Amazon Neptune vs a Data Catalog: When to Use Each

Emily Winks

Data Governance Expert

Updated:08/24/2026

Published:08/24/2026

18 min read

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Key takeaways

Amazon Neptune computes and traverses relationships; a data catalog governs and documents data across every source.
AWS built a semantic layer on Neptune Analytics in July 2026 so an LLM isn't handed raw catalog metadata all at once.
Informatica already builds its catalog's knowledge-graph capability on Amazon Neptune, proof the two are paired, not rivals.
Neither tool alone tells an AI agent what it can retrieve once the estate spans graph-shaped and non-graph-shaped data.

Amazon Neptune vs a data catalog: when do I need each?

Amazon Neptune is AWS's fully managed graph database, built for traversing relationships like fraud rings, identity graphs, and recommendation engines at multi-hop scale. A data catalog, like AWS Glue Data Catalog or AWS DataZone, governs and discovers data across every source, not just graph-shaped ones, answering what a dataset is, who owns it, and who can access it. Most teams running a real AI-agent workload need both, at different layers, and AWS's own July 2026 reference architecture already pairs Neptune with a semantic layer built specifically for that handoff.

Here's what each one actually does:

Amazon Neptune computes and traverses relationships: fraud rings, identity graphs, recommendation engines
A data catalog governs and discovers data across every source, not just graph-shaped ones
Most AI-agent workloads need both, at different layers, not one instead of the other
Neither tool alone tells an agent what it can retrieve once the estate spans both

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Amazon Neptune is a fully managed graph database built for traversing relationships. A data catalog governs, discovers, and documents data across every source in your stack, not just the ones shaped like a graph. Most teams asking “which one do I need” are really asking two different questions at once: how are these things connected, and what is this thing and who owns it, and only one tool answers each.

This isn’t a shootout between two competing products. Neptune and a data catalog like AWS Glue Data Catalog or AWS DataZone solve different jobs, and AWS’s own engineering teams already pair them rather than choosing one. What follows is the practical decision: when Neptune earns its place, when a catalog does instead, where AWS’s own reference architecture already runs both together, and the governance gap that becomes unavoidable once an AI agent, not a person, is the one asking the question. Atlan, AWS Glue Data Catalog, and AWS DataZone each take a different approach to that gap, and none of them close it with a graph database alone.

Different jobs, not competing products: Neptune computes and traverses relationships; a catalog surfaces metadata, ownership, and access rules
AWS already pairs them: an AWS ProServe reference architecture builds a semantic layer on Neptune Analytics specifically so an LLM isn’t handed raw catalog metadata
The confusion is real: a catalog can store a graph, and Neptune can compute lineage, but neither substitutes for the other’s core job
The open question: once an AI agent needs both a graph’s relationships and a catalog’s ownership context in the same query, something has to govern that handoff

Dimension	Amazon Neptune	Data catalog (AWS Glue Data Catalog / DataZone)
What it is	Fully managed graph database (Neptune Database + Neptune Analytics)	Metadata catalog for technical and business-facing data discovery
What it does	Stores and traverses relationships: nodes, edges, multi-hop paths	Stores what data exists, its schema, its owner, and who can access it
Who owns it	Data engineering, ML engineering	Data governance, platform teams
Key strength	Multi-hop traversal at scale, GraphRAG, agent memory	Discovery, governance, lineage-as-metadata across every source
Best for	Fraud graphs, identity graphs, recommendation engines, GraphRAG	Cross-source discovery, access requests, business glossary
Questions it answers	“How is X connected to Y, three hops out?”	“What is this dataset, who owns it, can I access it?”
Cost model	Capacity units (NCU) or instance-hours, plus storage	Free tier plus pay-as-you-go (DataZone); crawler and API pricing (Glue)

Amazon Neptune vs a data catalog: what’s the difference?

Amazon Neptune and a data catalog sit on opposite sides of the same question: Neptune tells you how things relate to each other, and a catalog tells you what a thing is and who’s responsible for it. Confusing the two is a common and consequential mistake, and it most often looks like treating a catalog’s stored relationships (a table feeding a dashboard, a column referencing another column) as equivalent to a graph database’s ability to traverse and compute across millions of those relationships in milliseconds, something a catalog simply isn’t built to do at that scale.

