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The Year of the Graph

What is a graph database? Do you really need one, and if yes, how do you choose?

Graph databases have been around for almost 20 years, but it was AWS and Microsoft moving in the domain, with Neptune and Cosmos DB respectively, that attracted widespread interest. So this hitherto niche domain has been the hottest one for the last couple of years. Besides trends, however, there are real reasons why graph databases are interesting, and real use cases they can help with.

Global business leaders such as Airbnb, AstraZeneca, China Mobile, eBay, Telia, Uber and Zalando are using graph in production and reaping the benefits. Global technology leaders such as Google, Facebook, LinkedIn and Microsoft are pushing the boundaries of the technology at scale.

There’s more to Graph Databases than hype. There are very good reasons why AWS and Microsoft have chosen to enter this market, driving the hype.

From operational applications to analytics, and from knowledge graphs and data integration to machine learning, a graph database can give you an edge. As Graph Databases are going mainstream, the dynamics are changing dramatically. Besides new entries, existing players keep evolving.

This means there is a growing demand to learn about graph databases, how they can be used, and which ones are right for each use case. Meeting this demand, however, is not easy. Besides lack of expertise, in the real world there is lack of resources and time for proper education and evaluation in the subject matter. In other words, it’s not easy to educate yourself and make the right choices.

What people do in situations like this is they look for the experts to help them navigate the domain. In the case of graph databases, however, that’s easier said than done.

Existing Graph Database research is mostly not up to the task. Most of it is outdated, shallow, expensive, and meant to serve as a marketing tool, not a hands-on guide for decision makers. Its authors do not have enough hands-on graph database expertise to be relevant. There’s a place for marketing, but not when you want to make an educated decision based on holistic criteria, and live with it every day in production.

What happens when you don’t have sufficient expertise and time to evaluate your options, or a trusted advisor to help, is you make suboptimal choices. Don’t let this happen when choosing a graph database.

This is where the Year of the Graph report comes in. As there is a gap in the market for graph database evaluation, i have decided to address it. I’ve done the hard work, so you don’t have to, and this is what makes me the most qualified person to do this.

I’ve been working with graph databases since 2005, when i implemented my first graph database prototype. This includes award-winning R&D, startups, enterprises, and consulting the (then) top Graph Database vendor on distributed queries in 2008. I have also been active as an analyst, consultant and entrepreneur since 2012.

Some highlights: defining and analyzing Agile Business Intelligence long before the Gartners of the world, and working with the likes of Hortonworks, SAP, and more. Since 2016, i have also been a ZDNet contributor for the Big on Data column. I’ve been monitoring graph databases for ever, publishing the Year of the Graph newsletter since 2018.

I have hands-on, deep knowledge and experience in graph databases. I also have long-standing experience as an analyst, author, and influencer. I have a holistic view, from Enterprise Architecture to the C-level suite. I saw this coming, and called 2018 The Year of the Graph, again 8 months before Gartner included Knowledge Graph in its Hype Cycle.

Graphs are in Gartner’s Hype Cycle. Late to the party again – 8 months, or 20 years, depending on how you count.

The Year of the Graph Report

In the summer of 2018, i published the Year of the Graph report on Graph Databases. This is the result of months of hard work. I have designed a unique, holistic and data-driven methodology that combines every aspect you need to consider when evaluating graph databases.

The methodology is based on high level KPIs, which in turn are based on layers of metrics. For each metric, a multitude of data points have been collected. Besides methodology design and data collection, domain knowledge expertise has been leveraged.

The Year of the Graph 2018

The Year of the Graph 2018. The most comprehensive and up to date research on Graph Databases.

I have been working in collaboration with vendors to verify the results of the research. Pretty much all of them said this work is beyond anything they have ever seen before. Some of them have gone on record saying so as well.


Impressive work. Well done. I’m not aware of another source that is as comprehensive as this one. Thank you for that as it’s providing me value from a graph market research perspective.

Jonathan Lacefield, Senior Director of Product Management, DataStax Enterprise Server

Kudos to you for your relentless work on informing the community about their options in the graph sphere. Think the report will be highly appreciated – it’s on a whole different level!

Jan Stücke, Head of Communications, ArangoDB

Anadiotis may understand MarkLogic better than we do ourselves.

Rob Lawrence, former Director or Strategic Programs, MarkLogic

Sure, this is not the only available research on graph databases. If you are looking for primers, last year’s overpriced business briefs, or yesteryear’s fragmented analysis, you can check those out.

If you are looking for

  • Up to date, comprehensive, unbiased research by an analyst with hands-ón expertise
  • A unique methodology, combining data & metrics on business and technical aspects

Then the Year of the Graph Report is what you need. Many levels up, at a fraction of the price. Would you like to try before you buy? That’s also an option.

For the last few months i have been presenting this work at various venues, including events such as Big Data Spain – one of the biggest and most forward-looking events in Europe. Slides and video from the presentation are online, and i’d be happy to answer any questions you may have.

