. The extracted data is then brought to the connector/adapter store based on the user inputs.
The client is an HR tech giant that provides its customers with Key Performance Indicators (KPIs). Their customers used disparate, unconnected off-the-shelf applications for various business functions. It prevented them from generating meaningful, organization-wide KPIs. Difficulties in curating the data from such disparate sources hampered any Business Intelligence (BI) operations challenging.
TechVariable developed a connector engine, making it possible to extract customizable user-defined data from any data source. The connector supports the OData sources, including Microsoft Azure, SharePoint, Office 365, Salesforce, and Tableau. It also allows data extraction from non-OData sources like JIRA, REST, and SOAP APIs. The extracted data is then brought to the connector/adapter store based on the user inputs.
The connector would fetch details of available data based on the application chosen by the user. The user can then select the data types required for analysis. The connector then injects the data into the BI pipeline to extract valuable insights in the form of KPIs.
The connector pushes extracted data to the downstream BI pipeline for further processing, analysis, and insights generation.
Using Grakn, the connector generates the schema using the metadata specified by the user.
Once the user defines the data source for extraction, the connector extract metadata. Users can select the exact information they require from this metadata.
A schedule can be set, and the connector extracts the data automatically at predefined periods.
1. The customers can now pull data from multiple, disjointed data sources to generate meaningful, holistic insights and KPIs.
2. The automation through schedulers and data push to BI systems increases the efficiency of the KPI generation.