Graph Database Use Cases

Why These Use Cases Belong in One Database

Every use case on this page has the same underlying shape: the value is in the relationships, but the relationships alone are not enough. Fraud analysts need the transaction graph and the time series of account behavior. Recommendation engines need the interaction graph and the vector embeddings that place items in semantic space. Knowledge graphs need entity relationships and the full-text search that gets a user to the right entity in the first place.

The conventional answer is polyglot persistence: a graph database for the edges, a vector store for the embeddings, a time-series database for the metrics, a search index on top, and an application layer stitching them together. That architecture works, but the cost is paid continuously. Every query that spans two stores becomes an application-level join. Every write becomes a distributed write with no shared transaction. Consistency between the graph and the index is now your problem, and the latency budget is spent on network hops rather than on the query itself.

ArcadeDB stores all six models in a single engine with a single ACID transaction boundary. A fraud query can traverse a graph, filter on a time-series window, and rank by vector similarity in one statement, against one copy of the data, in one transaction. That is the property that connects every use case below: they are not nine separate products, they are nine shapes of the same engine.

Graph RAG for LLMs

First-generation RAG chunks documents, embeds them, and retrieves by cosine similarity. That works until the answer depends on structure rather than similarity: when documents are treated as isolated vectors, the relationships between them are lost. A question like "which researchers at Stanford have published papers on the technique used by the company that acquired our competitor" requires traversing four or more hops, and no single chunk contains that chain. GraphRAG retrieves chunks together with their connected entities, relationships, and community structure, giving the model context that reflects how the facts actually connect. The differentiating capability is that the whole pipeline runs in one engine: vector search finds the semantic entry point, graph traversal expands it, full-text indexing pins exact references like "section 4.2.1", and temporal context separates current facts from superseded ones. Neo4j, Memgraph, and ArangoDB each require a separate system for the vector half.

For the retrieval patterns and query examples, read how ArcadeDB runs the entire GraphRAG pipeline in a single database.

Knowledge Graphs and Semantic Search

The average enterprise manages over 400 distinct data sources, roughly 80% of that data is unstructured, and knowledge workers spend about 30% of the day searching for information while still failing to find it half the time. A knowledge graph models the world as entities (concepts, documents, people, organizations) connected by typed relationships (mentions, relates-to, authored-by, cites), which is what makes multi-hop reasoning possible: discovering indirect connections that keyword search will never return. Keyword search breaks down when the user does not know the terminology, and a developer searching for "how to handle errors in the payment service" will not find a document titled "Exception Management in Billing Module". Semantic search over JVector embeddings closes that gap, while built-in full-text indexing with boolean operators, fuzzy matching, and phrase search handles exact error codes and product names, integrated directly into graph queries rather than bolted alongside them.

For automated graph construction and temporal knowledge, see how ArcadeDB builds and queries enterprise knowledge graphs.

Fraud Detection and Prevention

The FTC recorded $12.5 billion in consumer fraud losses in 2024, a 25% year-over-year increase, and businesses lose an average of 7.7% of annual revenue to fraud. Every investigation begins with the same question: who is connected to whom, and how? Fraudsters share devices, phone numbers, IP addresses, physical addresses, and beneficiary accounts, and each shared identifier is a relationship. That is why fraud is a graph problem: an individual account in a fraud ring looks legitimate in isolation, and the pattern only emerges when you trace the connections. What a graph alone cannot tell you is that both accounts were created 30 seconds apart at 3am and were followed by 47 micro-transactions in five minutes. ArcadeDB combines graph traversal for ring detection, time series for those temporal bursts, vector embeddings for novel patterns that rules miss, and full-text search for synthetic identity matching, in one investigation query.

For ring detection, AML layering, and synthetic identity patterns, read how ArcadeDB handles fraud ring detection in real time.

Recommendation Engines

Every recommendation is a relationship problem. "Users who bought X also bought Y" is a two-hop traversal; "friends of friends who liked this movie" is three hops; "products similar to what you viewed last week, filtered by your social circle's preferences" crosses several dimensions at once. Collaborative filtering is the backbone, resting on the insight that when two users have overlapping histories, the items one has discovered and the other has not are strong candidates. It fails on cold start, when a user or an item is new, and that is where vector embeddings take over with content-based similarity. Time is the dimension most systems bolt on as a decay factor at the end, even though temporal patterns are among the strongest signals available. The advantage of a multi-model engine is not supporting graph, vectors, and time series separately but combining all three in one query, with no data movement or synchronization between stores.

For collaborative filtering and cold-start patterns, see how ArcadeDB blends graph, vector, and time-series signals in a single recommendation query.

