Unleash the power of analytics through
modern data engineering
We enable modern data engineering through integration of various data sources, creation of hybrid data lakes and warehouses and fully operationalized data pipelines to support data driven innovations.
At Sparity, we believe that data engineering is the enabler for data science. Data science consists of AL & ML models and experimentations. They can be developed only when we have a solid data infrastructure, which is possible through data engineering processes like collection, storage and transformation.
We can help you build this framework and use the power of data to reduce operational costs, discover new revenue sources and create new products. Our proven processes ensure all data, internal or external move from identification to storage without losing granularity and value.
Our approach
Data ingestion
We study the different data sources – right from files and data bases to logs and images. At this stage, the objective is to ensure we don’t lose out on any critical information. We collect, aggregate and move all types of data to one central location.
Data transformation & processing
We need to ensure that integrity of the data is maintained while achieving consistency. Data from different sources is transformed and aggregated in a way that it maintains a central data architecture.
Data storage
All the data is now moved into data lakes and data warehouses using on premise or cloud or hybrid architecture.
Data Governance
Effective Standards and Policies to monitor and control usage of data.
Our Offerings
Consulting
- Data Maturity Assessment
- Data Consolidation Strategy
- EDW Roadmap
- Cloud Adoptions Strategy
Design
- Data Warehouse Architecture
- Data Pipelines (SQL & No-SQL)
- Data Lakes and Analytical Sand Boxes
- Cloud Architecture(AWS, Azure,GCP..)
Implementation
- EDW Implementation and Maintenance
- Cloud Migration
- Maintain Data Pipelines for ML
- Data Model Support/Update
Data Engineering Technologies we work with
Data ingestion
storage
processing
ETL
Insights
How Healthcare Leaders Can Cut Reporting Time by 70% Through Modernization
By Naresh Bishnoi
/ November 11, 2026
Healthcare organizations generate an abundance of data in various forms, including clinical, operational, financial, quality, and compliance data. However, many...
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Modernization of Data in Healthcare: From Legacy Reporting to Real-Time Data
By Naresh Bishnoi
/ November 5, 2026
Healthcare organizations generate massive amounts of data daily, whether in electronic health records (EHRs), various clinical and business applications, patient...
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What challenges are healthcare CIOs facing in 2026?
By Naresh Bishnoi
/ November 3, 2026
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The Hidden Cost of Legacy BI Platforms
By Naresh Bishnoi
/ November 2, 2026
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How to Choose an Enterprise App Development Agency
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/ October 23, 2026
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Top 5 Considerations When Choosing a BI Platform in the Age of AI
By Naresh Bishnoi
/ October 21, 2026
Data is very important for modern businesses and making the best use of data using AI can give your enterprise...
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What Is Data Modernization? Benefits, Strategy, and Key Trends for Enterprise Transformation
By Naresh Bishnoi
/ October 16, 2026
Businesses in the 21st century have 2 favorite topics to talk about – AI and data. Because data integrated with...
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