MarkLogic 11 Enhances Complex, Multi-Model Data Analytics, Deployment, Management, and Auditing

MarkLogic Corporation, the provider of the unified data platform for complex data and metadata management, is unveiling the latest features in its MarkLogic 11 update. Focusing on analytics, simplified development, management, and auditing, the update offers improvements to organizational management of data, including in the cloud.

It is undisputed that organizations face a myriad of challenges when it comes to data management’s modern demands of real-time operations; this demand is only further complicated by the innumerable data source types and the massive quantity of data generation. In response, MarkLogic reflects the same adaptability to change that is required in this ever-expanding data world, according to the company.

“Many think about the data volumes that we're seeing out there. I mean, the data flows keep growing, the archives keep growing, and the number of nodes in MarkLogic clusters are increasing,” explained Matthieu Jonglez, VP of product management at MarkLogic. “And that means that we constantly need to improve our monitoring ability, our deployment capabilities, and integrate in the more modern DevOps architecture and tooling that our customers are using.”

With the MarkLogic data platform, enterprises can utilize data and metadata as a single resource, ultimately increasing efficiency, innovation, and true data agility for multi-model data. The features comprising the MarkLogic 11 update introduce new capabilities to analyze and integrate multi-model data, as well as improve the platform’s ability to manage, audit, and monitor.

“MarkLogic database is unique in the way it is mult-model, so it has document models that can natively do JSON and XML. It can do relational, it can do semantics, it can do bi-temporal. It can do geospatial,” said Jeremy Bentley, EVP of strategy at MarkLogic. “Out there in the field, each of those types of modeling has a different query language, which means that when you're using one of those, you're only getting to one facet of the data. And the real value is in joining all of that data together to really break the data silos.”

Newfound support for GraphQL allows organizations to expose multi-model data to BI tooling, improving overall interoperability with data ecosystems. Other industry standards find support in MarkLogic 11, including OpenGIS and GeoSPARQL, which eases geospatial data queries, and OAuth, which acts as a new option for external authentication.

The previous launch of MarkLogic Optic AI in MarkLogic 9 is expanded upon in this release, providing new capabilities for delivery of multi-model data to BI tools, such as Tableau. Multi-model analytics are also enhanced with greater support for large analytics, reporting, geospatial data/analysis, as well as query exports with external sort and joins.

Agility and streamlined management also see improvements in MarkLogic 11, as new tools allow users to better manage growing volumes of data via larger result set handling at query time and new adaptive memory algorithms. Additionally, support for Docker and Kubernetes enables MarkLogic clusters to be deployed in cloud-neutral, containerized environments that adhere to best practices for optimized value.

Storage failure detection, system health monitoring, and an updated Section 508-compliant UI provides availability in case of system/storage device failures or cloud availability zone failures/brownouts, increased response time to said failures, as well as improved accessibility for all users.

“We have, essentially, at the tool level, two main audiences: data scientists and business analysts who own the data and need to do something with the data—and we're creating an experience for them—but we also have developers that are programmatically accessing the application and building applications on top of it,” said Jonglez. “So, we're modernizing the APIs, we're working on additional connectors, ingress and egress, reviewing our documentation, strategy, and learning platforms, and tailoring design patterns for the types of customers and the type of developers we're seeing as customers, today.”

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