Sprngy’s product features make it ideal to handle all use cases across the data life cycle management. For example:
Data Curation:
Importing data from disparate sources and defining 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
Even though being a Unified Data Analytics Platform, the Sprngy architecture is componentized to support specific, granular use cases. This enables Sprngy to be offered in product flavors such as:
High Performance Elastic Layer (HPEL): All-in product features covering entire data life cycle management i.e. Ingestion, Curation, Correlation, Analysis, Visualization and Advanced Analytics
High Performance Query Layer (HPQL): Provides Query and Visualization features with the ability to connect to customer’s business data layer.