The Future of ArangoDB and Graph ML
Gary Cripps 
ArangoDB, a NoSQL database and Graph ML market leader, recently announced a $27.8 million Series B investment to accelerate development of its next-generation Graph ML platform, powering advanced analytics at enterprise scale and enabling AI for knowledge graph applications.
This investment round will enable ArangoDB to fuel and accelerate innovation in distributed computing, analytics and artificial intelligence.
This article will provide an overview of ArangoDB’s Graph ML platform and the important implications of this funding round. We will discuss what this investment round could mean for users; how it may benefit the enterprise; and current trends in terms of advances made in Graph ML technology. We will also explore how these developments increase efficiency – specifically as relates to ArangoDB – so that organizations can better use their data for analytics, machine learning, artificial intelligence (AI), recommendation engines, forecasting models, decision support systems and more.
Overview of ArangoDB
ArangoDB is a multi-model NoSQL database that combines the power of graphs, documents, and key-values in one core technology, making it a powerful tool for data analytics and AI development.
On April 20th, ArangoDB announced a $27.8 million Series B investment to accelerate the development of next-generation graph ML, providing advanced analytics and AI capabilities at enterprise scale. This announcement has sparked much interest in the tech industry, and this article aims to provide an overview of ArangoDB and its capabilities.
What is ArangoDB?
ArangoDB is a distributed open-source database system developed by ArangoDB GmbH. It is an accessible and versatile multi-model NoSQL database that combines the power of graphs, documents, search, and key-value models. It provides developers with a powerful data platform for established web technologies like Node.js, modern frameworks like React, Angular, and Vue.js as well as serverless architectures.
ArangoDB is designed to help companies build innovative applications to anticipate customer needs better than the competition. It leverages existing technologies such as Structured Query Language (SQL) and JavaScript Object Notation (JSON) for faster application development timeframes for enterprises to remain competitive in the ever-evolving landscape of customer service operations and customer demands.
By building upon existing data infrastructure layers with its advanced graph analytics capabilities, ArangoDB eliminates the need to know multiple query language types while still enabling extensive query flexibility within a single layer platform (i.e., traverse any graph within the same context). To accomplish this goal, ArangoDB offers graphical model machine learning (Graph ML), which empowers businesses to rapidly innovate by tweaking parameters without needing external scripting logic or additional applications. Furthermore, Graph ML provides performance advantages over traditional data wrangling approaches because it can utilize transactions executed within each model layer separately and reduce memory usage because of its native query compiler implementation.
With Graph ML on board under Enterprise Edition license agreements since 2018 — along with the recent announcement of a $27 million Series B Investment — ArangoDB has become an increasingly attractive option for organizations looking for quick deployment cycles alongside flexibility and scalability when it comes to building innovative products that benefit customers’ needs in the long run.*
History of ArangoDB
ArangoDB is a multi-model database developed by ArangoDB GmbH, founded in 2014 by Claudius Weinberger and Frank Celler. The company is headquartered in Cologne, Germany and provides open source distributed graph database software. ArangoDB provides an enterprise-class, agile and reliable solution for advanced persistent data at web scale.
Since its founding in 2014, ArangoDB has released several versions of its open source software ranging from version 1 through version 5 and is currently incubating version 6. Over the years ArangoDB has become increasingly popular with developers due to its advanced query language AQL, which enables sophisticated data queries with ease on very large datasets. Additionally, the versatility of combining multiple data models (e.g., documents, graphs) makes ArangoDB particularly well suited for real-time analytics applications such as AI/machine learning (ML).
In 2019, ArangoDB announced a $27.8 million Series B investment round led by Hewlett Packard Enterprise (HPE) to accelerate developing next-generation Graph ML capabilities within the software platform. This will enable advanced analytics and AI capabilities at enterprise scale, enabling organizations to process complex queries more quickly.
In addition, with this new capital infusion, more experts are joining the development team and releasing more educational content about the product ecosystem via online training courses and seminars. Ultimately these efforts will help advance the availability and usability of powerful enterprise analytics capabilities powered by multi-model databases like ArangoDB.
