Data modeling is a crucial step in designing and implementing effective data storage and retrieval systems. Two widely used techniques in data...
Category: Data Engineering
LLMs & Data Engineering
Increase productivity of your data engineering team and optimize your delivery pace with the help of LLMs.The world of data is exploding, and data...
FAIR Principle —Riding the Data Wave: Building a Sustainable Data Architecture
The amount of data we generate continues to explode at an unprecedented rate. Estimates suggest that in 2023, a staggering 120 zettabytes of data...
Managing the Trust -Estimating “Blast Radius” in Data Quality: A Strategic Approach
Data has become the lifeblood of organizations, driving critical business decisions and strategies. However, the utility of these data are...
Self-Service Analytics for BI Use-Cases: Balancing the Buffet Approach with Challenges
Self-service Business Intelligence (BI) represents a strategic shift in how companies approach data analysis and decision-making. By empowering...
Sustainable Data Platform Design: Balancing Efficiency with Environmental Responsibility
While digitizing world, the environmental impact of data-driven technologies has come under increasing scrutiny. Data platforms, forming the...
The Future with Enterprise Data Models in a Self-Serve World
Balancing Precision and Accessibility: The Journey from Data Chaos to ClarityIn the digital age, data is not just an asset; it’s the backbone of...
Day-to-Day Challenges as a Data Engineer or Data Scientist
Are you leading a data team? Are you an individual contributor as a data experts? This article will help you to relate and find better solutions for...
2024 Resolution: Increase Your Productivity as a Data Engineer
Mastering Efficiency and Innovation in the Evolving World of DataWith my years of experience as a data engineer, I’d like to share insights and tips...
Embracing the Shift: Data Architecture in the Age of Context-Driven Analytics
What is “Context driven analytics”?In the ever-evolving landscape of data analytics, the significance of context cannot be overstated....