Amazon Neptune is AWS’s fully managed graph database, actually two products under one name: Neptune Database for low-latency transactional graph storage, Neptune Analytics for large-scale graph analytics, vector search, and GraphRAG. Both speak Gremlin, openCypher, and SPARQL. A deeper walk through what each product does, and how a graph database differs from a knowledge graph built on top of one, lives on Atlan’s dedicated Neptune deep dive; this page won’t re-explain it end to end.

A data catalog, in the AWS context, means AWS Glue Data Catalog (the technical metastore Athena and Redshift Spectrum query directly) and AWS DataZone (the business-facing layer for project-based discovery and access requests) working together. Neither one traverses relationships at scale. Both exist to answer a completely different question: what data exists, what does it mean, and who’s allowed to touch it. The full breakdown of how those two AWS catalogs divide that job lives on AWS DataZone vs Glue Data Catalog: a decision framework, and how Amazon DataZone stacks up against Atlan directly covers the buy-side of that same catalog question.

The confusion persists because both tools technically “store relationships.” A catalog can record that Table A feeds Dashboard B, the kind of lineage-as-metadata most governance programs run on. Neptune can compute that Table A is three hops from three hundred other assets an incident would touch. One is metadata about a connection; the other is a queryable structure built to answer questions about the connection at scale, and a catalog cannot be scaled into doing the second job just by adding more metadata fields. The more common practitioner error runs the other way: reaching for a knowledge graph for AI agents or a dedicated graph database for a discovery problem a catalog already solves, a caution practitioners raise often enough that it’s worth taking as seriously as the reverse mistake.

When should you use Amazon Neptune?

Amazon Neptune earns its place when the actual question you’re answering is shaped like a graph: multiple hops, dense relationships, and a traversal pattern that a relational join would choke on past two or three levels deep. Practitioners on r/dataengineering put it bluntly: “if you know up front that your use case will need to be exposed as a graph and you’re only doing a few hops, you most likely can get away with using an existing DB.” According to that Reddit r/dataengineering thread (2021), the signal to reach for a dedicated graph database is depth and density of traversal, not the mere presence of relationships in your data.

Concrete trigger conditions where Neptune fits:

Multi-hop fraud and identity graphs: finding rings of related accounts, transactions, or devices three-plus hops out, in near real time, the same traversal depth that separates an active knowledge graph from a static one
Recommendation engines: traversing shared-interest or co-purchase graphs at scale
Customer 360 / profile graphs: unifying a customer’s relationships across products, accounts, and touchpoints into one traversable structure
GraphRAG and AI-agent memory: Neptune Analytics is what Amazon Bedrock Knowledge Bases’ managed GraphRAG capability runs on underneath, combining vector similarity with relationship traversal, an approach worth comparing against construction frameworks like Neo4j GraphRAG, LlamaIndex, and LangChain’s graph transformer or FalkorDB’s take on graph databases for GraphRAG
Security and IT-infrastructure graphs: modeling assets and their relationships to investigate how systems interact when something breaks, the same pattern behind how to query a context graph with an AI agent

None of that means a graph database is the default answer to “we have related data.” A 2025 r/Database thread on relational-versus-graph recommendations put the caution plainly: “there’s no requirement for a graph; a simple, cost-effective search engine would suffice” for a lot of what people describe as relationship problems. The honest read of Neptune’s fit is narrower than AWS’s own “Graphs for AI” pitch suggests. It’s the right tool once you’ve confirmed the query pattern is genuinely multi-hop, not just “our data has foreign keys,” and narrower still than the case for an active context graph that an AI agent queries directly rather than a graph a human analyst traverses by hand.

When should you use a data catalog instead?

Flip the question, and a catalog is the right tool instead. The moment the question isn’t about relationships between rows but about the data assets themselves, what exists, what it means, who’s accountable for it, and whether someone is allowed to see it, that’s a job Neptune was never built to do. It’s the job AWS Glue Data Catalog and AWS DataZone split between them: Glue as the technical metastore query engines read directly, DataZone as the business-facing project-and-domain layer for discovery and access requests.