Real-Time Analytics and IoT

70% of data leaders say their stack is too complex, 85% cite tool integration as their top challenge, and over 63% spend more than a full day each week on maintenance instead of delivering insights. ArcadeDB's time-series engine is a dedicated storage layer built for high-throughput ingestion and analytical queries rather than an afterthought attached to a graph database, with specialized SQL functions for temporal analysis, continuous aggregates, and automatic downsampling so that years of history do not have to be stored at raw resolution. It integrates natively with Grafana through an HTTP API returning Grafana's DataFrame format, and speaks PromQL natively so existing Prometheus queries work without running a separate system. The reason to keep this alongside a graph is simple: a time-series database can tell you that CPU spiked at 14:32, but not whether the cause was a connection pool, a misrouted deployment, or a cascading failure upstream.

For ingestion protocols, PromQL support, and root-cause traversal, see how ArcadeDB pairs time series with graph context.

Customer 360 and Master Data Management

Only 14% of organizations have achieved a genuine 360-degree customer view; the rest operate with data fragmented across CRMs, support desks, e-commerce platforms, marketing tools, mobile apps, point-of-sale terminals, and call centers. Every customer sits inside a web of relationships: products purchased, devices used, addresses lived at, people referred, tickets opened, campaigns clicked. That is not tabular data, it is a graph. The foundation is identity resolution, because a single customer can appear as dozens of records with different work and personal emails, several devices, anonymous web sessions, a loyalty card, and a support phone number. Full-text search finds the candidate records, vectors score the fuzzy matches, and the graph holds the resolution decisions so downstream relationships stay pointed at the merged entity. Churn models built on individual attributes also miss social influence, which is a relationship signal that only a graph exposes.

For identity resolution, journey analysis, and where this goes beyond a CDP, read how ArcadeDB assembles a true 360-degree customer view.

AI/ML Feature Store

Data scientists spend between 50% and 80% of their time on collection, cleaning, and feature engineering rather than modelling, and teams spend over 40% of engineering effort maintaining pipelines instead of creating features. A feature store on ArcadeDB serves three feature families from one engine. Vector embeddings, powered by JVector, encode text, images, user behavior, and product attributes as high-dimensional vectors, typically 384 to 1536 dimensions. Graph features capture the signal tabular models miss: who is connected to whom, how tightly clustered a neighborhood is, how central a node is in a network. Time-series features capture temporal shape, where a transaction amount compared against a customer's 30-day rolling average is far more predictive than the amount alone. Keeping all three in one engine is also what removes training-serving skew, the failure mode where features are computed by different code paths at training time and at inference time.

For point-in-time correctness and serving patterns, see how ArcadeDB serves graph, vector, and temporal features from one store.

Supply Chain Management

In 2024, 80% of organizations experienced supply chain disruption, at an average cost of 8% of annual revenue. A supply chain is already a network of suppliers, components, products, warehouses, routes, and customers joined by typed relationships, so modelling it as a graph removes a translation step rather than adding one. The question that matters when a supplier fails is "what is the impact?", and in a relational system that means joining supplier to component to product to inventory to customer orders, with each join adding latency. As a traversal it is one query, which is what makes blast-radius analysis interactive instead of overnight. Regulations such as the EU Digital Product Passport and conflict minerals reporting require tracing every component back to its raw material origin, which is the same traversal run in the opposite direction. Vector similarity then handles alternative supplier discovery by capability, certification, and lead time rather than by directory lookup.

For blast-radius queries, batch traceability, and inventory intelligence, read how ArcadeDB maps multi-tier supplier networks.

Identity and Access Management

In 2024, 80% of breaches involved stolen or misused credentials, and the average breach cost reached $4.88 million, yet most organizations still hold permissions in spreadsheets, LDAP trees, or relational tables that cannot answer "what can this user actually access?". An IAM system is a network of relationships: users belong to groups, groups inherit roles, roles grant permissions, permissions apply to resources. Answering the effective-access question relationally means recursive self-joins across four or five tables. The dangerous permissions are rarely the assigned ones; they emerge from nested group membership, role inheritance, and transitive delegation, so a contractor added to a DevOps group can inherit production admin through a chain nobody designed. As a graph, that chain is a reachability query you can run continuously, and the same structure supports simulating a change before applying it and proving effective access during a SOX, GDPR, HIPAA, or PCI-DSS audit.

For escalation-path detection, access certification, and least-privilege analytics, see how ArcadeDB models permission hierarchies as a graph.

Which Data Models Each Use Case Uses

A practical way to scope a project is to work out which models it touches. If a use case needs more than one, it is a candidate for consolidation onto a single engine.