Compare DataBuck vs. Orion Data Validation Tool

Big Data Quality must always be verified to ensure that data is safe, accurate, and complete. Data is moved through multiple IT platforms or stored in Data Lakes. The Big Data Challenge: Data often loses its trustworthiness because of (i) Undiscovered errors in incoming data (iii). Multiple data sources that get out-of-synchrony over time (iii). Structural changes to data in downstream processes not expected downstream and (iv) multiple IT platforms (Hadoop DW, Cloud). Unexpected errors can occur when data moves between systems, such as from a Data Warehouse to a Hadoop environment, NoSQL database, or the Cloud. Data can change unexpectedly due to poor processes, ad-hoc data policies, poor data storage and control, and lack of control over certain data sources (e.g., external providers). DataBuck is an autonomous, self-learning, Big Data Quality validation tool and Data Matching tool.

The Orion Data Validation Tool serves as an integration validation solution designed to facilitate business data validation across various integration channels, ensuring compliance with data standards. By harnessing diverse sources and platforms, it enhances data quality effectively. This tool combines integration validation with machine learning features, positioning itself as a holistic solution for data validation, which ensures the accuracy and completeness necessary for sophisticated analytics endeavors. Additionally, it offers a collection of templates that expedite the data validation process and optimize the overall integration workflow. Users can choose from an extensive library of relevant templates or utilize custom files from any data source they prefer. Upon receiving a sample file, the Orion Data Validation Tool adeptly adapts to meet the specific requirements of that file. Subsequently, it assesses the data against established quality standards, while the integrated data listener provides real-time feedback on data validity and integrity scores. Through these capabilities, users can trust in the reliability of their data for informed decision-making.