Many companies are still waiting for the market to pop, all the thirty or so vendors. What are some of the hits that make graph databases so cool, ...
What’s advancing the world of graph databases, and what’s slowing us down?
In my relatively short two years in the graph database world, I’ve met people who have spent decades hoping that the world sees value in the analytics, semantics, and data integration that graph databases offer. Many companies are still waiting for the market to pop, all the thirty or so vendors. What are some of the hits that make graph databases so cool, and what are the misses that are keeping graph databases niche?
Here is my take on the top HITS and MISSES of the graph database world, covering a little bit about investors, technology, standards, and marketing.
Hit: Investor Interest in Data
We can learn a lot about the graph database market’s capability to woo investors by looking at Snowflake. Although clearly NOT a graph database, it makes for a good study. In its IPO, Snowflake raised money at a $12.4 billion valuation. Revenue in the first half of 2020 more than doubled to $242 million. Through this fantastic IPO, the company was still losing $171.3 million. Adoption is strong, with over 3000 customers on the platform.
Snowflake is reasonable, but not necessarily amazing technology. Sure, the analytical engine underneath Snowflake can automatically scale on the cloud, but it is not as configurable, agile, and fast for certain types of analytics as say Vertica, Redshift, or even Teradata. It’s a comparatively young platform, and believe me, it takes years to build out optimizations and periphery use-case analytics. Backup, workload management, encryption, security aren’t easy to build, but necessary to sell into corporate IT. Frankly, the more mature vendors have had time to build them out.
Is enthusiasm is overdone? Maybe not. Much of Snowflake’s valuation comes from cool branding and marketing, and an outstanding user experience that drives an amazingly fast adoption pace. Compared to Snowflake, the notion of business analysts having to go through an IT process to spin up servers, even cloud ones, and then wait for IT to install the right software is inferior. In the SaaS model it provides, it’s a few clicks, and I’m ready to load my data. It simplifies the entire analytics creation process, from data load to analytics.
In this newsletter, see different material on graph databases including a key graph database technology, cutting edge research, and more!Parts of the world are still in lockdown, while others are returning to some semblance of normalcy.
In this newsletter, see different material on graph databases including a key graph database technology, cutting edge research, and more! Parts of the world are still in lockdown, while others are returning to some semblance of normalcy.
In computing, a graph database (GDB) is a database which utilises graph structures for semantic queries with nodes, edges, and properties to represent and store data. The graph related data items in the store to a collection of nodes and edges, where edges are representing the relationships across the nodes. Graph databases are a kind of NoSQL database, built to address the limitations of relational databases.
See how graph databases can offer data modeling and analysis capabilities your business can leverage to model real-world systems and answer challenging questions.
In this article, take a look at data migration from JanusGraph to Nebula Graph. Speaking of graph data processing, we have had experience in using various graph databases. In the beginning, we used the stand-alone edition of AgensGraph. Later, due to its performance limitations, we switched to JanusGraph, a distributed graph database.