Analysis and news
In praise of graph databases Neo Technology’s Emil Eifremlooks at how researchers can generate insights from vast datasets with the fast-emerging SQL alternative
Big data means that we are no longer talking in megabytes or gigabytes when it comes to information, but petabytes and exabytes. Its debut also means we need to stop assuming as researchers that all we have to work on is structured information. As a result, researchers everywhere –
from science to business to government – are collecting vast streams of data about anything and everything, but often without much consideration on how it will be managed, analysed or stored. Traditional methods can struggle here, especially around the unstructured aspect of this tsunami of bytes. A possible aid is emerging, in the shape of something first used by social web giants Google, LinkedIn and Facebook. It’s an approach based on the insight that in data it’s the relationships that are all important and of interest to the researcher – an approach called graph databases. It’s a technology whose power was recently exemplified by what’s been hailed as the best example of data driven investigative journalism – the ‘Panama Papers’.
Limitations of SQL-based tools The Panama Papers is the biggest data- based investigation ever conducted, far larger than anything from Wikileaks or Snowden, at 2.6 terabytes and 11.5 million documents. The ICIJ, the group that coordinated the world’s media on investigating this data, says that it didn’t even know what it had on its hands until it started to look at graph databases as a way to work with it (‘It wasn’t until we picked up graph that we started to really grasp the potential of the data’).
Mar Cabra, the head of its data unit, has outlined how The Times of London found a story about the actress Emma Watson and the Panama Papers, based on unpicking hidden connections that manually would have taken an inordinate amount of time. Graph databases not only handle vast
datasets, but are uniquely able to uncover patterns that are difficult to detect using traditional representations such as SQL-based rdbms or other approaches. Indeed, when the International Consortium of Investigative Journalists (ICIJ) investigated its first big offshore financial story in 2012, links had to be drawn out by hand – drawing lines to spot relationships, in simple Word documents. SQL would struggle here, since the
high-volume, highly linked dataset the Panama Papers data set instantiates is too hard to parse. Relational databases model the world as a set of tables and columns, carrying out complex joins and self-joins when the dataset becomes more inter-related. Such queries are technically challenging to construct and expensive to run and making them work in synchronicity
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is not easy, with performance faltering as the dataset size increases. In addition, the relational data model doesn’t match our mental visualisation of the application (technically defined as ‘object-relational impedance mismatch’). If you think about it, you’ll see why: as people, we draw connections between data elements, creating an intuitive model on whiteboards. Attempting to take a data model based on relationships and forcing it into a tabular framework – the way a data platform like Oracle asks us to – creates a mental disconnect. By contrast, the power of graph database technology is in discovering relationships between data points and understanding them at huge scale. That’s why graph databases are ideal at allowing the researcher to uncover hidden patterns in large data sets, which are difficult to detect using traditional representations.
Life science use cases of graph are emerging every day It’s not just journalists who are finding this out. Tim Williamson, a data scientist
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