Posts Tagged ‘Data Validation’
Data Trust Scores and Circuit Breakers: Powering Data Pipeline Integrity
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…
Read MoreSimpler Data Access and Controls with Unity Catalog
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 More5 Downsides of Informatica Data Quality and How DataBuck Eliminates Them
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 MoreThe Quick and Easy Guide to 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…
Read MoreQuality, Validation, and Observability with Snowflake
Do you know how to get optimal use from Snowflake? Snowflake is a data ingestion and warehousing solution used by more than 7,000 companies worldwide. It makes it easy to ingest, retrieve, and analyze data from multiple sources, but it doesn’t guarantee data quality. To optimize results from Snowflake, you need to employ a third-party…
Read MoreData Integrity: The Last Mile Problem of Data Observability
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…
Read MoreEstablish autonomous data observability and trustability for AWS Glue Pipeline in 60 Seconds
Data operations and engineering teams spend 30-40% of their time firefighting data issues raised by business stakeholders. A large percentage of these data errors can be attributed to the errors present in the source system or errors that occurred or could have been detected in the data pipeline. Current data validation approaches for the data…
Read MoreWhy do Data Quality Programs Fail?
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…
Read MoreHow to Scale Your Data Quality Operations with AI and ML
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…
Read MoreAutonomous Cloud Data Pipeline Control: Tools and Metrics
Errors that get seeded in the data as it flows through the data pipeline, propagate throughout the organization and are responsible for 80% of all errors impacting the business users. High-quality data is essential to the success of any business or organization. It’s important to monitor your data pipeline to guard against missing, incorrect, old/not-fresh,…
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