Are Graph Databases the Next Big Thing for Big Data Analytics?

Gartner predicts a 100% year-on-year growth in the graph database segment through 2022. This article discusses the reasons behind this trend, its implications, and possibilities for your organization. 

Graph databases transform how we mobilize and utilize data, with exponential gains for Big data applications. Before we explore these advantages, let us understand the concept in more detail. 

What Is a Graph Database

A graph database is defined as a database that places equal importance on the data and the relationship between datasets, employing nodes, edges, and properties for data storage and representation so that you can use graph structures for data querying. 

In other words, a graph database is purpose-built for exploring the relationships within the information that it contains and not only highlight individual information pieces. While graph database technology has been around for a while in some form or the other, it’s only recently that it has become scalable. Navigational databases of the ‘60s followed a hierarchical model similar to graph databases today, but it was limited to small datasets due to storage constraints. Now, thanks to advancements in cloud computing, big data, and an increase in demand, graph databases are available commercially and can be scaled horizontally. 

While graph databases are yet to mature (there is no global standard language like SQL for relational databases), there is enormous potential. In 2018, AWS released its Amazon Neptune product, making the technology available to businesses worldwide. There’s also a growing segment of graph analytics specialists like Dgraph providing cloud-based, open-source options for greater accessibility. 

Learn More: Can Semantic Graph Databases Keep Data Lakes from Becoming Data Swamps? 

4 Reasons to Choose Graph Over Relational Databases for Big Data Analytics 

Graph databases offer an alternative way to structure, query, and approach data for analysis – but does this mean that’s inherently better than traditional relational databases we have been using for years? Research suggests that the answer is a resounding YES. Particularly for massive and semi/unstructured databases (i.e., Big Data), graph databases give you a significant advantage.

1. It is challenging to represent semi-structured or unstructured data using relational databases

In a relational database, the database schema is fixed using indexes that help to classify and organize information into a searchable table. This makes it unsuitable for unstructured and semi-structured data types – say, social media. Big Data like social media information is constantly updated with new and incoming data streams, impossible for a fixed schema to reconcile. On the other hand, in a graph database, the information is stored in the form of nodes and edges so you can keep on adding and extending it over time without disrupting the existing structure.

2. Data searching is several times faster in graph databases

This is the primary difference between graph and relational databases. In a relational database, a search query is matched with the indexed information of a dataset. It goes through the entire repository to find out which datasets carry the matching information. A graph database, on the other hand, finds one database that matches the search query and uses existing mapped relationships to find other matches without having to go through the entire repository. This allows you to find results in a fraction of the original time.

3. Graph databases get incrementally more efficient as you scale

Search time decreases as your database grows in a graph database because it offers more relationships to help optimize the querying process. In terms of storage, too, the flowchart-like architecture of a graph database with direct relationships reduces duplication and shrinks your storage size as you scale. According to researchOpens a new window , if a 0.32 MB database grows to 305.47 MB due to the addition of new information, a graph database will increase from 1.08 MB to 291.26 MB under similar conditions. This translates to exponential efficiency gains as you scale your Big Data capabilities.

4. You will be able to eliminate a significant portion of your backend code

Due to the inherently efficient nature of a graph database, you can remove most of the relational database code typically required and still achieve the same analytics results. When Oracle wrote down the code to look up all actors who appeared in the Iron Man movie, the code snippet for graph databases (written in Property Graph Query Language or PGQL) was about a third of the length of the SQL query and significantly simpler. 

Source: Oracle

This is why graph analytics providers like Dgraph promise to eliminate your backend code by up to 90%. 

Learn More: Advanced Graph Analytics: Here’s How To Fight Financial Fraud in 2021 and Beyond

What Does This Mean in Terms of Architecture, Storage, Visualization, and Performance?

Graph databases imply a vastly different way of doing analytics from relational databases. If you conceptualize a relational database as a highly structured table with indexes corresponding to certain fields, a graph database is like a flowchart. A dataset’s fields or properties exist in the form of nodes and edges that can be added to, removed or otherwise altered. The relationships between nodes and edges across the repository are closely mapped and explicitly mentioned, making it easier to search for related information when conducting predictive analytics or AI-driven forecasts. 

Before the 2000s, the above architecture would require more space – but sophisticated graph analytics technologies optimize storage as you scale. 

Also, graph databases are better suited for visualization, while relational databases are meant for textual display. Due to the clearly mapped relationships in the former, it is easier to represent visually via flowcharts. In the case of relational databases, you would have to first identify patterns (i.e., apply some analytics) before visualizing. 

All of this has one major implication: better performance. The graph database architecture, storage efficiency, and visualization readiness make it a prime candidate for compute-intensive Big Data analytics. You can control storage costs, improve execution time (both for data insertion and data search), and prepare for information visualization without restructuring or further data processing. For this reason, GartnerOpens a new window predicts that graph database usage will grow at 100% every year through 2022 in a bid to support complex data science and Big Data use cases. 

Learn More: Top 5 Common Strategies for Database Compliance in 2021  

Exploring Promising Business Applications

The use of graph databases for real-world outcomes is on the rise. This year, NASA announced that it would be using graph technology to build a talent database, highlighting how skills, individuals, and projects are related – identifying the DNA of occupation to hire better. Netflix has been using a domain graph service (DGS) for a while for implementing GraphQL, and it has now made DGS available on the open-source. 

Finally, last year, we saw the seminal COVID graph project where data from 130,000+ publications, case studies, molecular data repositories, and more were arranged using a graph database structure for faster querying and outcomes. 

The possibilities are endless. Any use case requiring high volume data processing and consistent predictions can gain from graph database technology. It’s only a matter of time until a global standard language is determined, a support community develops, and any security issues are addressed to drive widespread adoption. 

What are your thoughts on the rise of graph database technology? Comment with your thoughts below or tell us on LinkedInOpens a new window , TwitterOpens a new window , or FacebookOpens a new window . We would love to hear from you!