Concrete trigger conditions where a catalog fits instead of a graph database:

Cross-source discovery: finding the right table or dataset across S3, Snowflake, Redshift, and on-prem sources, none of which need to be graph-shaped to be discoverable, the exact job a data catalog built for AI has to do across every source at once
Business glossary and ownership: attaching a shared definition, a steward, and a certification state to an asset, the kind of structured metadata an AI agent needs to act on rather than just read
Access governance: project- and domain-based approval workflows instead of manually granting IAM permissions table by table, the same zero-trust access model that shows up across most cloud governance stacks
Lineage-as-metadata: recording that a transformation ran, and what it touched, which is a metadata record, not a computed traversal
Agent-facing retrieval: an MCP-connected data catalog is increasingly how AI agents query a catalog directly rather than through a person clicking through a UI

A 2023 r/dataengineering thread on the best database for storing and querying metadata drew the line cleanly: “graph databases are a good fit if you want to answer questions about lineage,” meaning computing impact across a chain of dependencies, while a catalog is where you’d store and surface that lineage as metadata in the first place. That’s a real distinction, not a redundancy. A catalog tells you a pipeline exists and what it touched last night. A graph database, or a graph capability sitting on top of a catalog, tells you what breaks three systems downstream if you change it today. Most teams need the first far more often than the second, which is exactly why catalogs, not graph databases, are the default infrastructure and graph databases are the exception you reach for deliberately, and why the data-catalog-vs-context-layer distinction matters just as much as the catalog-vs-graph-database one once an AI agent enters the picture.

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Amazon Neptune vs a data catalog: head-to-head comparison

Feature lists make Neptune and a data catalog look more alike than they are. Both can claim to “handle relationships,” and that overlapping claim is exactly where a side-by-side needs precision instead of a marketing pitch, the same precision evaluation criteria for a context layer demands before either tool gets treated as a substitute for governed, agent-facing context.

Dimension	Amazon Neptune	Data catalog (Glue Data Catalog / DataZone)
Primary focus	Computing and traversing relationships	Discovering, governing, and documenting data assets
Data shape it needs	Graph-native: nodes, edges, properties	Any shape: tables, files, dashboards, models
Query model	Gremlin, openCypher, SPARQL, multi-hop traversal	Search, glossary lookup, lineage-as-record, access request
Governance/ownership metadata	Not native; Neptune has no built-in glossary, steward, or certification concept	Native: glossary terms, ownership, certification, access policy
AI-agent readiness (2026)	Strong for GraphRAG and agent memory via Bedrock Knowledge Bases	Improving: Glue’s business-context/semantic-search preview, still a 4-region preview as of mid-2026
Migration difficulty	High: query languages and bulk-load formats differ from other graph engines, per AWS’s own migration documentation	Moderate: catalog metadata is portable; the harder cost is re-establishing ownership and glossary context, the same cost a build-vs-buy evaluation for metadata tooling has to price in
Schema enforcement	Minimal: only uniqueness of a node or edge ID, per AWS’s documentation	Structured: schemas, classifications, and business terms are first-class
Time to value	Fast for a defined traversal use case; slower once governance context needs to be layered on top	Immediate for technical metadata (Glue); scales with domain and project setup (DataZone)
Failure mode	Reaching for a graph database when the actual query is one or two hops, and a relational store would have done	“Too technical for end users,” the exact gap practitioners describe on Reddit about Glue Data Catalog alone

Consider a team building a fraud-detection graph on Neptune Database because their investigators need three-hop traversal across accounts and devices in near real time. That team still needs to know which of the underlying tables feeding that graph are certified, who owns them, and whether a given analyst is cleared to see the raw transaction data before it ever reaches Neptune. Neptune answers the traversal question. It has no opinion on the governance question, and it was never supposed to, which is precisely the gap a full context layer total-cost-of-ownership build-vs-buy-vs-bundle analysis has to account for once both tools are already in production.

Do Amazon Neptune and a data catalog work together?

Amazon Neptune and a data catalog aren’t just compatible, AWS’s own engineering teams already build them to work together, and the clearest evidence is a reference architecture AWS published itself rather than a vendor claim about either product.

