Compare Dafne vs. DataBuck

Dafne serves as an orchestration and data workflow engine primarily focused on automating data warehouse processes. It streamlines the creation, definition, scheduling, management, and oversight of production workflows and ETLs, enhancing visibility, reliability, and the handling of dependencies, priorities, and internal constraints, ultimately leading to improved service level agreements (SLAs) and overall performance metrics. Additionally, its user-friendly interface enables teams to efficiently adapt to changing data requirements.

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.