Sprngy implements pipelines and data lakes to provide high performance and secure data access to business as well as for traceability and 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.
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Overview
Sprngy is a unified data and analytics platform aimed at letting users derive insights from raw data in a few clicks.
The data platform provides ability to curate and correlate large amounts of raw data with audit, balance, control and error functionality while providing traceability, quality and governance metrics.
The analytics platform provides rapid access to large amounts of curated and correlated data, rich visualizations and building analytical and predictive models for insights and foresights.
The Sprngy architecture is detailed below.
Data Platform
Sprngy’s data platform uses a model-driven approach. The model driven approach is aimed at capturing:
how you would like your data to behave i.e. meta data model and
what treatment (curation, transformation, filtering, quality, governance and time travel) you would like your data to go through, in what sequence you would like your data to be processed and what insights you want your data to serve you i.e. ingestion model.
What we mean by that is that whether it is data profiling or data correlation or even importing the data from RDBMS or ingestion, curation, profiling, correlation or writing specific algorithms on the data, all of it can be done by defining models model which serve as a blueprint for the processing you want to do.
The model driven approach allows:
You can define the
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extraction, transformation and ingestion without worrying about having to code it
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You can define
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data cleansing and ensure your users have pristine data to work with
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You can define
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rules to
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mirror data
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for specific business uses
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You can define advanced algorithms to provide specific insights on
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curated data
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For advanced users, you can define machine learning routines to provide predictive capabilities
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.
Sprngy’s data platform translates the models provide customization features that can be implemented using regular SQL. We have built versioning capabilities around the models to provide traceability. 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
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