What you get

What you get when you buy the Year if the Graph Report is a bundle that includes:

  • The report. 40 pages of research, with key point summaries and images
  • Access to updates. The report will be updated regularly. Buying once gives you lifetime access to future releases, free or at reduced pricing.

Some key points:

It is important to define what a graph database is and what it is not.
A number of solutions offer graph-related features or analytic capabilities.
We define graph databases as the ones having the ability to fully support operational applications utilizing a graph data model and API.
The major choice when it comes to graph databases is the one regarding graph data models.
LPG (Labeled Property Graph) and RDF (Resource Description Framework) are the two options.
Databases utilizing each tend to have specific characteristics, making them more suitable for specific use cases.
Not being based exclusively on a graph data model does not necessarily mean being ruled out.
Multi-model graph databases support LPG or RDF, plus models such as key-value and document.
This makes for a more diverse platform, albeit possibly at the expense of optimizing for graph.
Cloud-only solutions from AWS and Microsoft are different from the rest and from each other.
Their features in terms of scalability and availability appear similar.
Their technical features are different, and they are at different maturity points.

The Year of the Graph Newsletter

If you knew and loved the newsletter, worry not. It’s not going away, and it’s always free.


Every month i publish and comment on the top Graph database related news items.


Why me? Ever since i implemented my first graph database prototype in 2005, i have worked on award-winning research, consulted the (then) leading vendor on distributed query implementation, and lead teams of all sizes and shapes working with graph databases.


I have also published a number of reports and articles with analysis on big data, distributed systems and analytics with Gigaom and ZDNet. Perhaps more importantly though:


I work for nobody else but me, and by extension, you. No fluff, just stuff. No automatically harvested or sponsored posts. No vendor affiliations or hidden agendas. Just hand picked, curated content, and objective, concise analysis.


Would you like to receive the latest Year of the Graph Newsletter in your inbox? Easy – just signup below. Have some news you think should be featured in an upcoming newsletter? Easy too – drop me a line here.

