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 pipelines 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 data pipelines can encounter issues that compromise data quality. This is where the concept of data trust scores, in conjunction with Unity Catalog, comes into play to introduce a powerful safeguard – Data Pipeline Ci ..read more

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. That said, hand-coding DQ rules is a non-trivial task. This is where FirstEigen’s DataBuck can apply sophisticated AI and ML techniques to automatically monitor your data for you. The best part ..read more

Around the globe, the number of connected devices forming the Internet of Things (IoT) is growing rapidly, with current projections predicting the total fleet of IoT devices will double — from 15.1 billion in 2023 to 29 billion — before the end of the decade. As devices proliferate, organizations increasingly rely on IoT analytics to guide decisions and strategies.  However, extracting accurate, reliable data analytics from IoT data feeds is a complex task with many potential pitfalls for data quality. This guide examines the major challenges associated with achieving high-quality IoT ana ..read more

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 several downsides that can affect their use of the tool and impact their organization’s data quality. That’s why a growing number of Informatica users are turning to FirstEigen’s DataBuck. It complements Informatica DQ by providing faster, easier-to ..read more

Seth Rao, CEO of FirstEigen, speaks about building a data trustability platform, ensuring data trustworthiness, the importance of a data trust score, how everyone in a business is a stakeholder, the need for accountability, and a glimpse of change. There is growing importance of data trustability as the volume of data being collected increases, which can result in errors and diminished trust in the data’s accuracy and reliability. FirstEigen, address this issue by creating a next gen data quality platform, leveraging AI and ML technologies to measure data trustability of every data set anywher ..read more

Is your organization ready to implement a data mesh architecture? Building a data mesh involves transitioning from a centralized to a decentralized data management model. You need to create a framework that pushes data storage and management from a monolithic entity to multiple data domains while improving access and scalability. To do this, you need to know the principles of data mesh architecture and how to apply them in the real world.  Quick Takeaways A data mesh is a distributed framework for decentralized data storage and management. The four principles of data mesh are data as a p ..read more

How well does your organization integrate data from multiple sources? Effective data integration is critical to turning raw data into actionable insights. You need a data integration solution that takes data disparate, often incompatible sources, monitors its data quality, and stores that data in an easily accessible format that everyone in your organization can use. Read on to learn more about the challenges, best practices, and available tools for creating a state-of-the-art data integration solution.  Quick Takeaways Data integration consolidates data from multiple sources into a sing ..read more

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 standards. That is why understanding the data preparation process is crucial. Quick Takeaways Data preparation turns raw data into processed, reliable information for an organization. Without proper data preparation, inaccurat ..read more

Do you know how data is ingested into a system? Can you distinguish between a data pipeline, data framework, and data process flow? Like all organizations, yours relies heavily on data to inform its operating and strategic decision-making. So, you need to know as much as possible about the data that flows into and is used by your organization, including data ingestion, pipelines, frameworks, and process flows.  Quick Takeaways Data ingestion is how new data is absorbed into a system. A data ingestion pipeline is how data is moved from its original sources to centralized storage.  A ..read more

Harnessing the power of cloud-based workflow management has become indispensable in modern IT environments. Amazon Web Services (AWS) offers Amazon Managed Workflows for Apache Airflow (MWAA), a crucial tool that simplifies complex computational workflows and enables Managed Airflow on AWS.  In 2022, AWS’s revenue surpassed $80 billion, indicating its prominent role in the growing cloud services industry. Additionally, the cloud market is expected to see an annual growth rate of 31% in 2023, marking the rising demand for automated and remote work solutions like Managed Airflow. This guide ..read more