TigerGraph Cloud Classic

TigerGraph  serves as a graph database to model accounts and transactions as edges for our company. In our organization, we implement TigerGraph  in an application called Hi HQ. We deal with a large amount of interconnected data, such as customer, transaction, and product relations, so we needed to implement a solution that can effectively analyze highly connected data. We modeled entities like customers, products, and transactions as nodes, and their interactions as edges. Using this, we built graph analytics workflows to traverse the relationships quickly and identify patterns such as suspicious activity and customer behavior trends.

TigerGraph has positively impacted our organization as we needed to deal with a large volume of datasets like customer transactions and product interactions. The goal was to implement a system that can easily and efficiently provide complex relationships in the data and facilitate faster insights. We implemented TigerGraph to model our data as nodes and relations using its analytic capabilities and parallel processing architecture. We built queries that could traverse multiple connections and analyze patterns much faster than before, significantly improving our ability to analyze connected data, reducing query processing time, and enabling faster decision-making. It helps our team uncover hidden relationships in data, improving our operational efficiency and analytical capabilities.

Since we needed to reduce the query execution time in our application, it has reduced it by up to 60%. Data relationship analysis that used to take minutes is now reduced to seconds, and we can process multiple millions of relationships in real-time, which provides significant value.

We have seen a return on investment since query processing has improved to under 30 seconds, and our analytic team's productivity has improved by 30%. The infrastructure cost has reduced as fewer complex queries are now required. Previously, if three people were needed for an analysis, it can now be handled by one member, and the business team receives insights much faster, improving the speed of decision-making.

The best feature of TigerGraph is the interconnectivity, which is very good for our needs as we were looking for highly connected data such as customer transactions. We needed our database to provide solutions for complex relationship queries quickly, and we can scale it with a large dataset. We adopted TigerGraph because it has massively parallel processing, real-time graph analytics, and deep link multi-hop queries.

I find the GSQL query feature to be the most reliable because it is a powerful SQL-like query language designed for graph analytics and complex pattern matching, which is the best aspect of TigerGraph.

Scalability is one of the key factors why we chose TigerGraph, as it provides fast analytics when the dataset increases and meets our needs very well.

TigerGraph can improve on certain factors, particularly the simple query language, as the learning curve can be very hard for new users or beginners. The visualization tools could also be improved.

For new developers, especially those who are freshers, the learning curve for the simple query language should be made easier because it is relatively harder for them to learn without much experience in any tech stack. I have a few team members who are freshers, and it is relatively harder for them to learn this kind of solution.

I have been using TigerGraph for the past 1.5 years.

TigerGraph is very stable.

Scalability is one of the key factors why we chose TigerGraph, as it provides fast analytics when the dataset increases and meets our needs very well.

I have not needed customer support for any tasks, but I think it is good.

We did not use any different solution prior.

TigerGraph is deployed in our organization in a public cloud environment with TigerGraph servers set up in our organization's cloud environment. The deployment involves setting up TigerGraph servers in our organization's cloud environment.

We are just a buyer and do not have any other business relationship with this vendor.

Since we needed to reduce the query execution time in our application, it has reduced it by up to 60%. Data relationship analysis that used to take minutes is now reduced to seconds, and we can process multiple millions of relationships in real-time, which provides significant value.

We have seen a return on investment since query processing has improved to under 30 seconds, and our analytic team's productivity has improved by 30%. The infrastructure cost has reduced as fewer complex queries are now required. Previously, if three people were needed for an analysis, it can now be handled by one member, and the business team receives insights much faster, improving the speed of decision-making.

I do not know much about the setup cost and pricing, as I did not set it up. The initial setup is a little costlier compared to traditional databases, but it is justified for our organization's needs.

Before choosing TigerGraph, we evaluated Amazon Neptune , which is a fully managed graph database service available on AWS  that supports both property graph and RDF model.

If your application or company needs a platform that will grow and handle datasets growing into millions in the near future, and if your company has the budget for TigerGraph, then you should go for it. It may be a little costly, but it ultimately provides very fast analytical capabilities of datasets, which is great. I would rate this product a 9 out of 10.