ArangoDB Announces $27.8 Million Series B Investment to Accelerate Development of Next-Generation Graph ML, Providing Advanced Analytics and AI Capabilities at Enterprise Scale
ArangoDB announced a $27.8 million Series B investment to help accelerate the development of their next-generation Graph ML technology. This investment will help the ArangoDB team bring advanced analytics and AI capabilities to enterprises.
In this article, we’ll explore the implications of this investment and ArangoDB’s plans for the future.
ArangoDB GmbH, builder of the leading open source multi-model NoSQL database, announced today a $27.8 million Series B investment round to accelerate its accelerated development of a next-generation Graph ML platform – giving customers a modern, agile way to supply intelligent features and inferences using both structured and unstructured data. The round was led by One Peak Partners and Dawn Capital, with participation from existing investor Target Partners.
ArangoDB’s advanced graph and relational capabilities are combined with its scalability and speed, offering organizations that need to make sense of complex interconnections an ideal environment for AI/ML (Artificial Intelligence/Machine Learning) feature development at scale. In addition, customers can deploy ArangoDB across any public cloud or private data centre hub for enterprise-scale mission critical performance.
ArangoDB’s Graph ML platform will allow companies to take advantage of the full range of relationships their data holds grouped by schema-less graphs – resulting in intuitive insights into relationships between people, places, objects, concepts through advanced analytics models such as natural language processing (NLP). Customer use cases include geolocation optimization to build smarter Car2Go ridesharing programmes or detect tendencies in fraud detection processes in banking. This provides a clearer pathway for larger organisations to benefit from AI/ML capabilities than traditional relational databases. AI developers seeking such capabilities will also benefit from ArangoDB’s web provided console tools that decrease infrastructure complexity while simplifying development efforts.
Benefits of Series B Investment
The Series B investment for ArangoDB provides a range of benefits for the company and its customers. This injection of funding will allow ArangoDB to accelerate development of its next-generation graph machine learning (Graph ML) platform, which will provide more advanced analytics and AI capabilities at an enterprise scale.
In addition to driving product development, this investment will further expand the company’s global sales and marketing efforts. With greater resources, ArangoDB plans to attract a wider audience of developers, data scientists and decision makers who are seeking modern data technologies that make sophisticated analytics accessible to everyone in their organization.
The raised funds also expand opportunities for partnerships that enable the delivery of enterprise solutions built on top of ArangoDB. The company has already worked with partners such as Capgemini, Google Cloud Platform (GCP) Marketplace and Snowflake Computing on databases tailored for mission-critical deployments in large enterprises.
Development of Next-Generation Graph ML
ArangoDB, a leading open source multi-model database provider, has just announced a $27.8 million Series B investment to accelerate the development of their next-generation Graph ML platform.
This platform will provide advanced analytics and AI capabilities to enterprises at scale, allowing them to make faster, more informed decisions. The deep-learning powered Graph ML platform will also create new opportunities for businesses to better understand and organize their data.
Let’s take a closer look at the potential of this next-generation Graph ML.
What is Graph ML?
Graph Machine Learning (Graph ML) is an emerging field of research which combines graph theory, data analysis and machine learning to create a powerful form of data processing for large and complex datasets. Graph ML algorithms apply machine learning on the relationships between objects or entities within the data, allowing for more accurate predictions than traditional methods.
In the case of ArangoDB’s Series B Investment announcement, the focus is on applying their advanced analytics and AI capabilities to revolutionize graph-based applications. In particular, they hope to advance the development of Graph ML by making it more accessible and providing sophisticated tools that allow users to query and analyze massive datasets quickly from a single interface. These algorithms will focus heavily on understanding the underlying structure within graphs and leveraging predictive analytics for more predictive tasks.
Furthermore, these algorithms will allow for closer inspections into relationships and create rich visualisations that present opportunities for rapid discovery in highly connected datasets with minimal effort.
