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1. Shard Calculation

Query:

Response:

Calculated Shard: 37 (from laksa123)

2. Crawled Status Check

Query:

curl -X POST \
  'http://laksa037.int.ahrefs:8124/' \
  -H 'Content-Type: text/plain' \
  -H 'X-ClickHouse-Database: crawler3' \
  -H 'Authorization: Basic YXBpOg==' \
  -d 'SELECT getAhrefsURLFromUnparsed(src_unparsed) AS found_url, ifNull(toUnixTimestamp(download_stamp), 0) AS crawl_time, ifNull(toUnixTimestamp(props_url_first_seen), 0) AS first_indexed_time, download_http_code AS http_code, src_unparsed AS src_unparsed, src_root_hash AS src_root_hash, history_drop_reason AS history_drop_reason, meta_title AS meta_title, meta_descriptions AS meta_descriptions, attrs_boilerpipe_text AS attrs_boilerpipe_text, attrs_markdown AS attrs_markdown, attrs_readable_markdown AS attrs_readable_markdown, meta_canonical AS meta_canonical FROM crawler3.page_info_local FINAL PREWHERE (src_root_hash, src_unparsed) IN ((getAhrefsRootHashFromUnparsed(getAhrefsUnparsedNoserviceFromURL(\'https://neo4j.com/blog/machine-learning/graph-data-platforms-collections-vs-connections/\')), getAhrefsUnparsedNoserviceFromURL(\'https://neo4j.com/blog/machine-learning/graph-data-platforms-collections-vs-connections/\'))) FORMAT JSONEachRow'

Response:

{"found_url":"https:\/\/neo4j.com\/blog\/machine-learning\/graph-data-platforms-collections-vs-connections\/","crawl_time":1775013759,"first_indexed_time":1740366276,"http_code":200,"src_unparsed":"com,neo4j!\/blog\/machine-learning\/graph-data-platforms-collections-vs-connections\/ s443","src_root_hash":"12828843287614304637","history_drop_reason":null,"meta_title":"Graph Data Platforms: Collections vs. Connections","meta_descriptions":["Read this blog to find out how graph databases differ from SQL and NoSQL databases, empowering you to reap the benefits of connected data."],"attrs_boilerpipe_text":"You may have heard that graph database technology underpins the latest in machine learning and AI.\nBut what are\nopens in new tab\ngraph data platforms\nand why are they rapidly becoming the first choice for application development? Can a shift in database technology really make a big difference?\nIn this second blog of our two-part series, we look at how SQL databases and NoSQL databases handle connected data and explain how graph data platforms, focused on data relationships, drive numerous benefits.\nSQL & NoSQL Systems Focus on Data Aggregation & Collection\nCollection-centric storage designs as implemented by SQL and Not only SQL (NoSQL) databases are designed to efficiently divide and store data.\nIn SQL’s case, the normalization of data into a tabular schema aims to minimize storage of duplicate data objects, types and values. These systems were born during the era of scarce physical memory and expensive disk-based storage, designed to avoid managing often-redundant data objects such as physical location addresses for shipping, billing, homes, offices, destinations, businesses, etc. For example, all of these data objects included common, redundant data such as a country and its provinces or states, or telephone area codes and postal codes.\nThe original relational databases were designed to minimize storage of duplicative data values because disk space was costly. (Ironically, each normalization incurs a cost in relationship storage in the form of JOIN tables, which\nopens in new tab\nnative graph databases\nhave managed to eliminate through the use of pointers.) The RDBMS design achieved this consolidated, normalized goal by linking tables of data via foreign keys to their associated records from other tables. This is why a relational dataset modeled into a graph often shrinks by an order of magnitude or more, maintaining the full richness of the data without redundant data storage.\nNoSQL systems like document, wide column and key-value data stores carry those concepts forward (and backward) by simplifying their models in exchange for higher levels of scale and simplicity. By eschewing data relationships and providing straightforward programmatic APIs, NoSQL systems make it easy for developers and administrators to work with simple data in a way that can easily scale.\nA lack of concern about relationships leads to looser data guarantees, plain APIs and straightforward scaling schemes. Data is easily spread out and just as easily retrieved, without the need to maintain the integrity of related data that’s written across a distributed storage or a cluster of machines and without needing to concern itself with the performance of distributed JOINs across those machines.\nThese systems take on the “store and retrieve” problem at scale for simple data, and their architectures reflect this, as does the set of problems they are equipped to address. However, none of these systems focus on interrelated, contextualized data or how that data might be traversed to reveal unobvious relationships, as explained below.\nGraph Systems Focus on Data Connections\nBy contrast, graph database technologies focus on how data elements are interrelated and contextualized as connected data.\nConnected data is the materialization and harnessing of relationships between data elements, which is modeled as a property graph.\nA property graph is a data model designed to express data connectedness as nodes connected via relationships to other nodes, where both nodes and relationships can have properties attached to them (which in turn can be indexed). For example, devices on an enterprise network might be modeled as nodes, with properties for their attributes (such as throughput) and relationships pointing to other adjacent devices.\nIn the graph model, data relationships are persisted so they can be navigated or traversed along connected paths to gain context. Relationships are both typed and directional.\nThe context provided by these data connections is essential to identifying friendships, making relevant real-time recommendations, attaching adjacent ideas and detecting fraud by following money trails. Without relationships as first-class data entities, all of these use cases\nbecome extremely difficult to execute.\nProperty Graphs Are Intentionally Simple\nYou can draw property graphs on whiteboards and map that design directly into a\ngraph database.\nYou can change or update a property graph easily, because its agile design eliminates\nmost of the structural overhead of traditional database schemas.\nYou can quickly program property graphs because their query language expresses and\nfollows relationships.\nYou can visualize and navigate property graphs efficiently by following the relationships\non their paths to context.\nYou can rapidly determine data context when property graph queries are executed in\nhyper-fast native graph platforms built on reliable, scalable database architectures.\nBenefits of Graph Databases\nSimple and natural data modeling\n: Graph databases provide flexibility for data\nmodeling, depending on relationship types. Since the graph model comes with no\ninherent rules, graph data stores add as much or as little semantic meaning as the\ndomain requires. This occurs without any constraints like normalization or restructuring\nof the data using denormalization.\nFlexibility for evolving data structures\n: Graph technology provides flexible schema\nevolution. In a constantly changing data environment, you need the option to add or\ndrop data entities or relationships, as well as extend or modify your data model. Graph\ndatabases allow for evolving data structures that match today’s agile development\nenvironments.\nSimultaneous support for real-time updates and queries\n: A graph data store and\nits model allow real-time updates on graph data while supporting queries concurrently.