Graph databases are now clearly riding the upward trend toward mainstream adoption for which the sector has been waiting for several years. Much like the cloud in its early period from 1998 (they were called ASPs--application service providers--back then) to 2012, the speedy search database is picking up buyer after buyer when they try it out and come away impressed.
In naming graph DBs one of the 10 biggest data and analytics trends of 2019, Gartner predicted that the category will grow a whopping 100 percent annually through 2022 “due to the need to ask complex questions across complex data, which is not always practical or even possible at scale using SQL queries.”
Believe the prognosticators or don’t believe them, the movement is in fact here, and the DBs are being sold.
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A good data point here is that graph databases are being used in multiple industries, including financial services, pharmaceutical, health care, telecom, retail and government. Most often they are utilized to explore relationships across massive data silos and achieve the holy grail of webscale analytics in real time. These applications include fraud and money-laundering detection, security analytics, personalized recommendation engines, artificial intelligence and machine learning.
Graph Represents the Real World
As Noel Gomez, data sciences leader at biopharmaceutical giant Amgen, said: “The value in graph is that you can represent the real world, represent objects that you can relate to easily.”
“A knock against graph used to be that it had hit a wall in terms of performance and analytics capabilities when the data volume grew too big and the answers were needed in real time,” TigerGraph executive Gaurav Deshpande told eWEEK. “But the technology has evolved to tackle the toughest data challenges in real time, regardless of how large or complex the data set.”
To get a snapshot on current thinking about graph’s future, seven industry experts were asked the following question: “Graph Is dominating the database market; where will it be in 2022?”
“Graph databases need to be all about deep link analytics. That’s because the more links you can traverse––what’s known as a hop––the greater the insight,” Deshpande said. “But the difficulty in scaling the computational requirements for large datasets has made it difficult to do deep link analytics, which requires going more than three hops deep into the dataset.
“But the technology has advanced. Thanks to improvements, such as faster data loading to build graphs quickly, faster execution of parallel graph algorithms and the ability to scale up and out for distributed applications, graph is meeting or even exceeding its promise.”
Ely Turkenitz, IS Manager for Santa Clara County in northern California, said “graph databases will not replace the Oracle, SQL Server, MySQLs of the world; it’s not a replacement, it’s an addition. It’s for a different use case. Specifically for government, knowing your customer, the 360 angle and the fraud detection.”
Tony Baer, dbInsight:
“Today, enterprises are beginning to understand what a graph database is. By 2022, I expect that the graph databases will become more accessible thanks to the cloud and automated tools that help optimize how the data is modeled and distributed. But the broadest impact of graph will be invisible, as the embedded database behind applications that involve connected people, processes, and/or things. Graph databases will graduate from powering early adopter use cases for digital communities to the mainstream of enterprise and consumer applications.”
Daniel Gutierrez, InsideBigData:
“Graph databases are rising in popularity because they represent an ideal solution for storing data and connecting relationships between data much more effectively than traditional relational databases. The expansion of enterprise applications needing to manage connected data is the primary factor driving the growth of the global graph database market. The graph database ecosystem is innovating rapidly. By 2022, graph databases should be firmly deployed by many prominent industries."
Tom Smith, Research Analyst at Devada, writer for DZone:
"In 2022, companies will be ingesting streaming data from infinite social, IoT, and retail sources into a fully-integrated data fabric of databases from which robotic process automation (RPA) will be automatically generating dashboards and reports and artificial intelligence (AI) will be producing unforeseen insights to inform and improve business operations, patient outcomes, and customer experience--both B2B and B2C."
Aaron Zornes, MDM Institute:
“Master data management (MDM) platforms are evolving to meet the dictates of the evolving digital economy. Today, the 'more modern' MDM platform incorporates graph technology, infuses insights from the data using advanced analytics and ML, and offers big data scale performance in the cloud. The challenges of employing graph tech (UI, query, DB) mandates a focus on update speed and scalability. Simply put, first-generation graph DBs do not provide the OLTP-like update speed and scalability required for enterprises to renovate/replace their RDBMS-based legacy MDM infrastructure.
“Moreover, the digital economy mandates a connected customer experience (e.g., blended, multi-channel), compliance (e.g., fraud detection), and business alignment. Again, first-gen graph DBs do not meet the requirement that deeper than 2-3 node hopping for analytics be high speed, not batch. The good news is that market-leading businesses and disruptive challengers are both forcing this issue with MDM solution providers--with the end result that both start-ups and mega-vendors are paying attention to these requirements, albeit with the mega vendors too often cobbling together a graph layer as an interim solution.”
Jeff Kagen, wireless analyst:
“The latest graph databases are better suited than traditional databases to solving some top-of-mind challenges for the telecommunications industry. Detecting and preventing fraud is at the top of the list of things networks must control. The threat grows every day, so network security must be able to learn how to identify weak links in the system, of which there are usually many. AI and machine learning are becoming the go-to methods for telcos to stay ahead of the next wave of threats. In fact, AI and machine learning may be the only solution to adequately handle the problem. Graph databases are proving a powerful tool in enabling the analytical techniques that AI and machine learning rely on.”
George Anadiotis, Linked Data Orchestration/ZDNet:
“2018 was the Year of the Graph, the year graph databases went mainstream. I have no reason to think this will change, it will only accelerate. To quote Accenture CTO Applied AI Jean-Luc Chatelain: 'Knowledge Graphs are the new black, seeing, for example, Microsoft making graph a centerpiece of its strategy and messaging, or Salesforce doing graph R&D for Einstein, we can expect this to trickle down to early and late majority adopters by 2022.'
“Graph databases are a natural fit for working with knowledge graphs. Although diversity is a strength and has been natural for an innovating field, standardization will help graph database adoption immensely. Bridging different technical approaches and cultures is challenging, but the signs we've seen in the W3C standardization efforts are encouraging. I expect to continue to see innovation coming from graph databases, leading developments in data management, and graph database adoption expanding.”
Based on what we heard from these experts, there are compelling reasons for graph’s upswing. Three years is a long time in this industry. It will be fascinating to watch where all this goes during that span of time.
This article was updated from an original post in July 2019.