In short, Graph ML promises accelerated development capabilities at enterprise scale allowing for deeper insights into dataset structure, creating predictability that shapes our future decisions and provides key insights from unexplored areas of data analysis at a much faster rate than ever before. With its application in finance, insurance, cyber security etc., ArangoDB’s Series B Investment has no doubt ignited growth by helping developers access quality Graph Machine Learning technologies with advanced AI Capabilities at Enterprise Scale!
Benefits of Graph ML
Graph ML provides a powerful array of enterprise-scale analytics and Artificial Intelligence (AI) capabilities. Leveraging the advantages of a graph-based approach enables users to gain deeper insights from data and develop more accurate models for AI applications. In addition, this technology can analyze structured and unstructured datasets, allowing organizations to extract valuable information from various sources.
Graph ML helps network operators gain real-time insights into customer behavior, market trends, server performance and other data stored in their systems. For example, by using graph ML techniques to explore connected networks or individual customers within large populations they can better understand customer needs or target products/services most effectively.
In addition to its analytical applications, Graph ML is suitable for AI environments. In contrast to deep learning, where large neural networks are trained on vast amounts of data, Graph ML encodes knowledge in graphs, which AI models can then apply for prediction tasks such as stream processing for fraud detection or anomaly detection for cybersecurity application security systems.
By investing in further development of its Graph ML capabilities, ArangoDB is helping to enable organizations that want to unlock the full potential of their data by developing advanced analytics and AI capabilities that scale with their business needs. In addition, this investment will help drive innovation into modern computing architectures such as cloud platforms, allowing businesses to utilize the latest cutting-edge machine-learning technologies more quickly on virtually any platform available today.
Advanced Analytics and AI Capabilities
ArangoDB recently announced a $27.8 million Series B investment to accelerate development of new graph machine learning (ML) capabilities, offering advanced analytics and AI capabilities at enterprise scale.
This investment will enable ArangoDB to continue building its ML stack, allowing customers to create more powerful and advanced analytics applications.
Let’s look at what this means for the future of ArangoDB and graph ML in general.
Overview of Advanced Analytics and AI Capabilities
ArangoDB, a German-headquartered provider of next generation distributed graph database technology, recently announced the completion of a $27.8 million Series B funding round, which will be used to further develop advanced analytics and artificial intelligence (AI) capabilities at enterprise scales.
According to the company, this ground-breaking development is driven by increased customer demand in response to the need for real-time insights gleaned from complex data and system intelligence that can process such data more efficiently and effectively than ever before.
The advanced analytics and AI capabilities are achieved through ArangoDB’s graph model which uses powerful machine learning tools to glean insights from previously hard-to-access structured and unstructured data sources. This model enables customers to build advanced analytics applications including natural language processing (NLP), sentiment analysis and sentiment scores, and advanced pattern recognition capabilities on top of their existing databases. In addition, ArangoDB customers can leverage advanced graph search algorithms for optimization tasks such as route planning or clustering items for efficient resource allocation by uncovering hidden relationships across existing datasets.
To accelerate the adoption of its advanced analytics and AI capabilities within corporate environments, ArangoDB is emphasizing growing its partner network by signing partnerships in areas such as Predictive Maintenance 4.0 (Predictive maintenance enables customers to manage assets lifecycle costs more efficiently by taking preventive measures).
Such partnerships aim to give customers access to plug n’ play AI solutions and tailored advice on how best to deploy such technologies within their organizations to get started with enterprise scale AI projects almost immediately. In addition, these partnerships expand ArangoDB’s ability to serve its customers start-up market segment going forward, driving what it views as an emerging era in how businesses use data—the democratization of enterprise AI solutions.
Benefits of Advanced Analytics and AI Capabilities
ArangoDB’s $27.8M investment will be used to accelerate the development of their next-generation Graph ML capabilities. With Graph ML, ArangoDB can provide enterprises with advanced analytics and AI capabilities at a massive scale. This technology is focused on helping businesses make better decisions and streamline workflows through deep machine learning technology and by exploring data in different ways. With this investment, ArangoDB will be able to offer an even greater suite of advanced analytics and AI capabilities for users.
