Data Engineering and Governance
Accelerate the RoI of your data investments while achieving trust and compliance.
Organizations need to be agile with evolving customer demands and preferences. Even when they are saddled with complex tech, data, and cloud infrastructure, enterprises need an efficient, reliable, and trustworthy data life cycle. How do organizations achieve agility? The foundation is set by the data engineering and governance practices for businesses including:
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1. A trustworthy data management practice and security controls enabling analytics and AI models
2. Realizing the value and actionable intelligence from the fast-growing enterprise data pool
3. Sustainable data preparation methodologies to access insights from structured and unstructured data
Our Data Engineering Services
Integration of data from multiple sources and handling of structured, semi-structured, and unstructured from stream and batch processing.
Data Storage ETL/ELT
Extraction, transformation, and storing of data in various relational, big data, and cloud storage depending on the format, volume, and velocity of data.
Building automated, reusable and independent data workflow pipelines to transfer data and process batch and real-time data.
Data Governance –
What do we do?
Saxon’s End-to-end Solution
Are you looking for a one-stop solution that can automate your data pipelines and perform a few auto-cleansing routines and create a dashboard in a day?
With in-built data engineering components, pre-built dashboards, and industry-specific AI and ML use cases, InsightBox operates with efficiency at the nucleus.
InsightBox addresses the needs across your data lifecycle by connecting your data sources, organizing and preparing data for insights, and simplifying dashboards and predictive insights.
- Cloud-agnostic – On-prem, Azure, AWS, or Google
- Scalable with self-service capabilities powered by low-code
- Holistic across the data engineering and insights to reduce cost, time, and data teams effort by around 50%
- Highly secure