The document discusses low-cost open data as-a-service (DBaaS) emphasizing the use of RDF technology to transform and publish data efficiently. It outlines the architecture and requirements for implementing such a service, detailing the benefits of using cloud-native solutions for scalability and cost efficiency. Additionally, it highlights lessons learned and encourages users to adopt an RDF DBaaS for enhanced productivity and easier data management.

1. Low-cost Open Data As-a-Service
Marin Dimitrov, Alex Simov, Yavor Petkov
May 31st, 2015
Low-cost Open Data as-a-Service / SemDev’2015 #1May 2015

2. • Use cases & requirements
• Cloud architecture for a RDF DBaaS
• Lessons learned
Contents
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3. Use Cases & Requirements
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4. Why an RDF DBaaS?
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Grafter Grafterizer
RDF DBaaSOpen Data Portal
• Transform tabular data into RDF
• Publish (Linked) data services,
instead of static datasets
• Lower-cost & easier data
publishing process

5. Why an RDF DBaaS?
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• Transform textual data into RDF
• Linked data services
• Low-cost & easy to use

6. • Elastic
– dynamically adapt to growing data & query volumes
• High availability & resilience
– no SPFs, “graceful degradation” upon failures
• Cost efficient
• Host a large number of data services (databases)
– But probably of low/moderate data & query volume
• Isolation of the multi-tenant databases
DBaaS requirements
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Not easy to
achieve all three!

7. Cloud Architecture
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8. • AWS based
– Network storage, compute & autoscaling, load
balancing, integration services, …
• Ontotext GraphDB as the RDF DB engine
– OpenRDF REST API
• Docker for containerisation
• An RDF DBaaS is…
– A GraphDB instance…
– Running within a Docker container…
– Storing its data on a private NAS volume
DBaaS architecture on AWS
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9. DBaaS architecture on AWS
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Elasticity vs
High Availability vs
Cost Efficiency

10. Dealing with failures
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our responsibility
CSP responsibility

11. • Elastic
– Routing nodes, data nodes + NAS storage grow as usage
grows
• High availability & resilience
– Strategies for dealing with failures in data, routing,
Coordinator nodes
– Planned: multi-DC deployment with replication
• Cost efficient
– Cloud native architecture -> cost savings
– Multi-tenant model -> cost savings
– Elastic: return underutilised or unused resources back
to CSP
Evaluation
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12. Lessons Learned
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13. • Cloud-native architecture
– Improved scalability, reliability, cost savings
• A microservice architecture will continuously
evolve
• Assume that failures will happen on all levels
– Design for “graceful degradation”
• A good DevOps process is essential
Lessons Learned
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14. Discussion
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15. • Use it for free!
– http://s4.ontotext.com (available NOW)
– http://dapaas.eu (end of June)
• Send us questions, comments, criticism,
suggestions for improvements, …
Help us improve it!
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16. • Are you measuring the TCO of your on-premise
RDF databases?
– Important for many Open Data scenarios
• What is your #1 concern for using an RDF DBaaS
• Do you have use cases where your productivity
will increase by using an RDF DBaaS
– Experiment & prototype faster; focus on building apps,
don’t worry about infrastructure; provision new DBs
instantly…
– Real world example: training courses by Ontotext
switching from local deployments to the RDF DBaaS
Discussion topics
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17. Thank you!
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