Apache Druid vs. Arango (ArangoDB)

It is extremely well suited to rapid ingest of data from large data sources, due to the fact that you can restrict what is ingested by column/field, so that you only pull in the data you actually want or need.

As stated earlier, the open source version could use better cluster management tools, and troubleshooting tools for failing jobs/tasks.

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.

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.

Incentivized

• Rapid ingest
• Limiting ingest to only the relevant fields/columns
• Easy ingest spec creation

• 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

Incentivized

• Security configuration is problematic
• Cluster management could have more features
• Troubleshooting incomplete tasks/jobs is a chore

• 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

Incentivized

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

Incentivized

• Integration with S3 storage has saved about 35% on our storage, over HDFS
• The rapid ingest has saved user's time in the query aspects of their applications.
• The ability to ingest from a variety of data sources has made overall user application queries much simpler

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

Incentivized