\nBetter, faster and more powerful querying and analytics\n: Graph data stores provide superior query performance with connected data using native storage and\nnative indexed data structure.\nConnected data in property graphs enables you to illustrate and traverse many relationships and find context for your next breakthrough application or analysis.\nConclusion\nToday, businesses need to bring together data from many different systems, which is driving a huge shift from on-premises to cloud as well as investments in new data integration models. Traditional rigid systems aren’t nimble enough to keep up with the pace of change. Supporting complex data environments often requires real-time data analysis to make split-second decisions to optimize the business.\nMaking the most of your data requires the ability to make connections across all of it. As the fastest growing database technology in the past decade, graph databases provide a much more scalable, secure and flexible platform than traditional databases for enterprises to deploy their use cases – from real-time recommendation engines to fraud detection to knowledge graphs to AI and machine learning.\nWhat will your first use case be? Find out more about Neo4j. Sign up for a\nopens in new tab\n30-minute demo\nwith a live Q&A.","attrs_markdown":"[Skip to content](https:\/\/neo4j.com\/blog\/machine-learning\/graph-data-platforms-collections-vs-connections\/#skip-to-content \"Skip to content\")\n\nNODES AI: Online Conference for Graph + AI - April 15, 2026 \\| [Register Today](https:\/\/neo4j.com\/nodes-ai)\n\n[![The Neo4j Graph Platform – The \\#1 Platform for Connected Data](https:\/\/dist.neo4j.com\/wp-content\/uploads\/20230926084108\/Logo_FullColor_RGB_TransBG.svg)](https:\/\/neo4j.com\/)\n\nMenu\n\n![Neo4j logo](https:\/\/dist.neo4j.com\/wp-content\/uploads\/20230926084108\/Logo_FullColor_RGB_TransBG.svg)\n\nSearch\n\nClose Menu\n\n- [Products]()\n  - [Back]()\n  - GRAPH DATABASE\n  - [Neo4j AuraDB Fully managed graph database as a service](https:\/\/neo4j.com\/product\/auradb\/)\n  - [Neo4j Graph Database Self managed, deploy anywhere graph database](https:\/\/neo4j.com\/product\/neo4j-graph-database\/)\n  - GRAPH ANALYTICS\n  - [Neo4j Aura Graph Analytics Fully managed graph analytics as a service](https:\/\/neo4j.com\/product\/aura-graph-analytics\/)\n  - [Neo4j Graph Data Science Self managed graph algorithms and ML modeling](https:\/\/neo4j.com\/product\/graph-data-science\/)\n  - GRAPH AI\n  - [Neo4j Aura Agent A single console to manage all your DB instances](https:\/\/neo4j.com\/product\/aura-agent\/)\n  - GRAPH TOOLS\n  - [Neo4j Fleet Manager A single control plane to manage all your DB instances](https:\/\/neo4j.com\/product\/fleet-manager\/)\n  - [Neo4j Bloom Easy graph visualization and exploration](https:\/\/neo4j.com\/product\/bloom\/)\n  - PARTNER SOLUTIONS\n  - [Neo4j Graph Analytics for Snowflake Fully managed graph analytics within Snowflake AI Data Cloud](https:\/\/neo4j.com\/neo4j-graph-analytics-snowflake\/)\n  - [Neo4j Graph Intelligence for Microsoft Fabric Fully managed graph database and analytics integrated in Fabric](https:\/\/neo4j.com\/neo4j-graph-intelligence-microsoft-fabric\/)\n- [Use Cases]()\n  - [Back]()\n  - [AI Systems Back your LLMs with a knowledge graph for better business AI](https:\/\/neo4j.com\/use-cases\/ai-systems\/)\n  - [Industries and Use Cases Fraud detection, knowledge graphs, financial services, and more](https:\/\/neo4j.com\/use-cases\/)\n  - [Customer Success Stories Case studies, customer videos, proof points, and more](https:\/\/neo4j.com\/customer-stories\/)\n- [Developers]()\n  - [Back]()\n  - [Developer Center Best practices, guides, tutorials, and downloads](https:\/\/neo4j.com\/developer\/)\n  - [opens in new tab GraphAcademy![](https:\/\/dist.neo4j.com\/wp-content\/uploads\/20240402072516\/Icon-GraphAcademy.svg) Free online courses and certifications. 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[Developer Center Best practices, guides, tutorials, and downloads Learn More](https:\/\/neo4j.com\/developer\/)\n    [opens in new tab GraphAcademy Free online courses and certifications. Join the 100K+ Neo4j experts. Learn More](https:\/\/graphacademy.neo4j.com\/)\n  - Developers\n    - [Deployment Center Deploy Neo4j on any cloud or architecture](https:\/\/neo4j.com\/deployment-center\/)\n    - [Documentation![](https:\/\/dist.neo4j.com\/wp-content\/uploads\/20240401103728\/Icon-Documentation.svg) Manuals for Neo4j products, Cypher, and drivers](https:\/\/neo4j.com\/docs\/)\n    - [Developer Blog Deep dives into more technical Neo4j topics](https:\/\/neo4j.com\/blog\/developer\/)\n    - [opens in new tab Community A global forum for online discussion](https:\/\/community.neo4j.com\/)\n  \n  DATA SCIENTISTS\n  \n  - [Data Science Documentation![](https:\/\/dist.neo4j.com\/wp-content\/uploads\/20240401103728\/Icon-Documentation.svg) Manuals for the Graph Data Science library](https:\/\/neo4j.com\/docs\/graph-data-science\/current\/)\n  - [Graph Data Science Home Learn what Neo4j offers for data science](https:\/\/neo4j.com\/product\/graph-data-science\/)\n  - [Get Started With Graph Data Science Download or get started in Sandbox today](https:\/\/neo4j.com\/graph-data-science-software\/)\n  - [opens in new tab Data Science Community A global forum for data-driven professionals](https:\/\/community.neo4j.com\/c\/neo4j-graph-platform\/graph-algorithms\/)\n- [AI Systems](https:\/\/neo4j.com\/use-cases\/ai-systems\/)\n- Learn\n  Learn: submenu\n  LEARN\n  \n  - [Documentation![](https:\/\/dist.neo4j.com\/wp-content\/uploads\/20240401103728\/Icon-Documentation.svg) Manuals for Neo4j products, Cypher, and drivers](https:\/\/neo4j.com\/docs\/)\n  - [GraphAcademy![](https:\/\/dist.neo4j.com\/wp-content\/uploads\/20240402072516\/Icon-GraphAcademy.svg) Free online courses and certifications](https:\/\/graphacademy.neo4j.com\/)\n  - [Resource Library White papers, datasheets, and more](https:\/\/neo4j.com\/resources\/)\n  - [Customer Success Stories Case studies, customer videos, proof points, and more](https:\/\/neo4j.com\/customer-stories\/)\n  \n  CONNECT\n  \n  - [Neo4j Events Hub![](https:\/\/dist.neo4j.com\/wp-content\/uploads\/20240402072514\/Icon-Events.svg) Live and on-demand events, training, webinars, and demos](https:\/\/neo4j.com\/events\/)\n  - [Neo4j Blog Announcements, guides, and best practices](https:\/\/neo4j.com\/blog\/)\n  - [Neo4j Video Hub Covering graph databases, data science, analytics & AI](https:\/\/neo4j.com\/videos\/)\n  \n  FEATURED EVENTS\n  \n  - [![GraphSummit Logo](https:\/\/dist.neo4j.com\/wp-content\/uploads\/20250306090638\/graphsummit-logo.svg) Graphs + AI: Transform Your Data Into Knowledge Learn more](https:\/\/neo4j.com\/graphsummit\/)\n  - [![Neo4j Nodes AI 2026 logo](https:\/\/dist.neo4j.com\/wp-content\/uploads\/20251118144030\/Nodes-White-Subhead-Date.svg) Virtual Conference Dedicated to Graph + AI Register Today](https:\/\/neo4j.com\/nodes-ai\/)\n- [Pricing](https:\/\/neo4j.com\/pricing\/)\n- [Contact Us](https:\/\/neo4j.com\/contact-us\/)\n- [Get Started Free](https:\/\/console.neo4j.io\/?mpp=4bfb2414ab973c741b6f067bf06d5575&mpid=%24device%3A0fd40cf9-b374-4184-ab5b-4861e15f8dac)\n### Warning: JavaScript is disabled on your browser. Parts of Neo4j.com will not work properly.\nBlog Home\n\nClose\n\n- [Blog Home](https:\/\/neo4j.com\/blog\/)\n- [Developer](https:\/\/neo4j.com\/blog\/developer\/)\n- [GenAI](https:\/\/neo4j.com\/blog\/genai\/)\n- [News](https:\/\/neo4j.com\/blog\/news\/)\n\n- [Machine Learning](https:\/\/neo4j.com\/blog\/machine-learning\/)\n# Graph Data Platforms: Collections vs. Connections\n![Photo of Dave Packer](https:\/\/dist.neo4j.com\/wp-content\/uploads\/20201207084935\/dave-packer-150x150.jpg)\n\n[Dave Packer](https:\/\/neo4j.com\/blog\/contributor\/dave-packer\/)\n\nVice President, Product Marketing\n\nJanuary 11, 2021\n\n1 min read\n\n![](https:\/\/dist.neo4j.com\/wp-content\/uploads\/20210105073827\/Graph-Data-Platforms2.png)\n\n**You may have heard that graph database technology underpins the latest in machine learning and AI.