Graph Machine Learning can help businesses better understand complex customer behavior and larger process optimization as it leverages graph algorithms to look beyond simple association between variables and dive into emergent user behaviors or wider trends across the entire system viewed as one big graph. Furthermore, utilizing AI for business operations can enable faster customer support responses using sentiment analysis, filter out irrelevant content using natural language processing, or increase customer engagement by predicting customer churn rates earlier.
These advanced analytics capabilities could assist organizations in drawing real insights from their data at an enterprise level while applying these nascent techniques on unstructured data graphs with ease of use due to vendor-neutral open source software like ArangoDB that takes complexity out of supporting various edge cases that customers encounter when they are dealing with complex AI models such as neural networks. In addition, by integrating advanced analytics systems into existing enterprise infrastructure, companies can improve customer experience and team collaboration resulting in increased productivity across departments.
Enterprise Scalability
ArangoDB recently announced a $27.8 million Series B investment to accelerate development of next-generation Graph ML and provide advanced analytics and AI capabilities at enterprise scale.
This investment underscores the potential of ArangoDB’s graph database technology to provide an enterprise-ready solution for data scientists, providing scalability and performance.
With this investment, ArangoDB is set to become a leader in the graph ML sector and promises to revolutionize how enterprises use data to drive insights.
Overview of Enterprise Scalability
As enterprises look to take advantage of the growing opportunities for leveraging advanced analytics and artificial intelligence (AI) capabilities to gain a competitive edge, scalability has become increasingly important. The ability to scale processing power and operation capacity cost-effectively is essential for businesses that process large datasets or apply inquiry functions on large volumes of data in near real-time. ArangoDB recently announced a $27.8 million Series B investment round led by world-leading healthcare analytics provider ST Healthcare to facilitate this.
The investment will enable ArangoDB to accelerate its development of the next generation of Graph Machine Learning (Graph ML), providing advanced analytics and AI capabilities at enterprise scale. This is critical for organizations that require the ability to switch between applications quickly, process large amounts of data efficiently across customized processes and quickly adapt their operations in response to rapidly shifting business priorities.
Graph ML provides organizations with enhanced scalability benefits, including faster learning cycles based on fully automated backpropagation, high recoverability due to distributed data management support, and optimized query performance allowing for more accurate insights in shorter processing times. As a result, Graph ML empowers companies with the highest levels of scalability without requiring costly IT investments or disruptive system changes.
Organizations can maximize returns by leveraging ArangoDB’s enterprise scalability capabilities and their existing technology investments while driving cost savings.
Benefits of Enterprise Scalability
The newly announced $27.8 million Series B investment in ArangoDB comes as the company seeks to accelerate developing its next-generation graph ML, providing enterprise-scale advanced analytics and AI capabilities. This gives organizations an incredibly powerful toolset to help them drive strategic decision making, discover insights faster, and ultimately achieve greater flexibility, scalability and cost savings.
At the core of ArangoDB is its unique combination of multi-model database capabilities that allow it to handle documents, graphs and key-value data in a single platform. This is what sets it apart from other popular solutions in the industry. In addition, by combining its graph core architecture with a globally distributed infrastructure, ArangoDB enables organizations to quickly scale up projects across multiple data centers while maintaining robust features such as detections & recommendations in production deployments.
What makes enterprise scalability so important is that it helps companies make more out of their existing resources by allowing them to do more with less effort — giving them a competitive edge above the competition that don’t have access to these kinds of databases. In addition, with ArangoDB’s high performance distributed system and optimization for dense real world workloads & scalability requirements, it can help future-proof operations using AI/ML use cases on massive data sets & queries with lightning speed – giving businesses a leg up when competing for customers or staying ahead of rapid changes in trends or regulations.
Conclusion
In conclusion, ArangoDB’s $27.8 million Series B investment provides the opportunity to accelerate the development of next-generation graph ML and advanced analytics capabilities at enterprise scale. This will allow businesses and organizations to process their data more quickly, accurately, and efficiently.
ArangoDB will become an essential tool for organizations utilizing ML and AI technology as it continues to evolve. By leveraging its powerful software platform, businesses can remain competitive in the age of big data by accessing industry-leading insights and predictive capabilities.
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