Neo4j has released a new managed cloud service, dubbed AuraDS, that looks to accelerate data science engineering practices for enterprises by offering a library of graph algorithms, machine learning pipelines, and data science methodologies.
Most enterprises have to develop intelligent software for growing business needs, but they lack the time and resources to tackle complex data engineering tasks, thereby delaying the entire process of developing and deploying next-gen applications, according to the company
The new AuraDS service, built around Neo4j’s cloud-based AuraDB, is designed to cut down the time needed to build these intelligent applications, the company said.
Graph databases are especially useful when it comes to modelling social, sales or service relationships, said Holger Mueller, principal analyst at Constellation Research. In addition to Neo4j, Graph databases include Amazon Neptune, TigerGraph, and AnzoGraph.
“The graph is more flexible and more information rich than the traditional relational databases that took over a few years back. Graph databases are now making a comeback,” Mueller added.
AuraDS takes the inherent advantages of a graph database and applies them to data pipelines that can feed machine learning models, Mueller said. However, the analyst said that this process is cumbersome and needs to run flawlessly.
Mueller also believes that there has been growing interest in similar technologies and NetApp’s recent announcement of its intent to acquire data and workflow application-as-a-service provider Instaclustr could be bucketed in the same category.
Features of AuraDS include automated operations, MLops support and one-click backup, Neo4j said. Along with providing a platform to extract meaning from data relationships through 65 graph algorithms in a single workspace, AuraDS offers a drag-and-drop UI to model and import data into a graph.
It has the ability to monitor, patch and backup workloads automatically along with the ability to restore models, according to the company. The fully managed service , which offers a pay-as-you-go pricing model with the option of pausing unused instances, comes with the option to scale up or down compute resources on demand and allows data scientists to backup instances, models, in-memory graphs with a single click, the company said.
At the moment, AuraDS is only available on Google Cloud Platform.
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