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It’s Essential – Verifying the Results of Data Transformations (Part 2)

Test cases, data, and validation procedures are crucial for data transformations, requiring an understanding of transformation requirements, scenarios, and specific techniques for accuracy and integrity. Data transformations require complex testing due to their sophisticated logic, computations, and dependency on real-time data streams. This necessitates extensive test case design, representative data, automation tools, and robust validation […]

Data Observability 101

Understanding data health involves monitoring and analyzing diverse aspects of data systems – referred to as data observability – to ensure optimal performance and reliability. Metrics are crucial, providing quantitative insights into data flow, processing times, and system resource utilization. They also help identify patterns and detect anomalies in real time. Logs offer a historical record of […]

Data Architecture Trends in 2025

A modern data architecture is required to support the data-driven organization that every enterprise wants to be. Without a solid data architecture – composed of the models, policies, rules, and standards you set for how data is collected, stored, managed, and used – your ability to attain a holistic view of your business, make informed […]

2025’s Game-Changers: The Future of Data Engineering Unveiled

As the digital world grows increasingly data-centric, businesses are compelled to innovate continuously to keep up with the vast amounts of information flowing through their systems. To remain competitive, organizations must embrace cutting-edge technologies and trends that optimize how data is engineered, processed, and utilized. From decentralized frameworks to AI-driven advancements, 2025 is poised to […]

Achieving Cost-Efficient Observability in Cloud-Native Environments

Cloud-native environments have become the cornerstone of modern technology innovation. From nimble startups to tech giants, companies are adopting cloud-native architectures, drawn by the promise of scalability, flexibility, and rapid deployment. However, this power comes with increased complexity – and a pressing need for observability. The Observability Imperative Operating a cloud-native system without proper observability […]

How AI Will Fuel the Future of Observability

We’re seeing a lot of convergence in the market between observability vendors and companies positioned as artificial intelligence (AI) companies. It’s a natural marriage, since AI has the potential to significantly improve what observability does. The question is how to make the best use of AI to support observability in discovering an organization’s unknowns, providing […]

Dynatrace Adds Data Observability to Analytics and Automation Platform

According to a new press release, Dynatrace has introduced AI-powered data observability capabilities for its analytics and automation platform. Named Dynatrace Data Observability, the feature aims to enhance the reliability and accuracy of data in the Dynatrace platform for business analytics, cloud orchestration, and automation. The technology allows teams to rely on high-quality data, ensuring […]

Understanding the Impact of Bad Data

Do you know the costs of poor data quality? Below, I explore the significance of data observability, how it can mitigate the risks of bad data, and ways to measure its ROI. By understanding the impact of bad data and implementing effective strategies, organizations can maximize the benefits of their data quality initiatives.  Data has become […]