Data Trust Scores and Circuit Breakers: Ensuring Robust Data Pipeline Integrity

Digital image representing Informatica data quality.

Data Pipeline Circuit Breakers: Ensuring Data Trust With Unity Catalog  In the fast-paced world of data-driven decision-making, the integrity and reliability of your data are paramount. Data Pipeline Circuit Breakers play a pivotal role in ensuring that data flows smoothly from source to destination, facilitating accurate analytics and informed decision-making. However, even the most robust…

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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.…

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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…

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What is Data Preparation? A 6-Step Guide to Clean, Transform, and Optimize Data for Analysis

Woman tying her shoes in preparation for a run; illustrates the need for data preparation.

Do you know why data preparation is important to your organization? Poor-quality or “dirty” data can result in unreliable analysis and ill-informed decision-making. This problem worsens when data flows into your system from multiple, unstandardized sources. The only way to ensure accurate data analysis is to prepare all ingested data to meet specified data quality…

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3 Ways to Solve Last Mile Data Integrity Challenges With Advanced Observability

Data Integrity

Data quality issues have been a long-standing challenge for data-driven organizations. Even with significant investments, the trustworthiness of data in most organizations is questionable at best. Gartner reports that companies lose an average of $14 million per year due to poor data quality.  Data observability has been all the rage in data management circles for a…

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Why Most Data Quality Programs Fail: Key Insights and Strategies to Succeed

Data Quality Programs

Fortune 1000 organizations spend approximately $5 billion each year to improve the trustworthiness of data. Yet only 42 percent of the executives trust their data. According to multiple surveys, executives across industries do not completely trust the data in their organization for accurate, timely business critical decision-making. In addition, organizations routinely incur operational losses, regulatory…

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How to Scale Your Data Quality Operations With AI and ML?

scale data quality operations

How can you cost-effectively scale your data quality operations as your business scales? The key is to employ artificial intelligence and machine learning technology that can take on an increasing amount of the data quality management chores as they learn more about your organization’s data. It’s all in the about making the best use of…

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The Ultimate Guide to Data Pipeline Tools in 2025

data pipeline monitoring tools and metrics

Welcome to our comprehensive guide on data pipeline tools for 2025! As businesses increasingly rely on accurate and timely data for decision-making, the significance of maintaining an error-free data pipeline has never been more crucial. Errors in data can propagate across an organization, leading to significant impacts on business operations.  This guide will provide you…

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