Choosing the Right Graph Database
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The document discusses choosing the right graph database for projects. It describes Ontotext, a provider of graph database and semantic technology products. It outlines use cases for graph databases in areas like knowledge graphs, content management, and recommendations. The document then examines Ontotext's GraphDB semantic graph database product and how it can address key use cases. It provides guidance on choosing a GraphDB option based on project stage from learning to production.
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Choosing the Right Graph Database
- 1. Choosing the Right Graph Database to Succeed in Your Project Marin Dimitrov (CTO) Feb 2016
- 2. About Ontotext • Provides products & solutions for content enrichment and metadata management − Founded in 2000, 70 employees − HQ in Sofia (Bulgaria), sales presence in NYC and London • Major verticals − Media & publishing − Healthcare & life sciences − Cultural heritage & digital libraries − Government − Financial information providers − Education 2Feb 2016Choosing the Right Graph Database to Succeed in Your Project
- 3. Some of Our Customers 3Feb 2016Choosing the Right Graph Database to Succeed in Your Project
- 4. Smart Data Management 4 Semantic Graph Database • Flexible graph data model • Ontology data model & metadata layer Enrichment, Search, Discovery • Metadata driven content • Semantic, exploratory search • Information discovery + recommendations Text Mining & Interlinking • Organisations, people, locations, topics, relations • Discover implicit relations • Reuse open Knowledge Graphs • Interlink with reference data Feb 2016Choosing the Right Graph Database to Succeed in Your Project
- 5. Presentation Outline • Use Cases for Graph Databases • GraphDB by Ontotext • Choosing a Database for Your Project • Q & A 5Feb 2016Choosing the Right Graph Database to Succeed in Your Project
- 6. Graph Databases for Interconnected Data • Integration of heterogeneous data sources • Hierarchical or interconnected datasets • Agile “schema-late” data integration • Dynamic data models / schema evolution • Relationship centric analytics / discovery • Path traversal / navigation, sub-graph pattern matching • Property graph DBs vs Semantic graph DBs (triplestores, RDF DBs) 6Feb 2016Choosing the Right Graph Database to Succeed in Your Project
- 7. Semantic Graph Databases – Advantages • Simple, graph based data model • Exploratory queries against unknown schema • Agile schema / schema-less / schema-late • Rich, semantic data models (schema) • Easily map between data models (schemas) • Global identifiers of nodes & relations • Inference of implicit facts, based on rules • Compliance to standards (RDF, SPARQL), no vendor lock-in • Easy to publish / consume open Knowledge Graphs (Linked Data) 7Feb 2016Choosing the Right Graph Database to Succeed in Your Project
- 8. Semantic Graph Databases – Inferring New Facts 8Feb 2016Choosing the Right Graph Database to Succeed in Your Project
- 9. Typical Use Cases • Network analysis (social, influencer, risk, fraud, …) • Recommendation engines • Heterogeneous data integration • Master Data Management • Metadata driven content / dynamic content publishing • Knowledge Graphs / data sharing & reuse • Information discovery / semantic search #9Feb 2016Choosing the Right Graph Database to Succeed in Your Project
- 10. Use Cases – Knowledge Graphs 10Feb 2016Choosing the Right Graph Database to Succeed in Your Project
- 11. Use Cases – Content Management & Recommendation 11Feb 2016Choosing the Right Graph Database to Succeed in Your Project
- 12. Use Cases – Metadata-Driven Content Management & Recommendation 12Feb 2016Choosing the Right Graph Database to Succeed in Your Project
- 13. Ontotext and AstraZeneca 13 Profile • Global, Bio-pharma company • $28 billion in sales in 2012 • $4 billion in R&D across three continents Goals • Efficient design of new clinical studies • Quick access to all of the data • Improved evidence based decision-making • Strengthen the knowledge feedback loop • Enable predictive science Challenges • Over 7,000 studies and 23,000 documents are difficult to obtain • Searches returning 1,000 – 10,000 results • Document repositories not designed for reuse • Tedious process to arrive at evidence based decisions Feb 2016Choosing the Right Graph Database to Succeed in Your Project
