GraphTech Ecosystem Overview: Graph Database Landscape and Storage Options (2019)
This article surveys the 2019 GraphTech ecosystem, detailing the rapid growth of graph databases, market drivers, ecosystem layers, and the variety of native and multi‑model storage systems that support graph‑structured data.
This article is the first part of a three‑part series on the GraphTech ecosystem (as of 2019), focusing on the graph database environment.
The graph database market has seen strong, steady interest since 2013, with market share and vendor numbers rising sharply; forecasts predict revenues growing from $39 million in 2017 to between $445 million and $2.4 billion by 2023‑2024. Key growth drivers include the need for faster performance to uncover data relationships, limitations of real‑time multidimensional processing, the rise of graph‑based AI/ML tools, and urgent requirements in fraud detection, financial crime, and security.
The ecosystem is divided into three primary layers, with the first layer—graph database management systems (GDBMS)—acting as the core drivers that help organizations store complex connected data and extract insights from massive datasets.
Although network models have existed since the 1960s, graph structures remained largely academic until the early 2000s when ACID‑compliant graph databases emerged, making graph databases a viable business solution for addressing relational database shortcomings.
Storage solutions fall into two categories: native graph systems (e.g., Neo4j, JanusGraph, DGraph, Stardog, TigerGraph, InfiniteGraph, Sparksee, HypergraphDB) and multi‑model or hybrid databases that support graph alongside other models (e.g., ArangoDB, Azure Cosmos DB, DataStax Enterprise, MarkLogic). These systems enable handling of diverse data types—documents, key/value, RDF triples, and graphs—within a single store, reducing the complexity of managing siloed databases.
Traditional relational vendors have also added graph capabilities: IBM introduced DB2‑RDF in 2012, Oracle rebranded its graph options as Oracle Spatial & Graph, and SAP HANA launched SAP HANA Graph in 2016, extending their relational DBMS with graph functionality.
The article concludes by listing and demonstrating the majority of storage systems used for graph data.
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