Does TigerGraph replace anything in my existing architecture?
TigerGraph is additive to your architecture – and is really two technologies for the price of one: a graph database and a relationship analytics engine. Its primary contribution to your business is its unique analytical insight – derived from a combination of its ability to connect your data together and at the same time perform sophisticated analytical queries on that connected data.
It runs on top of your strategic cloud provider, compressing the data it stores – meaning lower storage and compute costs than alternative cloud based approaches. It does not replace your cloud architecture.
It then outputs insight into your strategic AI/ML or data visualisation tools – and it can make that insight available to you in whichever format is most appropriate – whether through the native visualisation UI, or whether in a machine readable output such as CSV or JSON. Again, TigerGraph is additive to your data architecture – and is intended to enhance and not to replace your AI or visualisation stack.
- Compatible with and underpinned by any major cloud server (AWS, GCP, Azure), whether private or public – as well as being available on premise
- Consumes data in multiple input formats – e.g. CSV, JSON – in batch or via realtime API
- Scales horizontally – meaning the user experiences a single database even if underneath it’s spread across multiple servers
- Produces query results in real-time (TigerGraph can query more than the population of Greece every second, per server)
- 100,000 updates to the data per second, per server (equivalent to the population of the Seychelles!)
The Analytics Engine on top of the Database
- Get insight from day one with inbuilt native algorithms such as Community Detection and Classification
- De-duplicate and match data using native entity resolution algorithms even if there is no easy matching ID – enhancing the quality of your data from day one
- Infer information using native algorithms such as Cosine or Jaccard similarity
- Write any query you can possibly think of using our query language, GSQL, which is a fully customisable, Turing-complete query language that is very similar to SQL
- Use queries to print results back into your database – enabling a deep learning, iterative insight mechanism
- Control user access to subsets of data by setting permissions to use specific queries
Note: TigerGraph does not run NLP algorithms to extract structured data from unstructured text, but it will consume the structured outputs of any cloud NLP module that is able to run that extraction process.
Are there any limits I should be aware of?
Types of data. With TigerGraph, there are no limits to the number of types of data you want to include in the graph – whether it’s trial data, medical data, patent data, drug or patient data. All of your data types and how they relate together are mastered in the schema you can create manually within the TigerGraph Graph Studio UI. You can choose to include any new data type at any time by just updating your schema and loading the data.
Scale. TigerGraph is built for scale – and whilst it doesn’t provision cloud servers automatically (because we believe that is a cost and technical design that you should be able to control), it does distribute and partition data across your provisioned servers automatically. This means that the TigerGraph user experience feels like there is only one server – and the user is never asked to do anything more than once, like being asked to create queries or schema for every additional server.
Complexity of analytics. TigerGraph’s analytics engine is built for depth and breadth of query – meaning there is no limit to the number of data types or points you can include in a single query. We know that some of the most important insight comes from combining a significant number of data types and data points together at once – so we built the analytics engine to support you in whatever you need to ask of your data. We also recognise that native algorithms don’t always garner the details of the answer you need – and so we made our query language fully customisable – catering not just to any number of ifs, buts and whens, but also enabling you to print results back into your database as new or overlaid data to be included in future queries.
Speed. It’s important to note that where other technologies purport to be able to support the flexibility, scale and analytical power above, usually it is to the detriment of speed. This is because in effect the technology isn’t built natively to operate like that, and instead there are technical workarounds that make it possible, if given the time to run. By contrast, TigerGraph was built natively for all of the above – which means it really does deliver insight in realtime speeds – and this is of paramount importance for user experience if you’re wanting to investigate your data in an iterative research process during clinical discovery phases.
So how do I get started with TigerGraph?
You can download our free product here if you’d like to get your hands on it straight away. Or you can reach out directly to our sales team here if you’d like to see a demo, and talk about how we could run a proof of concept with you using some of your data.
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4 DCAT Value Chain Insights, Jul 2020, Link
5 DCAT Value Chain Insights, Jul 2020, Link
6 DCAT Value Chain Insights, Jul 2020, Link
7 McKinsey, Jan 2021, Link
8 CBO, Apr 2021, Link
9 PLOSONE journal, May 2015, Link
10 PLOSONE journal, May 2015, Link
11 Semantic Web Conference Paper, May 2020, Link