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
cloud
storage
processing
ETL
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