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Data Curation:
Importing data from disparate sources and defining curation treatment (rules for cleansing, transforming, filtering, quality check and governance) to data to build a single source of truth using data lake technology
Defining metadata blueprint with Sprngy’s ‘Meta Model’
Creating a pristine data layer with Sprngy’s dynamic data processing engine
Data Correlation:
Defining relationship between data entities with Sprngy’s intuitive user interface
Creating business-ready data layer primed for rapid access at scale
Data Analysis:
Creating and sharing visualizations with Sprngy’s in-built visualization tool
Creating ad-hoc queries using the query browser available in the visualization tool
Auditing and monitoring the platform with in-built audit visualizations.
Connecting a visualization tool and/or a querying tool of your choice
Making Predictions:
Supporting machine learning model development and testing
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