** But what are [opens in new tabgraph data platforms](https:\/\/neo4j.com\/product\/?ref=blog) and why are they rapidly becoming the first choice for application development? Can a shift in database technology really make a big difference?\n\nIn this second blog of our two-part series, we look at how SQL databases and NoSQL databases handle connected data and explain how graph data platforms, focused on data relationships, drive numerous benefits.\n\n[![Feature Image Graph Data Platforms](https:\/\/dist.neo4j.com\/wp-content\/uploads\/20210105073827\/Graph-Data-Platforms2.png)](https:\/\/dist.neo4j.com\/wp-content\/uploads\/20210105073827\/Graph-Data-Platforms2.png)\n\n### SQL & NoSQL Systems Focus on Data Aggregation & Collection\nCollection-centric storage designs as implemented by SQL and Not only SQL (NoSQL) databases are designed to efficiently divide and store data.\n\nIn SQL’s case, the normalization of data into a tabular schema aims to minimize storage of duplicate data objects, types and values. These systems were born during the era of scarce physical memory and expensive disk-based storage, designed to avoid managing often-redundant data objects such as physical location addresses for shipping, billing, homes, offices, destinations, businesses, etc. For example, all of these data objects included common, redundant data such as a country and its provinces or states, or telephone area codes and postal codes.\n\nThe original relational databases were designed to minimize storage of duplicative data values because disk space was costly. (Ironically, each normalization incurs a cost in relationship storage in the form of JOIN tables, which [opens in new tabnative graph databases](https:\/\/neo4j.com\/blog\/cypher-and-gql\/native-vs-non-native-graph-technology\/?ref=blog) have managed to eliminate through the use of pointers.) The RDBMS design achieved this consolidated, normalized goal by linking tables of data via foreign keys to their associated records from other tables. This is why a relational dataset modeled into a graph often shrinks by an order of magnitude or more, maintaining the full richness of the data without redundant data storage.\n\nNoSQL systems like document, wide column and key-value data stores carry those concepts forward (and backward) by simplifying their models in exchange for higher levels of scale and simplicity. By eschewing data relationships and providing straightforward programmatic APIs, NoSQL systems make it easy for developers and administrators to work with simple data in a way that can easily scale.\n\n[![](https:\/\/dist.neo4j.com\/wp-content\/uploads\/20210105074555\/ArrowGraphDataPlatforms-1024x234.png)](https:\/\/dist.neo4j.com\/wp-content\/uploads\/20210105074555\/ArrowGraphDataPlatforms-1024x234.png)\n\nA lack of concern about relationships leads to looser data guarantees, plain APIs and straightforward scaling schemes. Data is easily spread out and just as easily retrieved, without the need to maintain the integrity of related data that’s written across a distributed storage or a cluster of machines and without needing to concern itself with the performance of distributed JOINs across those machines.\n\nThese systems take on the “store and retrieve” problem at scale for simple data, and their architectures reflect this, as does the set of problems they are equipped to address. However, none of these systems focus on interrelated, contextualized data or how that data might be traversed to reveal unobvious relationships, as explained below.\n\n[![](https:\/\/dist.neo4j.com\/wp-content\/uploads\/20210105074214\/Company-Graph-1024x368.png)](https:\/\/dist.neo4j.com\/wp-content\/uploads\/20210105074214\/Company-Graph-1024x368.png)\n\n#### Graph Systems Focus on Data Connections\nBy contrast, graph database technologies focus on how data elements are interrelated and contextualized as connected data.\n\nConnected data is the materialization and harnessing of relationships between data elements, which is modeled as a property graph.\n\nA property graph is a data model designed to express data connectedness as nodes connected via relationships to other nodes, where both nodes and relationships can have properties attached to them (which in turn can be indexed). For example, devices on an enterprise network might be modeled as nodes, with properties for their attributes (such as throughput) and relationships pointing to other adjacent devices.\n\nIn the graph model, data relationships are persisted so they can be navigated or traversed along connected paths to gain context. Relationships are both typed and directional.\n\nThe context provided by these data connections is essential to identifying friendships, making relevant real-time recommendations, attaching adjacent ideas and detecting fraud by following money trails. Without relationships as first-class data entities, all of these use cases  \n become extremely difficult to execute.\n\n### Property Graphs Are Intentionally Simple\n- You can draw property graphs on whiteboards and map that design directly into a  \n   graph database.\n- You can change or update a property graph easily, because its agile design eliminates  \n   most of the structural overhead of traditional database schemas.\n- You can quickly program property graphs because their query language expresses and  \n   follows relationships.\n- You can visualize and navigate property graphs efficiently by following the relationships  \n   on their paths to context.\n- You can rapidly determine data context when property graph queries are executed in  \n   hyper-fast native graph platforms built on reliable, scalable database architectures.\n### Benefits of Graph Databases\n- **Simple and natural data modeling**: Graph databases provide flexibility for data  \n   modeling, depending on relationship types. Since the graph model comes with no  \n   inherent rules, graph data stores add as much or as little semantic meaning as the  \n   domain requires. This occurs without any constraints like normalization or restructuring  \n   of the data using denormalization.\n- **Flexibility for evolving data structures**: Graph technology provides flexible schema  \n   evolution. In a constantly changing data environment, you need the option to add or  \n   drop data entities or relationships, as well as extend or modify your data model. Graph  \n   databases allow for evolving data structures that match today’s agile development  \n   environments.\n- **Simultaneous support for real-time updates and queries**: A graph data store and  \n   its model allow real-time updates on graph data while supporting queries concurrently.\n- **Better, faster and more powerful querying and analytics**: Graph data stores provide superior query performance with connected data using native storage and  \n   native indexed data structure.\n\nConnected data in property graphs enables you to illustrate and traverse many relationships and find context for your next breakthrough application or analysis.\n\n### Conclusion\nToday, businesses need to bring together data from many different systems, which is driving a huge shift from on-premises to cloud as well as investments in new data integration models. Traditional rigid systems aren’t nimble enough to keep up with the pace of change. Supporting complex data environments often requires real-time data analysis to make split-second decisions to optimize the business.