Data Hub Central is the newest capability of MarkLogic® Data Hub Service, providing a collaborative, self-service user interface for agile data integration in the cloud
San Carlos, Calif. – July 15, 2020 – MarkLogic Corporation, a leader in simplifying cloud data integration, today announced Data Hub Central, the newest capability of MarkLogic Data Hub Service. For users of their cloud service, Data Hub Central provides a simple user interface for self-service data integration where developers, architects, and business analysts can collaborate to integrate, explore, analyze, and share consistent data assets tailored to business needs.
“MarkLogic is well-known in the enterprise for its innovative approach to agile data integration,” said David Gorbet, SVP of Products at MarkLogic. “Data Hub Central takes that approach and packages it into a simple user experience that users can instantly leverage – all without writing any code.”
Enterprise data integration is a team sport with many stakeholders, each holding a piece of the complex data integration puzzle. Data Hub Central enables these teams to meet the complex requirements that they face. It provides a shared cloud-native interface for data stored in MarkLogic Data Hub Service, breaking down both data silos and organizational silos.
For many business analysts, using Data Hub Central will be the first time getting direct access to operational and analytical data at the data layer in a self-serve manner. By relying on MarkLogic’s built-in search capability, Data Hub Central enables analysts to “shop” for the exact data sets needed to solve pressing business problems. Data sets can be saved, shared, and re-used in popular BI tools – without having to make an IT request.
Data Hub Central spans the integration lifecycle in a single interface. It enables architects who are tasked with overseeing data modeling and governance to collaborate with business analysts to define data models and relationships in the language of the business. Systems analysts, who understand source systems and data, can go to Data Hub Central to look at and adjust the model. As the model is adjusted, data can be loaded from multiple source systems to see how it maps to the target model, as it is changed.
Developers benefit from Data Hub Central by having a centralized source of trusted data assets and they do not have to wait on lengthy ETL cycles to get access to the data. While Data Hub Central provides powerful no-code capabilities, it is also extensible. While data experts are using it to integrate the data, developers can write re-usable components that extend and customize it.
Because Data Hub Central runs as part of MarkLogic Data Hub Service, it has all of the same enterprise data security capabilities enforced at the data layer. This means users can take advantage of role-based access control and Infosec teams can trust the proven advanced encryption capabilities.
As a whole, this collaborative approach provides faster results and reduces risk when things change. It is another key step that makes data integration simpler, which is the vision for MarkLogic Data Hub Service. By bringing agile principles to the data management layer, users can iteratively ingest, curate, and access data as they go. That it all happens in a cloud-native environment just increases the agility because there is no infrastructure to manage.
“The end result is that in our constantly unpredictable, changing world, large enterprises with complex data problems can get business value from their data faster. By breaking down barriers between IT and the business and bringing business users closer to the data, organizations can truly unlock their data silos,” noted Gorbet.
Learn more about Data Hub Central by watching the keynote announcement.
MarkLogic helps customers create value from complex data faster. Our platform ingests data from any source, creating and refining metadata to support powerful models. Customers use these models for deep search and query, building enterprise applications and bringing unique insights to analytics and machine learning.