7 Ways DataChemist Is Different


The world of business data is a broad church. Even within the narrower confines of the graph database ecosystem, there are a number of players capable (to some degree) of mapping connections and relationships between entities and thus providing new perspectives and insight from existing datasets.

But all graph databases are not the same. DataChemist is the fastest and most powerful graph database on the planet, and as a result we deliver more insight at a lower cost than any alternative. Here’s 7 reasons why:

1. Model Driven

At the heart of every DataChemist implementation is a meaningful data model that defines the entities within your dataset and the relationships between them. In other words, we put real-world structure on your data, and our platform actively manages that model to ensure the specific rules governing relationships and attributes are never broken, even as the data evolves over time.

Creating a model in this way ensures your data is understandable. It also means we are able to automatically amend and correct existing data - because the platform knows what it should look like. We can also find and report any instances where real-world rules are broken, which can be a huge help in identifying and rectifying issues in business operations.

That’s not all. By structuring data within a model in this way, we are able to ask better questions, and extract meaningful information in a more efficient matter: the model informs our query language as to how to traverse, utilise and transform the data in question.

2. Faster

Everyone says they’re faster. We are faster - and we’re happy to go toe-to-toe with any competing product on the market. The DataChemist platform has been built from the ground up to ensure the best possible speeds even at huge scale. A compressed in-memory database enables fast query search on vast datasets whilst our parallelized architecture means we can support deep and complex queries without any loss of performance.

3. Deeper

In today’s world many relationships are not obvious. In some cases, they are deliberately concealed. Standard graph databases are only capable of relating two entities across 2 or 3 ‘degrees of separation’ - and in many scenarios that simply isn’t enough.

DataChemist uses sophisticated techniques based on Kripke logic to find connections between two entities across a dozen or more degrees of separation. We can find ownership cycles and chains 10 links long. So if you need to know how closely one business is related to another, we can tell you, no matter who doesn’t want you to know.

4. Flexible Deployment

DataChemist will always be easy to use and easy to deploy. Specifically, we give our customers the power to choose exactly where and how data is handled. Whether in the cloud as a hosted service, in your cloud, or on your own servers in your own premises - it’s up to you.

We also understand that whilst some customers want a dynamic 2-way integration with their existing data systems, others want to provide batch data for offline analysis. DataChemist handles either approach - we don’t dictate how you use the platform and we adapt to your requirements.

5. Temporal Scoping

How things change over time can be of critical importance. So can answering the question “is this true now, and was it ever true?” Unfortunately, answering what sounds like a simple question involves taking millions of time slices across huge, complex dataset and is - effectively - impossible without DataChemist’s unique query language.

Not only can we answer any temporal queries of this type, we are also able to show how relationships changed over time direct from our UI: it’s simply a matter of adjusting the date and seeing connections appear (and disappear) over time.

6. Geo Scoping

In the real world, real entities have real locations: and the patterns we see in those locations matter. DataChemist allows you to see at a glance the physical and geographical reality behind networks, connections and patterns of influence. After all, what can be hidden in tables - or even graph database views, can suddenly become apparent when mapped onto the physical world.

7. Negative Queries

Possibly the hardest questions to answer - to the effect of being impossible until now - are those that involve negative queries. It turns out that finding the non-existence of relationships within large datasets is extremely difficult, so questions such as “confirm there are no situations in which one order is associated with two customers” become impossible to answer.

DataChemist’s unique ability to query at scale can finally help organisations answer these questions. We can identify where connections that should exist do not exist, or where conditions that should be true are not true. Quickly, and at ‘web scale’.

To Conclude…

Unlike our basic graph competitors, DataChemist uses semantics to impose order on data. This semantic layer captures the underlying meaning of the data in terms of the important business objects and the relationships between them. This in turn allows us to quickly re-organise the data by changing the model and gives us explainable AI.

In short, we build enterprise knowledge graphs. Our approach reveals new connections from the data which would otherwise go unseen. It’s the difference between an asset that generates new knowledge and a database just sitting there, waiting to be queried. A knowledge graph allows new insights, which in turn can be used to infer new things about your world.

Written by

Kevin Feeney


Posted

October 24th, 2018

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