\n\nMaking the most of your data requires the ability to make connections across all of it. As the fastest growing database technology in the past decade, graph databases provide a much more scalable, secure and flexible platform than traditional databases for enterprises to deploy their use cases – from real-time recommendation engines to fraud detection to knowledge graphs to AI and machine learning.\n\nWhat will your first use case be? Find out more about Neo4j. Sign up for a [opens in new tab30-minute demo](https:\/\/neo4j.com\/live-demos\/?ref=blog) with a live Q\\&A.\n\n**Ready to learn more? Click below to download your free copy of [opens in new tab*A Brief Introduction to Graph Data Platforms*](https:\/\/neo4j.com\/whitepapers\/brief-intro-graph-data-platforms\/?ref=blog).**\n\n[Get My Free White Paper](https:\/\/neo4j.com\/whitepapers\/brief-intro-graph-data-platforms\/?ref=blog)\n\n- [AI \/ Machine Learning](https:\/\/neo4j.com\/blog\/tag\/ai-machine-learning\/)\n- [Collections vs. Connections](https:\/\/neo4j.com\/blog\/tag\/collections-vs-connections\/)\n- [Data Relationships](https:\/\/neo4j.com\/blog\/tag\/data-relationships\/)\n- [graph data platforms](https:\/\/neo4j.com\/blog\/tag\/graph-data-platforms\/)\n- [nosql](https:\/\/neo4j.com\/blog\/tag\/nosql\/)\n- [real-time recommendation](https:\/\/neo4j.com\/blog\/tag\/real-time-recommendation\/)\n- [relational databases vs. graph](https:\/\/neo4j.com\/blog\/tag\/relational-databases-vs-graph\/)\n\n## Share Article\n- [opens in new tab](https:\/\/www.linkedin.com\/shareArticle?mini=true&url=https:\/\/neo4j.com\/blog\/machine-learning\/graph-data-platforms-collections-vs-connections\/)\n- [opens in new tab](https:\/\/twitter.com\/intent\/tweet?url=https:\/\/neo4j.com\/blog\/machine-learning\/graph-data-platforms-collections-vs-connections\/&text=)\n- [opens in new tab](https:\/\/www.facebook.com\/sharer\/sharer.php?u=https:\/\/neo4j.com\/blog\/machine-learning\/graph-data-platforms-collections-vs-connections\/)\n\n[![](https:\/\/dist.neo4j.com\/wp-content\/uploads\/20250430121502\/Aura-1.jpg)](https:\/\/neo4j.com\/product\/auradb\/)\n\n[opens in new tab![](https:\/\/dist.neo4j.com\/wp-content\/uploads\/20250430121707\/GA.jpg)](https:\/\/graphacademy.neo4j.com\/)\n\n[![](https:\/\/dist.neo4j.com\/wp-content\/uploads\/20250430121852\/VideoHub.jpg)](https:\/\/neo4j.com\/videos\/)\n## Explore\n- [Knowledge Graph](https:\/\/neo4j.com\/blog\/knowledge-graph\/)\n- [Supply Chain & Logistics](https:\/\/neo4j.com\/blog\/supply-chain-and-logistics\/)\n- [Fraud Detection](https:\/\/neo4j.com\/blog\/fraud-detection\/)\n- [Graph Visualization](https:\/\/neo4j.com\/blog\/graph-visualization\/)\n- [AuraDB](https:\/\/neo4j.com\/blog\/auradb\/)\n- [Digital Twin](https:\/\/neo4j.com\/blog\/digital-twin\/)\n\n***\n## Related Articles\n![](https:\/\/dist.neo4j.com\/wp-content\/uploads\/20241204070836\/what-is-graphrag.png)\n\n- [GenAI](https:\/\/neo4j.com\/blog\/genai\/)\n- [Knowledge Graph](https:\/\/neo4j.com\/blog\/knowledge-graph\/)\n- [Machine Learning](https:\/\/neo4j.com\/blog\/machine-learning\/)\n## What Is GraphRAG?\n[13 min read](https:\/\/neo4j.com\/blog\/genai\/what-is-graphrag\/)\n\n![](https:\/\/dist.neo4j.com\/wp-content\/uploads\/20260317194948\/dijksta.png)\n\n- [Aura Graph Analytics](https:\/\/neo4j.com\/blog\/aura-graph-analytics\/)\n- [AuraDB](https:\/\/neo4j.com\/blog\/auradb\/)\n- [Graph Data Science](https:\/\/neo4j.com\/blog\/graph-data-science\/)\n## Finding the Fastest Way Out: How Dijkstra’s Algorithm Finds Shortest Paths\n[5 min read](https:\/\/neo4j.com\/blog\/aura-graph-analytics\/finding-the-fastest-way-out-how-dijkstras-algorithm-finds-shortest-paths\/)\n\n![](https:\/\/dist.neo4j.com\/wp-content\/uploads\/20260224105201\/pagerank-3.png)\n\n- [Aura Graph Analytics](https:\/\/neo4j.com\/blog\/aura-graph-analytics\/)\n- [AuraDB](https:\/\/neo4j.com\/blog\/auradb\/)\n- [Graph Data Science](https:\/\/neo4j.com\/blog\/graph-data-science\/)\n## Whose Signature Really Matters? Understanding PageRank Through Yearbook Signatures\n[1 min read](https:\/\/neo4j.com\/blog\/aura-graph-analytics\/whose-signature-really-matters-understanding-pagerank-through-yearbook-signatures\/)\n\n![](https:\/\/dist.neo4j.com\/wp-content\/uploads\/20260126142956\/humanity-workplace15.jpg)\n\n- [Aura Graph Analytics](https:\/\/neo4j.com\/blog\/aura-graph-analytics\/)\n- [Graph Data Science](https:\/\/neo4j.com\/blog\/graph-data-science\/)\n- [Graph Database](https:\/\/neo4j.com\/blog\/graph-database\/)\n## Why LeBron James Shouldn’t Drive Your Recommendations: The Intuition Behind the Jaccard Coefficient\n[4 min read](https:\/\/neo4j.com\/blog\/aura-graph-analytics\/why-lebron-shouldnt-drive-your-recommendations-the-intuition-behind-the-jaccard-coefficient\/)\n\n![](https:\/\/dist.neo4j.com\/wp-content\/uploads\/20250528044140\/what-are-graph-algorithms-e1748432767472.png)\n\n- [Aura Graph Analytics](https:\/\/neo4j.com\/blog\/aura-graph-analytics\/)\n- [Graph Data Science](https:\/\/neo4j.com\/blog\/graph-data-science\/)\n- [Machine Learning](https:\/\/neo4j.com\/blog\/machine-learning\/)\n## What Are the Different Types of Graph Algorithms & When to Use Them?\n[8 min read](https:\/\/neo4j.com\/blog\/graph-data-science\/graph-algorithms\/)\n\n![Neo4j earns new AWS competencies.](https:\/\/dist.neo4j.com\/wp-content\/uploads\/20241203094441\/new-aws-competencies.png)\n\n- [Financial Services](https:\/\/neo4j.com\/blog\/financial-services\/)\n- [GenAI](https:\/\/neo4j.com\/blog\/genai\/)\n- [Machine Learning](https:\/\/neo4j.com\/blog\/machine-learning\/)\n## Neo4j Expands AWS Collaboration With New Competencies in Finance, Automotive, GenAI, and ML\n[5 min read](https:\/\/neo4j.com\/blog\/news\/new-aws-competencies\/)\n\n![](https:\/\/dist.neo4j.com\/wp-content\/uploads\/20230921083327\/homepage-viz_ART-left.svg) ![](https:\/\/dist.neo4j.com\/wp-content\/uploads\/20230921083329\/homepage-viz_ART-right.svg) 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Notes](https:\/\/neo4j.com\/release-notes\/)\n\nData Scientists\n\n- [Graph Data Science Home](https:\/\/neo4j.com\/product\/graph-data-science\/)\n- [Data Science Documentation](https:\/\/neo4j.com\/docs\/graph-data-science\/current\/)\n- [Get Started with Graph Data Science](https:\/\/neo4j.com\/graph-data-science-software\/)\n- [opens in new tabData Science Community](https:\/\/community.neo4j.com\/c\/neo4j-graph-platform\/graph-algorithms\/73)\n- [opens in new tabGraphAcademy for Data Science](https:\/\/graphacademy.neo4j.com\/categories\/data-scientist\/)\n\nLearn\n\n- [Resource Library](https:\/\/neo4j.com\/resources\/)\n- [Neo4j Blog](https:\/\/neo4j.com\/blog\/)\n- [opens in new tabGraphAcademy](https:\/\/graphacademy.neo4j.com\/)\n- [Research Center](https:\/\/neo4j.com\/research\/)\n- [Case Studies](https:\/\/neo4j.com\/customer-stories\/)\n- [Neo4j Video Hub](https:\/\/neo4j.com\/videos\/)\n- [Neo4j Events Hub](https:\/\/neo4j.com\/events\/)\n- 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[Leadership](https:\/\/neo4j.com\/leadership\/)\n- [opens in new tabSupport](https:\/\/support.neo4j.com\/s\/)\n- [opens in new tabTrust Center](https:\/\/trust.neo4j.com\/)\n\n[Contact Us →](https:\/\/neo4j.com\/contact-us\/?ref=footer)\n\n- US: [1-855-636-4532](tel:1-855-636-4532)\n- Sweden: [\\+46 171 480 113](<tel:+46 171 480 113>)\n- UK: [\\+44 20 3868 3223](<tel:+44 20 3868 3223>)\n- France: [\\+33 (0) 1 88 46 13 20](<tel:+33 (0) 1 88 46 13 20>)\n- Singapore: [\\+65 6859 0336](<tel:+65 6859 0336>)\n- Australia: [\\+61 2 8395 2895](<tel:+61 2 8395 2895>)\n\nSocial Networks\n\n[opens in new tab](https:\/\/www.linkedin.com\/company\/neo4j)\n\n[opens in new tab](https:\/\/x.com\/neo4j)\n\n[opens in new tab](https:\/\/youtube.com\/neo4j)\n\n[opens in new tab](https:\/\/www.facebook.com\/neo4j.graph.database)\n\n[opens in new tab](https:\/\/community.neo4j.com\/)\n\n[opens in new tab](https:\/\/github.com\/neo4j)\n\n© 2026 Neo4j, Inc.