Item: ArangoDB
Rating: 8
Author: Pavan sreevatsav Akula

Overall Satisfaction with ArangoDB

Use Cases and Deployment Scope

AQL query language is very simple and efficient in use if anyone using SQL can quickly learn AQL Language.

Developers can easily map the database and can access various patterns like search, ranking.

JSON and semantic search is the latest and next-generation technology to implement to access and extract large datasets.

Pros and Cons

Pros

AQL query language is big plus for ArangoDB
It can be implemented cloud as well as on-prem
Search Engine is a very good option for ArangoDB

Cons

By providing the free services for few months will be help understand for beginners
Enhancing features in dashboard and can make UI more user-friendly
Should conduct more surveys and adv to improve scalability

Most Important Features

AQL Query Language
Multi-Model Powered Machine Learning
Cloud – On-Prem – Anywhere

Return on Investment

It is very powerful tool and should adv more to improve sales
Should conduct more free trails and trainings
Open source and runs everywhere

Alternatives Considered

MongoDB, Google Cloud AI and AWS CloudTrail

It uses AQL query Language, which is different from other Databases.
It has flexibility to integrate in cloud, on-prem anywhere

Key Insights

Do you think ArangoDB delivers good value for the price?

Yes

Are you happy with ArangoDB's feature set?

Yes

Did ArangoDB live up to sales and marketing promises?

I wasn't involved with the selection/purchase process

Did implementation of ArangoDB go as expected?

Yes

Would you buy ArangoDB again?

Yes

Other Software Used

MongoDB, Google Cloud AI, AWS CloudTrail

Use cases provided by default are good and can be improve better using Machine Learning and AI.

AQL query language is very simple and efficient in use if anyone using SQL can quickly learn AQL Language.