In a July 2026 AWS Database Blog post, a team of five AWS engineers, led by Rahul Shaurya, Principal Data Architect at AWS, describes building a semantic ontology on Neptune Analytics to solve a problem neither tool solves alone: an LLM reasoning over a full catalog’s metadata degrades in accuracy as its context window fills with irrelevant tables and columns. Their fix uses Neptune’s graph structure to model the catalog’s assets and relationships, the same pattern behind most enterprise efforts to build a knowledge graph for AI agents, so an agent gets targeted retrieval instead of the entire catalog dumped into its context window.

AWS didn’t choose Neptune over a catalog, or a catalog over Neptune. It built a semantic layer on Neptune specifically to make catalog metadata usable by an agent, an ontology-first approach rather than a raw property-graph dump. The same logic runs the other direction: Informatica built its Cloud Data Governance and Catalog’s knowledge-graph capability directly on top of Amazon Neptune, using it as the traversal engine underneath a catalog product, not as a replacement for one.

Invest in Neptune first when the immediate blocker is a traversal query a relational store can’t scale to. Invest in the catalog first when nobody can find, trust, or get approved access to data that already exists. Build both, deliberately connected, when an AI agent is the eventual consumer, since it needs the graph’s relationships and the catalog’s ownership rules in the same reasoning pass, and stitching that connection by hand is exactly what a DIY context layer build looks like before a team decides whether to build it or buy one.

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The governance gap neither tool closes alone

Neither Amazon Neptune nor a data catalog, on its own, tells an AI agent what it’s allowed to retrieve once the data estate includes both graph-shaped relationships and everything else. That gap doesn’t show up when a human analyst is the one asking the question, because a person can hold “I have access to this” and “this is how it connects to that” in their head at once. An agent can’t, unless something hands it both, which is exactly why AI agent governance has become its own discipline rather than an extension of existing data governance.

This is the same boundary the AWS DataZone vs Glue Data Catalog comparison runs into from the catalog side: AWS’s own June 2026 Glue Data Catalog preview and the AWS Context knowledge-graph service it announced the same month, still described as “coming soon” as of mid-2026, both stay inside AWS’s Lake Formation and IAM boundary. Neptune’s side of the same problem is narrower but just as real: a graph that computes relationships perfectly still has no concept of who owns an asset, whether it’s certified, or whether the requesting agent is cleared to read it, the same distinction that separates a data catalog from an LLM’s usable knowledge base and the reason a semantic layer alone doesn’t replace a data catalog any more than a graph database does.

Atlan doesn’t compete with Neptune or replace either AWS catalog. It reads schema, lineage, and ownership context from wherever it already lives, including a Neptune-backed knowledge graph, AWS Glue Data Catalog, DataZone, Snowflake, Databricks, and on-prem sources, and gives an AI agent one governed layer to ask what a piece of data means and whether it can use it, regardless of which underlying store answers how that data connects to everything else. That’s why teams evaluating the broader set of agent context layer tools tend to land on the same enterprise context layer question, worth weighing once a team has already decided which of Neptune, Glue Data Catalog, or DataZone it needs for the job in front of it.

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Why the AWS boundary isn’t the real Neptune-vs-catalog question

The real decision was never Neptune or a data catalog. Confirm which job you actually have, a traversal problem or a discovery-and-governance problem, and the trigger conditions above answer it directly; most teams running a real AI-agent workload eventually need both, not instead of each other but at different layers. AWS’s own semantic-ontology-on-Neptune reference architecture proves that pairing works technically, and the same logic extends to a multi-cloud context layer once the graph, the catalog, and Snowflake or Databricks all need to answer to the same agent. What it doesn’t solve is whether all of that hands an AI agent one consistent, governed answer instead of three disconnected ones, which is usually the point of implementing an enterprise context layer for AI once the Neptune-or-catalog decision is already made.

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FAQs about Amazon Neptune vs a data catalog

1. When should I use Amazon Neptune instead of a data catalog?

Use Amazon Neptune when your actual query is graph-shaped: multi-hop traversal across dense relationships, like fraud rings, identity graphs, or recommendation engines, where a relational join would choke past two or three levels deep. Use a data catalog when the question is about the data assets themselves, what exists, who owns it, and whether someone can access it.