\n\n[Terms](https:\/\/legal.neo4j.com\/#website-terms-of-use) \\| [Privacy Notice](https:\/\/neo4j.com\/legal-terms\/privacy-notice\/) \\| [Sitemap](https:\/\/neo4j.com\/sitemap\/)   \n [opens in new tabAnti-Corruption Policy](https:\/\/assets.neo4j.com\/Neo4j_Anti-Corruption_Policy.pdf)\n\n©2026 Neo4j, Inc., Neo Technology®, Neo4j®, Cypher®, Neo4j Bloom™, Neo4j Graph Data Science Library™, Neo4j® Aura™, and Neo4j® AuraDB™ are registered trademarks or a trademark of Neo4j, Inc. All other marks are owned by their respective companies.\n\n[![](https:\/\/dist.neo4j.com\/wp-content\/uploads\/20210608133508\/icon-tooltip-info.svg) Contact Us](https:\/\/neo4j.com\/contact-us\/)","attrs_readable_markdown":"**You may have heard that graph database technology underpins the latest in machine learning and AI.** But what are [opens in new tabgraph data platforms](https:\/\/neo4j.com\/product\/?ref=blog) and why are they rapidly becoming the first choice for application development? Can a shift in database technology really make a big difference?\n\nIn this second blog of our two-part series, we look at how SQL databases and NoSQL databases handle connected data and explain how graph data platforms, focused on data relationships, drive numerous benefits.\n\n[![Feature Image Graph Data Platforms](https:\/\/dist.neo4j.com\/wp-content\/uploads\/20210105073827\/Graph-Data-Platforms2.png)](https:\/\/dist.neo4j.com\/wp-content\/uploads\/20210105073827\/Graph-Data-Platforms2.png)\n\n### SQL & NoSQL Systems Focus on Data Aggregation & Collection\nCollection-centric storage designs as implemented by SQL and Not only SQL (NoSQL) databases are designed to efficiently divide and store data.\n\nIn SQL’s case, the normalization of data into a tabular schema aims to minimize storage of duplicate data objects, types and values. These systems were born during the era of scarce physical memory and expensive disk-based storage, designed to avoid managing often-redundant data objects such as physical location addresses for shipping, billing, homes, offices, destinations, businesses, etc. For example, all of these data objects included common, redundant data such as a country and its provinces or states, or telephone area codes and postal codes.\n\nThe original relational databases were designed to minimize storage of duplicative data values because disk space was costly. (Ironically, each normalization incurs a cost in relationship storage in the form of JOIN tables, which [opens in new tabnative graph databases](https:\/\/neo4j.com\/blog\/cypher-and-gql\/native-vs-non-native-graph-technology\/?ref=blog) have managed to eliminate through the use of pointers.) The RDBMS design achieved this consolidated, normalized goal by linking tables of data via foreign keys to their associated records from other tables. This is why a relational dataset modeled into a graph often shrinks by an order of magnitude or more, maintaining the full richness of the data without redundant data storage.\n\nNoSQL systems like document, wide column and key-value data stores carry those concepts forward (and backward) by simplifying their models in exchange for higher levels of scale and simplicity. By eschewing data relationships and providing straightforward programmatic APIs, NoSQL systems make it easy for developers and administrators to work with simple data in a way that can easily scale.\n\nA lack of concern about relationships leads to looser data guarantees, plain APIs and straightforward scaling schemes. Data is easily spread out and just as easily retrieved, without the need to maintain the integrity of related data that’s written across a distributed storage or a cluster of machines and without needing to concern itself with the performance of distributed JOINs across those machines.\n\nThese systems take on the “store and retrieve” problem at scale for simple data, and their architectures reflect this, as does the set of problems they are equipped to address. However, none of these systems focus on interrelated, contextualized data or how that data might be traversed to reveal unobvious relationships, as explained below.\n\n#### Graph Systems Focus on Data Connections\nBy contrast, graph database technologies focus on how data elements are interrelated and contextualized as connected data.\n\nConnected data is the materialization and harnessing of relationships between data elements, which is modeled as a property graph.\n\nA property graph is a data model designed to express data connectedness as nodes connected via relationships to other nodes, where both nodes and relationships can have properties attached to them (which in turn can be indexed). For example, devices on an enterprise network might be modeled as nodes, with properties for their attributes (such as throughput) and relationships pointing to other adjacent devices.\n\nIn the graph model, data relationships are persisted so they can be navigated or traversed along connected paths to gain context. Relationships are both typed and directional.\n\nThe context provided by these data connections is essential to identifying friendships, making relevant real-time recommendations, attaching adjacent ideas and detecting fraud by following money trails. Without relationships as first-class data entities, all of these use cases  \n become extremely difficult to execute.\n\n### Property Graphs Are Intentionally Simple\n- You can draw property graphs on whiteboards and map that design directly into a  \n   graph database.\n- You can change or update a property graph easily, because its agile design eliminates  \n   most of the structural overhead of traditional database schemas.\n- You can quickly program property graphs because their query language expresses and  \n   follows relationships.\n- You can visualize and navigate property graphs efficiently by following the relationships  \n   on their paths to context.\n- You can rapidly determine data context when property graph queries are executed in  \n   hyper-fast native graph platforms built on reliable, scalable database architectures.\n### Benefits of Graph Databases\n- **Simple and natural data modeling**: Graph databases provide flexibility for data  \n   modeling, depending on relationship types. Since the graph model comes with no  \n   inherent rules, graph data stores add as much or as little semantic meaning as the  \n   domain requires. This occurs without any constraints like normalization or restructuring  \n   of the data using denormalization.\n- **Flexibility for evolving data structures**: Graph technology provides flexible schema  \n   evolution. In a constantly changing data environment, you need the option to add or  \n   drop data entities or relationships, as well as extend or modify your data model. Graph  \n   databases allow for evolving data structures that match today’s agile development  \n   environments.\n- **Simultaneous support for real-time updates and queries**: A graph data store and  \n   its model allow real-time updates on graph data while supporting queries concurrently.\n- **Better, faster and more powerful querying and analytics**: Graph data stores provide superior query performance with connected data using native storage and  \n   native indexed data structure.\n\nConnected data in property graphs enables you to illustrate and traverse many relationships and find context for your next breakthrough application or analysis.\n\n### Conclusion\nToday, businesses need to bring together data from many different systems, which is driving a huge shift from on-premises to cloud as well as investments in new data integration models. Traditional rigid systems aren’t nimble enough to keep up with the pace of change. Supporting complex data environments often requires real-time data analysis to make split-second decisions to optimize the business.\n\nMaking the most of your data requires the ability to make connections across all of it. As the fastest growing database technology in the past decade, graph databases provide a much more scalable, secure and flexible platform than traditional databases for enterprises to deploy their use cases – from real-time recommendation engines to fraud detection to knowledge graphs to AI and machine learning.\n\nWhat will your first use case be? Find out more about Neo4j. Sign up for a [opens in new tab30-minute demo](https:\/\/neo4j.com\/live-demos\/?ref=blog) with a live Q\\&A.","meta_canonical":null}