11March 20191. Graph data standardization: It’s just a graph, making gravitational waves in the real world 2. Gartner Identifies Top 10 Data and Analytics Technology Trends for 2019 3. Graph Technology Landscape 2019 4. Introducing Gremlin query hints for Amazon Neptune 5. "Thoughts on the future of Apache TinkerPop 6. An Experimental Comparison of Graph Databases 7. Why Google Needed a Graph Serving System 8. Google Shows Us How It Uses Entity Extractions for Knowledge Graphs 9. It’s Time To Make Your Data Speak For Itself! 10. AllegroGraph 6.5 Marks First Multi-model Semantic Graph and Document Database via JSON and JSON-LD 11. Solid: taking back the Web through decentralization 12. More Connected, More Data, More London: Connected Data London 2019 13. The XRP Ledger And Graphs: Pattern Detection And Fraud Investigation 13. Getting started with Provenance and Neo4j 14. Lean Dependencies- Reduce Project Delivery Chaos with Graphs 15. Viewing Matrices & Probability as Graphs
10February 20191. The Semantic Zoo – Smart Data Hubs, Knowledge Graphs and Data Catalogs 2. A general specification for implementing structured markup (using and JSON-LD) 3. What can a knowledge graph do for oncology? 4. Machine learning eclipses knowledge-based reasoning 5. "Nobody agrees on what AI is” – How Elsevier’s report used AI to define the undefinable 6. Knowledge Graphs & 10,000 pairs of hands 7. Contextualizing Airbnb by Building Knowledge Graph 8. Explainable Reasoning over Knowledge Graphs for Recommendation in eBay 9. Why Machine Learning Needs Semantics Not Just Statistics 10. Querying machine learning distributional semantics with SPARQL 11. Ontotext’s GraphDB 8.8.0 Enriches the Knowledge Graph Experience 12. The Year of the Graph in Review & the Emergence of Graph OLAP Databases 13. Graph software shines spotlight on data relationships 13. How Representative is a SPARQL Benchmark? An Analysis of RDF Triplestore Benchmarks 14. The Future of Graph Query Languages & an Introduction to SKOS 15. W3C Workshop on Web Standardization for Graph Data
9January 20191. The Year of the Graph 2019 trends 2. The Future of Data is Connected and Open Minded 3. Apache TinkerPop 3.4.0 Released 4. Designing a Linked Data developer experience 5. Wikidata and its SPARQL endpoint used to compare the mentions of programming languages 6. First Milestone Release of the GraphQL to Cypher Transpiler 7. Cosmos DB – Request Units charged for processing a Gremlin API request 8. Announcing Stardog 7 beta 9. Comparing Linked Data Triplestores 10. DataStax Presents: Property Graph Modeling 11. How to Avoid Doppelgängers in a Graph Database 12. PostgreSQL: Announcing the release of AgensGraph 2.0 13. Redfield has integrated OrientDB with Knime Analytics Platform 13. Ontology and Data Science 14. Exploring ontology visualisation techniques for biological data 15. How to get started with machine learning on graphs
8December 20181. The Year of the Graph 2. Benchmarking RedisGraph 1.0 3. On “Benchmarking RedisGraph 1.0” 4. Amazon Neptune: First Impressions 5. A New Neo4j Integration with Apache Kafka 6. Stardog Native MongoDB Support 7. How to Create and Query Labeled Property Graphs in AnzoGraph 8. Towards building the next generation database query engine 9. Will GraphQL Become a Standard for the New Data Economy? 10. Apache Atlas and JanusGraph – Graph-based Meta Data Management 11. The DBpedia Databus – Transforming Linked Data into a Networked Data Economy 12. Connected Data London recap
7November 2018
1. The Largest Single Investment in the Graph Space. 2. The Graph Traversal Machine 3. Analyzing and Improving the performance Azure Cosmos DB Gremlin queries 4. The curious case of Pagination for Gremlin queries 5. W3C Workshop on Web Standardization for Graph Data 6. International Semantic Web Conference (ISWC) 2018 Trip Report 7. Decentralizing the Semantic Web through incentivized collaboration 8. Google ponders the shortcomings of machine learning 9. Helping you along your Search journeys 10. 11.
KBpedia – Open-source Integrated Knowledge Structure
12. Where Ontologies End and Knowledge Graphs Begin 13. Difference between Ontologies and Vocabularies 14. What is an application network, and why does it need a graph? 15., a visual, collaborative, real-time ontology and knowledge graph schema editor 16. Creating a linked data endpoint on Neo4j 17. Dockerpedia 18. Using Wikidata to look for links between legislative behaviour and the biographies of Members of Congress 19. Introducing MarkLogic Data Hub Service 20. Semantics, meet Data Science 21. An overview of Graph Database and Graph Visualisation in 2018 22. The Year of the Graph in Berlin
6October 20181. Knowledge Graphs Beyond the Hype 2. What is a knowledge graph? 3. Knowledge Graphs can help collapse the IT Stack 4. Scaling Knowledge Access and Retrieval at Airbnb 5. Knowledge Graph and AI Experts for Connected Data London 2018 Program 6. What Is the Semantic Web and Why Should Enterprise Publishers Care? 7. Google can now search for datasets. First research, then the world? 8. On RDF and SPARQL 9. Azure Cosmos DB – database for Intelligent Cloud 10. Neo4j 3.5 Poised to Power the Next Generation of AI & Machine Learning Systems 11. Introducing the Latest Release of TigerGraph 12. Release Candidate 1 ArangoDB 3.4 13. Building Applications on Graph Databases
5September 20181. Knowledge Graphs - Connecting the Dots in an Increasingly Complex World 2. The biggest knowledge graph ever 3. How Do You Know If a Graph Database Solves the Problem? 4. Building Semantic Data Catalogs 5. Hubs & Authorities in Ethereum 6. Similarity search with Machine Learning in Stardog 7. Graphs and ML: Multiple Linear Regression 8. Neo4j Graph View Editor 9. Apache Tinkerpop IDE 10. Spring Data Gremlin for Azure Cosmos DB Graph API 11. SPARQL to Gremlin query bridge 12. Subgraphs in R using Gremlin 13. Ubiquity of Large Graphs and Surprising Challenges of Graph Processing 14. AWS Neptune vs TigerGraph benchmark
4July 20181. The Year of the Graph report 2. Graph DBs (CAGR) of 24.0%, market size of USD 2,409.1 million by 2023 3. A practical guide to graph databases 4. Graph tips and tricks 5. Tips for using the Gremlin API 6. Time traveling with graph databases 7. Distributed Graphs Processing With Spark GraphX 8. Selecting a Graph Query Language 9. Symbolic Methods Coming Back 10. CLEVR, a dataset for graph based reasoning 11. AI tool "DIG" exposes sex rings
3June 20181. AWS Neptune goes GA 2. Azure CosmosDB new features 3. GQL - a proposal for property graph query language 4. TigerGraph launches free developer edition 5. Practical Gremlin guide new edition 6. Type-safe Gramlin graph queries 7. Data science and graph databases 8. Enterprise knowledge graphs in the real world 9. Repaying the GDPR Data Governance Debt with Metadata and Semantics 10. Why JSON-LD is a good idea
2May 20181. DataStax Enterprise Graph 6.0: what’s new, and what’s coming? 2. What's New in Neo4j 3.4 3. Ontotext’s New GraphDB 8.5 4. What Are Graph Queries, Graph Algorithms And Graph Analytics? 5. How to explain index-free adjacency to your manager 6. MDM and Graph 7. Looking under the hood at Amazon Neptune 8. WebVOWL - Web-based Visualization of Ontologies 9. Transforming JSON output of SPARQL queries 10. Review prediction with Neo4j and TensorFlow
1April 20181. Why the year of the graph 2. Building a model for event data as a graph 3. Fishing for graphs in a Hadoop data lake 4. Graphs as the front end for machine learning 5. Harmonizing big data with an enterprise knowledge graph 6. Making true connections in a complex world 7. Ontology, Meaning and Knowledge Management 8. Neo4j: A Reasonable RDF Graph Database & Reasoning Engine 9. Ted Wilmes on the state of JanusGraph 2018 10. Back to the future: Does graph database success hang on query language?

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