10 Best Data Pipeline Monitoring Tools in 2025

Data Pipeline Monitoring

What Are Data Pipeline Monitoring Tools? Data pipeline monitoring tools ensure the performance, quality, and reliability of data as it moves across systems. These platforms are indispensable for identifying anomalies, detecting bottlenecks, and proactively resolving errors in data flow. With the growing complexity of data pipelines, robust monitoring tools are essential for ensuring seamless operations.…

Read More

Data Migration Strategies to Cut Down Migration Costs by 70%

Databricks Migration

Migrating data can feel overwhelming and expensive. But it doesn’t have to be. With the right strategies, you can actually reduce migration costs by up to 70%. So, what’s the trick? It’s all about being intentional—only migrating the data that’s essential and ensuring its quality. Let’s break down three practical strategies that can make a…

Read More

Seamless Teradata to Databricks Migration: How to Tackle Challenges and Ensure Data Quality With DataBuck

Data Quality with DataBuck

Data migration is one of those projects that often sounds straightforward—until you dive in and start uncovering the layers of complexity. Moving from Teradata to Databricks, a journey many companies are embarking on to boost flexibility and scalability, is a prime example. The promise of enhanced data processing capabilities is there, but so are a…

Read More

How Data Trustability Shapes Acquisition Outcomes: The Veradigm Deal

Data Trustability Shapes Acquisition Outcomes

In recent reports, McKesson (NYSE: MCK) and Oracle (NYSE: ORCL) have emerged as key players in the pursuit to acquire Veradigm (OTC: MDRX), a leading electronic medical records company. With a potential deal expected to exceed Veradigm’s $1B market cap, major private-equity firm Thoma Bravo has also expressed interest, especially given its ties to healthcare…

Read More

Data Observability: a Blueprint for Competitive Advantage in Modern Enterprises

Blueprint for Competitive Advantage

Data Observability: a Must-Have for Modern Enterprises Modern enterprises thrive on data-driven decision-making. Yet, raw data alone offers limited value. Success lies in extracting actionable insights, driving innovation, and maintaining a competitive advantage. This requires a strategic approach to data management. Data observability fills this vital role. It allows businesses to understand their data’s health,…

Read More

Top 5 Challenges of Data Validation in Databricks and How to Overcome Them

Artistic representation of validating data on Databricks.

Databricks data validation is a critical step in the data analysis process, especially considering the growing reliance on big data and AI. While Databricks offers a powerful platform for data processing and analytics, flawed data can lead to inaccurate results and misleading conclusions. Here’s how to ensure your Databricks data is trustworthy and ready for…

Read More

Simpler Data Access and Controls With Unity Catalog 

Data lakes and data warehouses

Foreword: The below blog post is being reproduced on our website with permission from Speedboat.pro as it closely intertwines with FirstEigen’s DataBuck philosophy around building well-architected lakehouses. When building data pipelines, a thorough validation of the data set upfront (I call it ‘defensive programming’) yields great rewards in terms of pipeline reliability and operational resilience.…

Read More

5 Downsides of Informatica Data Quality and How DataBuck Eliminates Them

The Informatica logo against a teal textured background.

Do you know the major downsides of Informatica Data Quality—and how to work around them? Often known as Informatica DQ, this tool is part of the larger Informatica data integration platform. Numerous enterprises rely on it to optimize data quality across both on-premises and cloud systems. However, Informatica DQ is not perfect. Users have reported…

Read More

How to Deploy Data Quality Tools & Data Trust Monitors Across Pipelines to Reduce Dark Data?

Data Integration

As businesses collect ever-increasing volumes of data, the risk of accumulating “dark data”—data that remains unused or untrustworthy—continues to grow. The solution lies in implementing advanced data quality tools and data trust monitors across data pipelines to ensure the accuracy, reliability, and trustability of your data. Seth Rao, CEO of FirstEigen, speaks about building a…

Read More