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Boilerpipe Text	You may have heard that graph database technology underpins the latest in machine learning and AI. But what are opens in new tab graph data platforms and why are they rapidly becoming the first choice for application development? Can a shift in database technology really make a big difference? In this second blog of our two-part series, we look at how SQL databases and NoSQL databases handle connected data and explain how graph data platforms, focused on data relationships, drive numerous benefits. SQL & NoSQL Systems Focus on Data Aggregation & Collection Collection-centric storage designs as implemented by SQL and Not only SQL (NoSQL) databases are designed to efficiently divide and store data. In SQL’s case, the normalization of data into a tabular schema aims to minimize storage of duplicate data objects, types and values. These systems were born during the era of scarce physical memory and expensive disk-based storage, designed to avoid managing often-redundant data objects such as physical location addresses for shipping, billing, homes, offices, destinations, businesses, etc. For example, all of these data objects included common, redundant data such as a country and its provinces or states, or telephone area codes and postal codes. The original relational databases were designed to minimize storage of duplicative data values because disk space was costly. (Ironically, each normalization incurs a cost in relationship storage in the form of JOIN tables, which opens in new tab native graph databases have managed to eliminate through the use of pointers.) The RDBMS design achieved this consolidated, normalized goal by linking tables of data via foreign keys to their associated records from other tables. This is why a relational dataset modeled into a graph often shrinks by an order of magnitude or more, maintaining the full richness of the data without redundant data storage. NoSQL systems like document, wide column and key-value data stores carry those concepts forward (and backward) by simplifying their models in exchange for higher levels of scale and simplicity. By eschewing data relationships and providing straightforward programmatic APIs, NoSQL systems make it easy for developers and administrators to work with simple data in a way that can easily scale. A lack of concern about relationships leads to looser data guarantees, plain APIs and straightforward scaling schemes. Data is easily spread out and just as easily retrieved, without the need to maintain the integrity of related data that’s written across a distributed storage or a cluster of machines and without needing to concern itself with the performance of distributed JOINs across those machines. These systems take on the “store and retrieve” problem at scale for simple data, and their architectures reflect this, as does the set of problems they are equipped to address. However, none of these systems focus on interrelated, contextualized data or how that data might be traversed to reveal unobvious relationships, as explained below. Graph Systems Focus on Data Connections By contrast, graph database technologies focus on how data elements are interrelated and contextualized as connected data. Connected data is the materialization and harnessing of relationships between data elements, which is modeled as a property graph. A property graph is a data model designed to express data connectedness as nodes connected via relationships to other nodes, where both nodes and relationships can have properties attached to them (which in turn can be indexed). For example, devices on an enterprise network might be modeled as nodes, with properties for their attributes (such as throughput) and relationships pointing to other adjacent devices. In the graph model, data relationships are persisted so they can be navigated or traversed along connected paths to gain context. Relationships are both typed and directional. The context provided by these data connections is essential to identifying friendships, making relevant real-time recommendations, attaching adjacent ideas and detecting fraud by following money trails. Without relationships as first-class data entities, all of these use cases become extremely difficult to execute. Property Graphs Are Intentionally Simple You can draw property graphs on whiteboards and map that design directly into a graph database. You can change or update a property graph easily, because its agile design eliminates most of the structural overhead of traditional database schemas. You can quickly program property graphs because their query language expresses and follows relationships. You can visualize and navigate property graphs efficiently by following the relationships on their paths to context. You can rapidly determine data context when property graph queries are executed in hyper-fast native graph platforms built on reliable, scalable database architectures. Benefits of Graph Databases Simple and natural data modeling : Graph databases provide flexibility for data modeling, depending on relationship types. Since the graph model comes with no inherent rules, graph data stores add as much or as little semantic meaning as the domain requires. This occurs without any constraints like normalization or restructuring of the data using denormalization. Flexibility for evolving data structures : Graph technology provides flexible schema evolution. In a constantly changing data environment, you need the option to add or drop data entities or relationships, as well as extend or modify your data model. Graph databases allow for evolving data structures that match today’s agile development environments. Simultaneous support for real-time updates and queries : A graph data store and its model allow real-time updates on graph data while supporting queries concurrently. Better, faster and more powerful querying and analytics : Graph data stores provide superior query performance with connected data using native storage and native indexed data structure. Connected data in property graphs enables you to illustrate and traverse many relationships and find context for your next breakthrough application or analysis. Conclusion Today, businesses need to bring together data from many different systems, which is driving a huge shift from on-premises to cloud as well as investments in new data integration models. Traditional rigid systems aren’t nimble enough to keep up with the pace of change. Supporting complex data environments often requires real-time data analysis to make split-second decisions to optimize the business. Making the most of your data requires the ability to make connections across all of it. As the fastest growing database technology in the past decade, graph databases provide a much more scalable, secure and flexible platform than traditional databases for enterprises to deploy their use cases – from real-time recommendation engines to fraud detection to knowledge graphs to AI and machine learning. What will your first use case be? Find out more about Neo4j. Sign up for a opens in new tab 30-minute demo with a live Q&A.
