Apache Druid vs. kdb+

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.

kdb is well suited for real time tick data and time series analytics.

Incentivized

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

• Time series analysis. The built-in vector operations are extremely fast. Also with the q language you can code up any customized analytical ideas quickly.
• The database are all file based, very easy to maintain.
• Very solid and fast interface to websocket, so you can interface with javascript easily.

Incentivized

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

• Run time error message readability, particularly for new users.
• Backwards compatibility between versions.

Incentivized

switching costs

Incentivized

its ok

Incentivized

We don't use it.

Incentivized

Python is very commonly used for large data analysis and in general is much easier to pickup than kdb+. The biggest drawback of kdb+ is the learning curve.

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 perfectly solves most of our real time tick data needs.
• Finding good kdb resources is slightly difficult. Also new people trying to learn kdb experience a relatively longer learning curve.

Incentivized