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The Sprngy architecture is detailed below.
Data Platform
Sprngy Sprngy’s data platform uses a model-driven approach for data processing. The model driven approach is aimed at capturing:
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You can define the extraction, transformation and ingestion without worrying about having to code it
You can define data cleansing and ensure your users have pristine data to work with
You can define rules to mirror data for specific business uses
You can define advanced algorithms to provide specific insights on curated data
For advanced users, you can define machine learning models routines to provide predictive capabilities
Sprngy Sprngy’s data platform has built in versioning capabilities for model driven approach to provide time travel, audit and traceability of data and model used to process the data. implements pipelines and data stores to provide high performance and
secure data access to business as well as for traceability, auditing. Data lakes are defined to provide raw or stage data, pristine data and business data layers. Also, the storage is columnar to provide rapid access while keeping the costs low. Sprngy’s data platform translates the models into dynamic data pipelines to provide cost effective high performance ingestion. The in built capabilities manage, secure and provide rapid access to underlying data lakes/stores.
Analytic Platform
Sprngy’s analytic platform provides:
SQL access to the data lakes/store
Distributed processing layer for rapid access
Visualization and Business Intelligence capabilities for
Reporting
Charting
Dashboarding
Mining
Trends/pattern detection
Analytics
Descriptive
Diagnostic
Advanced
Predictive