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Parts of Neo4j.com will not work properly. Blog Home Close - [Blog Home](https://neo4j.com/blog/) - [Developer](https://neo4j.com/blog/developer/) - [GenAI](https://neo4j.com/blog/genai/) - [News](https://neo4j.com/blog/news/) - [Machine Learning](https://neo4j.com/blog/machine-learning/) # Graph Data Platforms: Collections vs. Connections ![Photo of Dave Packer](https://dist.neo4j.com/wp-content/uploads/20201207084935/dave-packer-150x150.jpg) [Dave Packer](https://neo4j.com/blog/contributor/dave-packer/) Vice President, Product Marketing January 11, 2021 1 min read ![](https://dist.neo4j.com/wp-content/uploads/20210105073827/Graph-Data-Platforms2.png) You may have heard that graph database technology underpins the latest in machine learning and AI. But what are [opens in new tabgraph data platforms](https://neo4j.com/product/?ref=blog) and why are they rapidly becoming the first choice for application development? Can a shift in database technology really make a big difference? In this second blog of our two-part series, we look at how SQL databases and NoSQL databases handle connected data and explain how graph data platforms, focused on data relationships, drive numerous benefits. [![Feature Image Graph Data Platforms](https://dist.neo4j.com/wp-content/uploads/20210105073827/Graph-Data-Platforms2.png)](https://dist.neo4j.com/wp-content/uploads/20210105073827/Graph-Data-Platforms2.png) ### SQL & NoSQL Systems Focus on Data Aggregation & Collection Collection-centric storage designs as implemented by SQL and Not only SQL (NoSQL) databases are designed to efficiently divide and store data. In SQL’s case, the normalization of data into a tabular schema aims to minimize storage of duplicate data objects, types and values. These systems were born during the era of scarce physical memory and expensive disk-based storage, designed to avoid managing often-redundant data objects such as physical location addresses for shipping, billing, homes, offices, destinations, businesses, etc. For example, all of these data objects included common, redundant data such as a country and its provinces or states, or telephone area codes and postal codes. The original relational databases were designed to minimize storage of duplicative data values because disk space was costly. (Ironically, each normalization incurs a cost in relationship storage in the form of JOIN tables, which [opens in new tabnative graph databases](https://neo4j.com/blog/cypher-and-gql/native-vs-non-native-graph-technology/?ref=blog) have managed to eliminate through the use of pointers.) The RDBMS design achieved this consolidated, normalized goal by linking tables of data via foreign keys to their associated records from other tables. This is why a relational dataset modeled into a graph often shrinks by an order of magnitude or more, maintaining the full richness of the data without redundant data storage. NoSQL systems like document, wide column and key-value data stores carry those concepts forward (and backward) by simplifying their models in exchange for higher levels of scale and simplicity. By eschewing data relationships and providing straightforward programmatic APIs, NoSQL systems make it easy for developers and administrators to work with simple data in a way that can easily scale. [![](https://dist.neo4j.com/wp-content/uploads/20210105074555/ArrowGraphDataPlatforms-1024x234.png)](https://dist.neo4j.com/wp-content/uploads/20210105074555/ArrowGraphDataPlatforms-1024x234.png) A lack of concern about relationships leads to looser data guarantees, plain APIs and straightforward scaling schemes. Data is easily spread out and just as easily retrieved, without the need to maintain the integrity of related data that’s written across a distributed storage or a cluster of machines and without needing to concern itself with the performance of distributed JOINs across those machines. These systems take on the “store and retrieve” problem at scale for simple data, and their architectures reflect this, as does the set of problems they are equipped to address. However, none of these systems focus on interrelated, contextualized data or how that data might be traversed to reveal unobvious relationships, as explained below. [![](https://dist.neo4j.com/wp-content/uploads/20210105074214/Company-Graph-1024x368.png)](https://dist.neo4j.com/wp-content/uploads/20210105074214/Company-Graph-1024x368.png) #### Graph Systems Focus on Data Connections By contrast, graph database technologies focus on how data elements are interrelated and contextualized as connected data. Connected data is the materialization and harnessing of relationships between data elements, which is modeled as a property graph. A property graph is a data model designed to express data connectedness as nodes connected via relationships to other nodes, where both nodes and relationships can have properties attached to them (which in turn can be indexed). For example, devices on an enterprise network might be modeled as nodes, with properties for their attributes (such as throughput) and relationships pointing to other adjacent devices. In the graph model, data relationships are persisted so they can be navigated or traversed along connected paths to gain context. Relationships are both typed and directional. The context provided by these data connections is essential to identifying friendships, making relevant real-time recommendations, attaching adjacent ideas and detecting fraud by following money trails. Without relationships as first-class data entities, all of these use cases become extremely difficult to execute. ### Property Graphs Are Intentionally Simple - You can draw property graphs on whiteboards and map that design directly into a graph database. - You can change or update a property graph easily, because its agile design eliminates most of the structural overhead of traditional database schemas. - You can quickly program property graphs because their query language expresses and follows relationships. - You can visualize and navigate property graphs efficiently by following the relationships on their paths to context. - You can rapidly determine data context when property graph queries are executed in hyper-fast native graph platforms built on reliable, scalable database architectures. ### Benefits of Graph Databases - Simple and natural data modeling: Graph databases provide flexibility for data modeling, depending on relationship types. Since the graph model comes with no inherent rules, graph data stores add as much or as little semantic meaning as the domain requires. This occurs without any constraints like normalization or restructuring of the data using denormalization. - Flexibility for evolving data structures: Graph technology provides flexible schema evolution. In a constantly changing data environment, you need the option to add or drop data entities or relationships, as well as extend or modify your data model. Graph databases allow for evolving data structures that match today’s agile development environments. - Simultaneous support for real-time updates and queries: A graph data store and its model allow real-time updates on graph data while supporting queries concurrently. - Better, faster and more powerful querying and analytics: Graph data stores provide superior query performance with connected data using native storage and native indexed data structure. Connected data in property graphs enables you to illustrate and traverse many relationships and find context for your next breakthrough application or analysis. ### Conclusion Today, businesses need to bring together data from many different systems, which is driving a huge shift from on-premises to cloud as well as investments in new data integration models. Traditional rigid systems aren’t nimble enough to keep up with the pace of change. Supporting complex data environments often requires real-time data analysis to make split-second decisions to optimize the business. Making the most of your data requires the ability to make connections across all of it. As the fastest growing database technology in the past decade, graph databases provide a much more scalable, secure and flexible platform than traditional databases for enterprises to deploy their use cases – from real-time recommendation engines to fraud detection to knowledge graphs to AI and machine learning. What will your first use case be? Find out more about Neo4j. Sign up for a [opens in new tab30-minute demo](https://neo4j.com/live-demos/?ref=blog) with a live Q\&A. *Ready to learn more? Click below to download your free copy of [opens in new tabA Brief Introduction to Graph Data Platforms](https://neo4j.com/whitepapers/brief-intro-graph-data-platforms/?ref=blog).