DATA ENGINEERING
The volume of data has grown dramatically over the years, and organizations have realized that insights from data are their biggest assets. Despite the emergence of Big Data Analytics, AI & Machine Learning, and other modern technologies, traditional data warehouse, business intelligence and reporting constitutes a major chunk of workloads. Whether it is retail, insurance, finance, or manufacturing, DW/EDW and BI remains a powerful tool for data analytics, reporting, and visualization. Optigrise follows a unified approach to data engineering using unified tools and processes, extending DevOps & Agile principles to data

Typical Approach
- Solid Approach – Separate tools, process for different teams
- Separate Pipelines – Separate pipeline/data flow b/w traditional data engineering, big data & ML teams.
- Focus on data science only – While AI and predictive analytics solve many use cases, still organisations have huge amount of data in relational and structured form, they should continue to have a strong DW/BI strategy.


Our Approach
- Unified Approach – Unified tools, process
- Unified Pipelines – Unified pipeline from data ingestion, data preparation to visualization for traditional DW/BI, Big data & AI.
- Balanced Approach – Balanced approach b/w traditional DW/BI, Big data & AI.
- Data Ops – Bringing in Devops & Agile Principles to data projects.
- Cost Optimization – Cost saving on DW/BI, so that additional savings could be spent on AI & Big data.