I have been using TigerGraph for the last two years. The use case for TigerGraph is at quran.com, where we need to connect the mapping verse connections to model each verse as a node and the relationship between the verses, such as shared themes, concepts, and linguistic links. This creates a semantic network of the Quran, and I made a thematic visualization of the Quran for this structure.

First, I needed to define what kind of nodes and relationships our graph would contain using TigerGraph. For the verse, I made it Surah, including Surah number, verse number, Arabic text, and translation. Then I created a theme with the primary ID and the description. I created a unidirectional edge called has_theme from verse to theme, and then I created a unidirectional edge called related_to from verse to verse with relationship type, which could be a string such as same_theme, cities, or explains, and then the weight as a double.

In this schema, each verse is a vertex, each theme is a vertex, and vertices connect to themes through has_theme edges. Verses can also connect directly to other verses through related_to edges where they share semantic connections. I inserted data for verses sharing the same theme. I then wrote a query to find verses by theme using the TigerGraph GSQL query language to retrieve all verses connected to a specific theme. The visualization graph shows two circular nodes representing the verses, labeled such as 2:1, 3, and 3:200, and one center node representing the theme patient. Edges shown as arrows connect each verse to the patient node and also a direct edge between the two verses if the related_to connection was added.

The best features TigerGraph offers include, first of all, the performance and scalability, which is why we started using it. Then there is the modern hybrid engine for AI that moves beyond simple data connection to real on-the-graph RAG for explainable AI, and the developer and enterprise features like powerful query language, low-cost solution, and cloud-native deployment. Additionally, it seamlessly connects with the ultra-fast connectors for common data sources like Snowflake, Databricks, and Kafka, which we are using.

Regarding how TigerGraph has handled large datasets or high query loads in my experience, it demonstrates world record scale with the ability to process graph data containing large dataset sizes. In this context, we can increase throughput by adjusting concurrency settings for loading jobs and modifying the Kafka loader replica number configuration to increase the number of concurrent Kafka loading jobs beyond the default one. With the ultra-fast connector and Kafka integration, we can add the built-in primary TigerGraph Kafka connector, which requires nothing to install as it is based on a trusted Kafka Connect framework. The connector streams from one source data, such as our application stream, into TigerGraph on the internal Kafka cluster. A Kafka loading job ingests the messages into the graph dataset. This design allows for faster, scalable, and concurrent data streams from multiple sources and supports modern data formats.

GSQL is Turing complete and combines SQL-like syntax with procedural programming, supporting accum, post-accum, and order by with traversals. It also supports openCypher and ISO GQL for flexibility across teams. With cloud-native deployments like Savanna, TigerGraph Savanna offers separate storage and compute so you can scale resources independently and pause workspaces for cost savings. It includes automatic failover, high availability, and fine-grained role-based authentication out of the box.

The impact of TigerGraph has been significant. It reduced complex multi-join query times from minutes to milliseconds, enabled real-time fraud detection across billions of transactions, and cut development effort by over seventy percent because the graph traversal logic that previously required thousands of lines of SQL became just a few dozens of lines of GSQL. The ability to visualize connected data in GraphStudio helped business users discover hidden relationships, such as shared account identifiers that had gone undetected for years.

Every organization needs improvements. GraphStudio has UI/UX issues and bugs. The visual interface has several frustrating bugs, for example, graph exploration results sometimes disappear and node selection can be difficult when edges are very close together. Additionally, loading jobs created by GSQL do not appear in GraphStudio at all, forcing users to manage the UI and command line separately. A dark theme is also missing in the new versions.

Regarding query development, the query installation process can be painfully slow, with some users reporting that installed queries get dropped or hang, especially when the system is overloaded. Additionally, there is no way to create or replace a query directly, which complicates iterative development. Large result sets over two gigabytes cannot be paginated, requiring workarounds such as writing to files.

There are some missing modern developer features such as query profiling, and TigerGraph does not support openCypher syntax or interpret queries, and it failed to track create, update, and delete operations. The lack of built-in pagination for large REST API responses remains a pain point for building production applications. Integrating TigerGraph with popular Graph RAG frameworks has been challenging. The light-RAG integration suffers from performance issues due to inefficient per-node operations rather than batch processing. Microsoft's GraphRAG does not abstract the storage layer, so its output can be stored independently.

TigerGraph is stable in my experience.

Regarding scalability, TigerGraph is exceptional as demonstrated by verified benchmarks. The performance scales horizontally while automatic partitioning maintains sub-second performance for deep multiple-hop traversals even as data grows and supports real-time streaming ingestion with over one hundred million updates per machine per hour.

The customer support is better, and whenever I reached out, it was extremely responsive.

Before choosing TigerGraph, we evaluated other options, including Amazon Neptune and Neo4j. Neo4j was the strongest contender given its mature ecosystem, Cypher query language, and strong community support. Neptune was also appealing for its managed and cloud-native architecture. However, TigerGraph's on-premises flexibility and proven performance at petabyte scale ultimately aligned better with our data sovereignty and performance needs, which is why we chose TigerGraph.

I have been working in my field for more than six years. I started with front-end development and then moved towards back-end development and also worked on a couple of mobile apps and other areas such as DevOps, augmented reality, virtual reality, and some AI automations.

The documented ROI is substantial based on a Forrester Total Economic Impact study. We saved nine point six million dollars in increased profits over three years from the new products and services enabled by graph-powered insights.

Regarding pricing and setup cost, I am not much aware of the details because I am handling the technical part of full-stack development, and the accounts team handles these matters. However, I can say that the Community Edition is completely free and supports production usage, and it is perfect for development and proof of concept work. For the cloud-based Savanna offering, free credits are provided to new users for a full year, allowing for thorough evaluation before any financial commitment. Moving forward, it works with a pay-as-you-go model.

Before choosing TigerGraph, we evaluated other options, including Amazon Neptune and Neo4j. Neo4j was the strongest contender given its mature ecosystem, Cypher query language, and strong community support. Neptune was also appealing for its managed and cloud-native architecture. However, TigerGraph's on-premises flexibility and proven performance at petabyte scale ultimately aligned better with our data sovereignty and performance needs, which is why we chose TigerGraph.

Start with the Community Edition and then design your graph schema iteratively. Expect a learning curve with GSQL and test real-world scale early so you can plan your export strategy and engage with the TigerGraph solution team for any enterprise licenses. I would rate this product a nine out of ten.

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.