- 14. Ontotext and Financial Times 14 • Goals − Create a horizontal platform for both data and content based on semantics and serve all functionality through it • Challenges − Critical part of FT.COM − GraphDB used not only for data, but for content storage as well − Personalized recommendation based on user behavior and semantic context (Related Reads) Feb 2016Choosing the Right Graph Database to Succeed in Your Project
- 15. Ontotext and EuroMoney 15 • Goals − Create a horizontal platform to serve 100 different publications − Platform which would include the latest authoring, storing, and display technologies including, semantic annotation, search and a triple store repository • Challenges − Multiple domains covered − Sophisticated content analytics including relation, template and scenario extraction Feb 2016Choosing the Right Graph Database to Succeed in Your Project
- 16. LinkedLifeData – Knowledge Graph 16Feb 2016Choosing the Right Graph Database to Succeed in Your Project
- 17. Graph Database Landscape “Despite all of this attention the market is dominated by Neo4J and Ontotext (GraphDB), which are graph and RDF database providers respectively. These are the longest established vendors in this space (both founded in 2000) so they have a longevity and experience that other suppliers cannot yet match. How long this will remain the case remains to be seen.” Bloor Group report Graph Databases, April 2015 http://www.bloorresearch.com/technology/graph-databases/ 17Feb 2016Choosing the Right Graph Database to Succeed in Your Project
- 18. Graph Database Landscape “Linking a few data sources is often simple, but to do so with significant amounts of heterogeneous data requires a radically new approach. Graph databases are a powerful optimized technology that link billions of pieces of connected data to help create new sources of value for customers and increase operational agility for customer service. […] they are well-suited for scenarios in which relationships are important.” Forrester report Market Overview: Graph Databases, May 2015 https://www.forrester.com/Market+Overview+Graph+Databases/fulltext/-/E-RES121473 18Feb 2016Choosing the Right Graph Database to Succeed in Your Project
- 19. Graph Database Landscape “What’s different in a graph store from a database perspective is the sheer volume of connections, or relationships—how people, places, and things relate to one another through those interactions. If your data is rich, you’ll see lots of relationships between the entities in native graph form. Older database technologies place less emphasis on relationships, resulting in less context. Graphs offer the chance for richer context through more connections and any- to-any data models rather than the usual tabular or hierarchical models” PwC report The promise of graph databases in public health, June 2015 http://www.pwc.com/us/en/technology-forecast/2015/remapping-database-landscape.html 19Feb 2016Choosing the Right Graph Database to Succeed in Your Project
- 20. Presentation Outline • Use Cases for Graph Databases • GraphDB by Ontotext • Choosing a Database for Your Project • Q & A 20Feb 2016Choosing the Right Graph Database to Succeed in Your Project
- 21. GraphDB by Ontotext • High performance semantic graph database, 10s of billions of triples • Full compliance to W3C standards • Various inference profiles, including custom rules • Extensions −Geo-spatial, RDF Rank, full-text search, Blueprints/Gremlin, 3rd party plugins • Tooling for DBAs 21Feb 2016Choosing the Right Graph Database to Succeed in Your Project
- 22. Advanced Features • Connectors to Solr, Elasticsearch, MongoDB* • Consistency checks • RDF Rank for graph analytics • Geo-spatial querying • Notifications, plugin architecture for 3rd parties • “Explain plan” • High-availability cluster 22Feb 2016Choosing the Right Graph Database to Succeed in Your Project
- 23. GraphDB Connectors Selective replication Query Processor Graph indexesInternal indexes SPARQL SELECT with or without an embedded Solr / Elasticsearch query Solr / Elasticsearch direct queries Solr / Elasticsearch GraphDB engine SPARQL INSERT/DELETE 23Feb 2016Choosing the Right Graph Database to Succeed in Your Project
- 24. High-Availability (Replication) Cluster • Improved resilience & query performance • Worker nodes can be added/removed dynamically • “Graceful degradation” of cluster performance when one or more worker nodes fail • Flexible topologies, multi-DC deployment 24Feb 2016Choosing the Right Graph Database to Succeed in Your Project
