Compare DataBuck vs. TopBraid

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.

Graphs represent one of the most adaptable formal data structures, allowing for straightforward mapping of various data formats while effectively illustrating the explicit relationships between items, thus facilitating the integration of new data entries and the exploration of their interconnections. The inherent semantics of the data are clearly defined, incorporating formal methods for inference and validation. Serving as a self-descriptive data model, knowledge graphs not only enable data validation but also provide insights on necessary adjustments to align with data model specifications. The significance of the data is embedded within the graph itself, represented through ontologies or semantic frameworks, which contributes to their self-descriptive nature. Knowledge graphs are uniquely positioned to handle a wide range of data and metadata, evolving and adapting over time much like living organisms. Consequently, they offer a robust solution for managing and interpreting complex datasets in dynamic environments.