2018 is a graph-shaped year
This is a guest blogpost by Neo4j’s CEO Emil Eifrem, in which he says graph databases are about to grow up
Graph technology has come a long way: from financial fraud detection in the Panama and Paradise papers to contextual search and information retrieval in NASA’s knowledge graph and its support for true conversational ecommerce in eBay’s ShopBot.
What propels this success is graph’s unique focus on data relationships. And we’ve witnessed the value of connected data explode, as businesses look to drive innovation as they connect supply chain, IoT devices, marketing technology, logistics, payment history, making the value of connectedness across all those data elements increase exponentially.
But only a decade ago, the graph industry was just Neo4j and a few niche players. In the subsequent years other startups made their entrance as part of the NoSQL revolution, while more recently tech giants such as Oracle, Microsoft, SAP and IBM have each produced graph offerings of their own. Today the graph paradigm offers choices – with native platforms, NoSQL multi-model containers and embedded-in-SQL variants.
Amazon’s long-standing absence from this list of tech behemoths was always a notable irony, given that its business models, in both ecommerce and the data centre, are so graph-influenced. So the recent launch of Amazon Neptune is a welcome progression, marking the full acceptance of graph software into the mainstream. Amazon’s entrance should be welcomed by the graph database market, as it will drive the growth generally and contribute to graph technology’s commercial success.
As with all markets, more competition and choice means stronger market and better products. Ultimately, customers will benefit.
Still in the graph database kindergarten
Now that all of the major database players are in the graph game, the next phase of the market’s development will be all about solutions – though it’s evident that we are only at the beginning of this journey. Graph platforms will likely become foundational elements of corporate technology stacks, interweaving different types of data sources, applying comprehensive graph analytics, deploying easy-to-use graph visualisation tools and constructing purpose-built graph-based applications, which will speed widespread adoption.
Creating the graph ‘SQL‘
Second, to achieve widescale adoption, the market needs a standard graph query language analogous to SQL that is simple as well as easy to learn and implement.
I believe Cypher will become this standard, because in addition to years of real-world validation it has by far the widest adoption among actual graph end users.
Cypher is overseen by the openCypher project, whose governance model is open to the community; it now has over 30 participants including academics, vendors and enthusiasts. To date, Cypher is used by Neo4j, SAP HANA, Redis Graph and Splunk, and the project has released Cypher for Apache Spark and Gremlin. Amazon is interesting, having hedged bets on two older languages; its decision here may well have an influence.
The graph community is growing
Finally, along with this commercial success comes a growing interest in graph skills and awareness. The community needs to ensure that every developer, data scientist, data architect and even business analyst is skilled in graph technology.
2017 was a massive year for graphs. More entrants into the graph community means 2018 will be even bigger.