- 25. GraphDB Editions • Free (+ AWS Marketplace) • Standard (+ AWS Marketplace) • Enterprise • Database-as-a-Service 25Feb 2016Feb 2016Choosing the Right Graph Database to Succeed in Your Project
- 26. Ontotext GraphDB 26Feb 2016Choosing the Right Graph Database to Succeed in Your Project + Java based, deploy anywhere + Maven artefacts + Docker images
- 27. GraphDB on the AWS Marketplace • “1-Click” purchasing • Variety of hardware configurations • Manage big RDF graph data • Pay-per-hour pricing, 5-day trial 27Nov 2015Feb 2016Choosing the Right Graph Database to Succeed in Your Project
- 28. Fully Managed Database-as-a-Service • Low-cost DBaaS for Ontotext GraphDB • Ideal for small to moderate data & query volumes −database options: 10M (free), 50M, 250M & 1B triples • Instantly deploy new databases when needed −Easily scale up / down as data volume changes • Zero administration −automated operations, maintenance & upgrades • Faster experimentation & prototyping, reduced TCO 28Feb 2016Choosing the Right Graph Database to Succeed in Your Project
- 29. Fully Managed Database-as-a-Service 29Feb 2016Choosing the Right Graph Database to Succeed in Your Project
- 30. Ontotext GraphDB – Key Advantages 1. High availability cluster 2. Performance & scalability 3. Advanced features & extensions 4. Variety of deployment options 5. Developed by an established vendor 6. Full lifecycle support – data modelling, integration, deployment 7. Proven in high-profile business critical use cases 30Feb 2016Choosing the Right Graph Database to Succeed in Your Project
- 31. Presentation Outline • Use Cases for Graph Databases • GraphDB by Ontotext • Choosing a Database for Your Project • Q & A 31Feb 2016Choosing the Right Graph Database to Succeed in Your Project
- 32. From Experimentation to Production • Priorities: cost, ease of deployment, performance, availability • GraphDB options: Free, Standard, Enterprise (HA) • Deployment: on premise, AWS cloud, database-as-a-service • Seamless upgrade paths −all options based on the same engine 32Feb 2016Choosing the Right Graph Database to Succeed in Your Project Learning Prototype Pilot Production
- 33. Learning • Priorities −Free −Easy & quick to set up, “sandbox” environment • Recommended −Database-as-a-Service (free 10M triples) −GraphDB Free (on premise / on AWS) 33Feb 2016Choosing the Right Graph Database to Succeed in Your Project Learning Prototype Pilot Production
- 34. Prototype • Priorities −Free / low-cost −Easy & quick to set up, “sandbox” environment • Recommended −GraphDB Free (on premise / on AWS) −Database-as-a-Service (10M – 50M triples) 34Feb 2016Choosing the Right Graph Database to Succeed in Your Project Learning Prototype Pilot Production
- 35. Pilot • Priorities − Low-cost − Performance & scalability • Recommended − GraphDB Standard (on premise / on AWS) • Also consider − Database-as-a-Service (250M – 1B triples) − GraphDB Free (on premise / on AWS) 35Feb 2016Choosing the Right Graph Database to Succeed in Your Project Learning Prototype Pilot Production
- 36. Production • Priorities − Performance & scalability − High availability • Recommended − GraphDB Enterprise • Also consider − GraphDB Standard (on premise / on AWS) 36Feb 2016Choosing the Right Graph Database to Succeed in Your Project Learning Prototype Pilot Production
- 37. Key Takeaways • Graph databases are well suited for interconnected data, heterogeneous data integration, relationship-centric analytics & discovery, schema evolution • Use cases include network analysis, MDM, knowledge graphs, metadata management, recommendations, … • Ontotext GraphDB is an enterprise-grade semantic graph database, proven in mission-critical scenarios • Various GraphDB deployment options, optimal for learning, prototyping & experimentation, production 37Feb 2016Choosing the Right Graph Database to Succeed in Your Project
- 38. Links • Ontotext GraphDB −http://ontotext.com/products/graphdb/ −http://graphdb.ontotext.com/ −@OntotextGraphDB • Customers & Verticals −http://ontotext.com/company/customers/ −http://ontotext.com/knowledge-hub/case-studies/ 38Feb 2016Choosing the Right Graph Database to Succeed in Your Project
- 39. Choosing the Right Graph Database to Succeed in Your Project Thank You!