2. Can a data catalog replace a graph database like Amazon Neptune?

No. A catalog can record that two assets are related, but it isn’t built to compute or traverse relationships across millions of connections in milliseconds. If your workload genuinely needs multi-hop traversal at scale, a catalog alone will not perform that job, no matter how well it’s configured.

3. Does AWS Glue Data Catalog use Amazon Neptune under the hood?

Not by default, but the pattern exists in the market: Informatica’s Cloud Data Governance and Catalog product builds its knowledge-graph capability directly on top of Amazon Neptune, using it as the traversal engine underneath a catalog’s discovery layer, a real example of the two working together rather than competing.

4. What’s the difference between Neptune Database and Neptune Analytics?

Neptune Database is the original transactional graph store, built for high-throughput, low-latency workloads like fraud detection and Customer 360. Neptune Analytics is the newer in-memory engine with native vector search, purpose-built for graph analytics at scale and GraphRAG, and it’s what Amazon Bedrock Knowledge Bases’ managed GraphRAG capability runs on.

5. Can Amazon Neptune power GraphRAG for AI agents?

Yes. Amazon Bedrock Knowledge Bases offers a managed GraphRAG capability that runs on Neptune Analytics, combining vector similarity search with relationship traversal so an agent’s retrieval reflects both semantic closeness and how entities actually connect.

6. Do I need both Amazon Neptune and AWS DataZone or Glue Data Catalog?

Most teams running a real AI-agent workload eventually need both, at different layers. Stand up Neptune when a traversal problem is the immediate blocker, stand up the catalog when discovery and governance are, and treat connecting the two as a deliberate step rather than an afterthought once an agent needs both a graph’s relationships and a catalog’s ownership context in the same query.

Sources

Amazon Neptune, AWS Product Page. https://aws.amazon.com/neptune/
Amazon Neptune Review: A Scalable Graph Database for OLTP, InfoWorld (2019). https://www.infoworld.com/article/2260682/amazon-neptune-review-a-scalable-graph-database-for-oltp.html
Amazon Neptune Migration Compatibility, AWS Documentation. https://docs.aws.amazon.com/neptune/latest/userguide/migration-compatibility.md
Amazon DataZone Pricing, AWS. https://aws.amazon.com/datazone/pricing/
AWS Glue Data Catalog and Crawler Overview, AWS Documentation. https://docs.aws.amazon.com/glue/latest/dg/catalog-and-crawler.html
Rahul Shaurya, Edvin Hallvaxhiu, Shukhrat Khodjaev, Przemysław Paprocki, and Sindi Cali (2026). Build a Semantic Ontology to Power AI Assistants on AWS, Part 1, AWS Database Blog. https://aws.amazon.com/blogs/database/build-a-semantic-ontology-to-power-ai-assistants-on-aws-part-1/
How Informatica Cloud Data Governance and Catalog Uses Amazon Neptune for Knowledge Graphs, AWS Database Blog (2022). https://aws.amazon.com/blogs/database/how-informatica-cloud-data-governance-and-catalog-uses-amazon-neptune-for-knowledge-graphs/
Which User Facing Data Catalog Do You Use?, r/aws community, Reddit. https://www.reddit.com/r/aws/comments/1fee95r/which_user_facing_data_catalog_do_you_use/
Channy Yun (2023). Amazon DataZone Now Generally Available, AWS News Blog. https://aws.amazon.com/blogs/aws/amazon-datazone-now-generally-available-collaborate-on-data-projects-across-organizational-boundaries
Graph Databases for Data Engineering, r/dataengineering community, Reddit (2021). https://www.reddit.com/r/dataengineering/comments/o4yodf/graph_databases_for_data_engineering/
What Database System Is Best for Storing and Querying Metadata?, r/dataengineering community, Reddit (2023). https://www.reddit.com/r/dataengineering/comments/17rg1t7/what_database_system_is_best_for_storing_and/
Relational vs Graph Database Recommendation, r/Database community, Reddit (2025). https://www.reddit.com/r/Database/comments/1icel0k/relational_vs_graph_database_recommendation/

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