* [Get My Free White Paper](https://neo4j.com/whitepapers/brief-intro-graph-data-platforms/?ref=blog) - [AI / Machine Learning](https://neo4j.com/blog/tag/ai-machine-learning/) - [Collections vs. Connections](https://neo4j.com/blog/tag/collections-vs-connections/) - [Data Relationships](https://neo4j.com/blog/tag/data-relationships/) - [graph data platforms](https://neo4j.com/blog/tag/graph-data-platforms/) - [nosql](https://neo4j.com/blog/tag/nosql/) - [real-time recommendation](https://neo4j.com/blog/tag/real-time-recommendation/) - [relational databases vs. graph](https://neo4j.com/blog/tag/relational-databases-vs-graph/) ## Share Article - [opens in new tab](https://www.linkedin.com/shareArticle?mini=true&url=https://neo4j.com/blog/machine-learning/graph-data-platforms-collections-vs-connections/) - [opens in new tab](https://twitter.com/intent/tweet?url=https://neo4j.com/blog/machine-learning/graph-data-platforms-collections-vs-connections/&text=) - [opens in new tab](https://www.facebook.com/sharer/sharer.php?u=https://neo4j.com/blog/machine-learning/graph-data-platforms-collections-vs-connections/) [![](https://dist.neo4j.com/wp-content/uploads/20250430121502/Aura-1.jpg)](https://neo4j.com/product/auradb/) [opens in new tab![](https://dist.neo4j.com/wp-content/uploads/20250430121707/GA.jpg)](https://graphacademy.neo4j.com/) [![](https://dist.neo4j.com/wp-content/uploads/20250430121852/VideoHub.jpg)](https://neo4j.com/videos/) ## Explore - [Knowledge Graph](https://neo4j.com/blog/knowledge-graph/) - [Supply Chain & Logistics](https://neo4j.com/blog/supply-chain-and-logistics/) - [Fraud Detection](https://neo4j.com/blog/fraud-detection/) - [Graph Visualization](https://neo4j.com/blog/graph-visualization/) - [AuraDB](https://neo4j.com/blog/auradb/) - [Digital Twin](https://neo4j.com/blog/digital-twin/) *** ## Related Articles ![](https://dist.neo4j.com/wp-content/uploads/20241204070836/what-is-graphrag.png) - [GenAI](https://neo4j.com/blog/genai/) - [Knowledge Graph](https://neo4j.com/blog/knowledge-graph/) - [Machine Learning](https://neo4j.com/blog/machine-learning/) ## What Is GraphRAG? [13 min read](https://neo4j.com/blog/genai/what-is-graphrag/) ![](https://dist.neo4j.com/wp-content/uploads/20260317194948/dijksta.png) - [Aura Graph Analytics](https://neo4j.com/blog/aura-graph-analytics/) - [AuraDB](https://neo4j.com/blog/auradb/) - [Graph Data Science](https://neo4j.com/blog/graph-data-science/) ## Finding the Fastest Way Out: How Dijkstra’s Algorithm Finds Shortest Paths [5 min read](https://neo4j.com/blog/aura-graph-analytics/finding-the-fastest-way-out-how-dijkstras-algorithm-finds-shortest-paths/) ![](https://dist.neo4j.com/wp-content/uploads/20260224105201/pagerank-3.png) - [Aura Graph Analytics](https://neo4j.com/blog/aura-graph-analytics/) - [AuraDB](https://neo4j.com/blog/auradb/) - [Graph Data Science](https://neo4j.com/blog/graph-data-science/) ## Whose Signature Really Matters? 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Readable Markdown	You may have heard that graph database technology underpins the latest in machine learning and AI. But what are [opens in new tabgraph data platforms](https://neo4j.com/product/?ref=blog) and why are they rapidly becoming the first choice for application development? Can a shift in database technology really make a big difference? In this second blog of our two-part series, we look at how SQL databases and NoSQL databases handle connected data and explain how graph data platforms, focused on data relationships, drive numerous benefits. [![Feature Image Graph Data Platforms](https://dist.neo4j.com/wp-content/uploads/20210105073827/Graph-Data-Platforms2.png)](https://dist.neo4j.com/wp-content/uploads/20210105073827/Graph-Data-Platforms2.png) ### SQL & NoSQL Systems Focus on Data Aggregation & Collection Collection-centric storage designs as implemented by SQL and Not only SQL (NoSQL) databases are designed to efficiently divide and store data. In SQL’s case, the normalization of data into a tabular schema aims to minimize storage of duplicate data objects, types and values. These systems were born during the era of scarce physical memory and expensive disk-based storage, designed to avoid managing often-redundant data objects such as physical location addresses for shipping, billing, homes, offices, destinations, businesses, etc. For example, all of these data objects included common, redundant data such as a country and its provinces or states, or telephone area codes and postal codes. The original relational databases were designed to minimize storage of duplicative data values because disk space was costly. (Ironically, each normalization incurs a cost in relationship storage in the form of JOIN tables, which [opens in new tabnative graph databases](https://neo4j.com/blog/cypher-and-gql/native-vs-non-native-graph-technology/?ref=blog) have managed to eliminate through the use of pointers.) The RDBMS design achieved this consolidated, normalized goal by linking tables of data via foreign keys to their associated records from other tables. This is why a relational dataset modeled into a graph often shrinks by an order of magnitude or more, maintaining the full richness of the data without redundant data storage. NoSQL systems like document, wide column and key-value data stores carry those concepts forward (and backward) by simplifying their models in exchange for higher levels of scale and simplicity. By eschewing data relationships and providing straightforward programmatic APIs, NoSQL systems make it easy for developers and administrators to work with simple data in a way that can easily scale. A lack of concern about relationships leads to looser data guarantees, plain APIs and straightforward scaling schemes. Data is easily spread out and just as easily retrieved, without the need to maintain the integrity of related data that’s written across a distributed storage or a cluster of machines and without needing to concern itself with the performance of distributed JOINs across those machines. These systems take on the “store and retrieve” problem at scale for simple data, and their architectures reflect this, as does the set of problems they are equipped to address. However, none of these systems focus on interrelated, contextualized data or how that data might be traversed to reveal unobvious relationships, as explained below. #### Graph Systems Focus on Data Connections By contrast, graph database technologies focus on how data elements are interrelated and contextualized as connected data. Connected data is the materialization and harnessing of relationships between data elements, which is modeled as a property graph. A property graph is a data model designed to express data connectedness as nodes connected via relationships to other nodes, where both nodes and relationships can have properties attached to them (which in turn can be indexed). For example, devices on an enterprise network might be modeled as nodes, with properties for their attributes (such as throughput) and relationships pointing to other adjacent devices. In the graph model, data relationships are persisted so they can be navigated or traversed along connected paths to gain context. Relationships are both typed and directional. The context provided by these data connections is essential to identifying friendships, making relevant real-time recommendations, attaching adjacent ideas and detecting fraud by following money trails. Without relationships as first-class data entities, all of these use cases become extremely difficult to execute. ### Property Graphs Are Intentionally Simple - You can draw property graphs on whiteboards and map that design directly into a graph database. - You can change or update a property graph easily, because its agile design eliminates most of the structural overhead of traditional database schemas. - You can quickly program property graphs because their query language expresses and follows relationships. - You can visualize and navigate property graphs efficiently by following the relationships on their paths to context. - You can rapidly determine data context when property graph queries are executed in hyper-fast native graph platforms built on reliable, scalable database architectures. ### Benefits of Graph Databases - Simple and natural data modeling: Graph databases provide flexibility for data modeling, depending on relationship types. Since the graph model comes with no inherent rules, graph data stores add as much or as little semantic meaning as the domain requires. This occurs without any constraints like normalization or restructuring of the data using denormalization. - Flexibility for evolving data structures: Graph technology provides flexible schema evolution. In a constantly changing data environment, you need the option to add or drop data entities or relationships, as well as extend or modify your data model. Graph databases allow for evolving data structures that match today’s agile development environments. - Simultaneous support for real-time updates and queries: A graph data store and its model allow real-time updates on graph data while supporting queries concurrently. - Better, faster and more powerful querying and analytics: Graph data stores provide superior query performance with connected data using native storage and native indexed data structure. Connected data in property graphs enables you to illustrate and traverse many relationships and find context for your next breakthrough application or analysis. ### Conclusion Today, businesses need to bring together data from many different systems, which is driving a huge shift from on-premises to cloud as well as investments in new data integration models. Traditional rigid systems aren’t nimble enough to keep up with the pace of change. Supporting complex data environments often requires real-time data analysis to make split-second decisions to optimize the business. Making the most of your data requires the ability to make connections across all of it. As the fastest growing database technology in the past decade, graph databases provide a much more scalable, secure and flexible platform than traditional databases for enterprises to deploy their use cases – from real-time recommendation engines to fraud detection to knowledge graphs to AI and machine learning. 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