Compare DataBuck vs. Mosaic AIOps

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.

LTI's Mosaic serves as a unified platform that integrates data engineering, sophisticated analytics, automation driven by knowledge, IoT connectivity, and an enhanced user experience. This innovative platform empowers organizations to achieve significant advancements in business transformation, adopting a data-centric methodology for informed decision-making. It provides groundbreaking analytics solutions that bridge the gap between the physical and digital realms. Additionally, it acts as a catalyst for the adoption of enterprise-level machine learning and artificial intelligence. The platform encompasses features such as Model Management, Training at Scale, AI DevOps, MLOps, and Multi-Tenancy. LTI's Mosaic AI is specifically crafted to deliver a user-friendly experience for constructing, training, deploying, and overseeing AI models on a large scale. By amalgamating top-tier AI frameworks and templates, it facilitates a smooth and tailored transition for users from the “Build-to-Run” phase of their AI workflows, ensuring that organizations can efficiently harness the power of artificial intelligence. Furthermore, its adaptability allows businesses to scale their AI initiatives according to their unique needs and objectives.