Neo4j Unveils Cloud Database Upgrade For 100x Faster Analytics and Decision-Making

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Published: October 25, 2023 at 9:00 am Updated: October 25, 2023 at 6:27 am

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In Brief

Neo4j introduced new capabilities that enable concurrent threads across multiple CPU cores for running analytical graph queries.

Graph database and analytics company Neo4j today announced substantial updates for its platform, empowering cloud and self-managed customers to accelerate analytical queries by up to 100 times. The new update aims to facilitate concurrent transactional and analytical processing within a unified database, and automate real-time tracking of data changes for streamlined critical decision-making.

The integration of operational and analytical workloads within a single database is Neo4j’s latest stride, enriched by the introduction of parallel runtime and change data capture (CDC). These innovations equip customers with real-time insights, economical data management, and streamlined architecture, heralding a paradigm shift in the realms of speed, performance, and agility.

The platform has introduced new capabilities that enable concurrent threads across multiple CPU cores for running analytical graph queries. The company said that it employs a technique known as morsel-based parallelism — that optimizes scalability, resource utilization and multitasking.

“Morsel-based parallelism is a parallel computing approach used to divide a computational task into smaller, more fine-grained units of work, referred to as “morsels” or “chunks.” Each morsel is a small and self-contained unit of work that can be processed independently and in parallel by multiple processors or threads,” Sudhir Hasbe, Chief Product Officer at Neo4j told Metaverse Post. “This approach is particularly useful in graph queries which need to access the whole graph and are not anchored in a specific entity in the graph.”

Neo4j’s Native Change Data Capture (CDC) aims to automate real-time tracking and notification of data changes within the database. The company said that CDC integrates with Neo4j Connector for Kafka and Confluent, facilitating the streaming of changes to other data platforms and applications.

“CDC capability lets users get real-time change events from its graph database. Users can get incremental changes or complete updates to a specific node or relationship. The downstream systems can then integrate and more easily consume these events as needed,” Neo4j’s Hasbe told Metaverse Post. “This will enable enterprises to integrate Neo4j seamlessly with all other enterprise applications and systems.”

Enhanced Graph Database Capabilities

The company said that platform’s new embedding models can predict and identify missing relationships while inferring new connections within an organization’s knowledge graph, enhancing semantic understanding. Moreover, complex workflows can be streamlined through pathfinding algorithms, identifying optimal sequences and critical paths between nodes on a graph.

With the release, the company announced the addition of two new algorithms for pathfinding: topological sort and longest path.

“Topological sort is used to sort nodes in the graph following the direction/flow of relationships. This is useful for helping organizations handle dependencies in complex systems, such as supply chains, inventory management, and software projects,” explained Hasbe. “Likewise, Longest path is used to find the most costly paths in the graph or the “critical” paths. This can be used for a variety of use cases involving complex systems, including estimating completion times in complex projects with multiple interdependent tasks and supply chain resource allocation.”

Knowledge Graph Embedding (KGE) models are machine learning techniques that aim to discover missing links/connections in a knowledge graph. KGE models accomplish this by taking a graph as input, transforming it into embedding (numeric vector) representations, and learning where specific relationships form based on the rest of the graph structure.

“With our addition for KGE support, Neo4j aims to empower organizations to operationalize their trained KGE models at scale in a graph database, thereby enabling them to bridge knowledge gaps and unlock additional insights from their data,” added Hasbe. “This can enhance semantic understanding for search and Generative AI applications that rely on enterprise-specific data. Using KGE to discover new links can help improve the relevancy and insights obtained from querying the knowledge graph, going beyond simple fact-based queries by surfacing further inferences and context-rich information through uncovered relationships.”

“Neo4j’s new capabilities enable modern law enforcement agencies to react with greater agility to mission-critical events, empowering them to fight more crimes and solve them faster,” said Christoph Willemson, CTO, GraphAware. “For example, we can trigger alerts and send them to front-line officers when the phone number of a Person of Interest pings from a cellular tower near a high-risk event where a VIP is present, bodycam footage shows the image of a child at risk, and other events.”

The company recently integrated native vector search into its core database capabilities to provide accuracy, explainability, and transparency for Language Model Models (LLMs) and other generative AI applications. The new features are readily available on Neo4j Graph Database and Neo4j AuraDB at no cost, with CDC initially accessible as an Early Access Program (EAP) public beta.

“We believe our new capabilities, especially parallel runtime and CDC, will enable enterprises to unlock more value from their Neo4j investments. They can now use Neo4j for many more analytical use cases, which were slow to use at scale in the past,” Hasbe told Metaverse Post. “CDC unlocks the value of data in the graph database, especially when used as a system of record, to make real-time decisions in downstream applications or systems. Together, they strengthen our market position as an operational database system of record and a database for analytical applications.”

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Victor is a Managing Tech Editor/Writer at Metaverse Post and covers artificial intelligence, crypto, data science, metaverse and cybersecurity within the enterprise realm. He boasts half a decade of media and AI experience working at well-known media outlets such as VentureBeat, DatatechVibe and Analytics India Magazine. Being a Media Mentor at prestigious universities including the Oxford and USC and with a Master’s degree in data science and analytics, Victor is deeply committed to staying abreast of emerging trends.
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