ClickHouse vs. MonetDB

ClickHouse delivers exceptional speed and performance, positioning it as the top choice for managing large-scale analytical workloads. With a bunch of built-in functions, it empowers analysts to extract maximum insights from data effortlessly. If your scenario is to deal with analytical questions, then ClickHouse is for you, but if you are looking into a transactional database, that's not the case; even their table engines are not made for this.

Incentivized

I think for what we use it for, mainly scheduling, forecast and to compare against payroll, it works well. I think there are some other things that could be added to it to use, like more expansive ways to use the forecasting tool and an easier way to pull previous data from prior years. This would help making the forecast for scheduling in the future.

Incentivized

• Their MergeTree table engine provide impressive performance for data insert in bulk
• Not only data insert but also the way MergeTree engine uses Primary Keys to sort the data and perform data skipping based on the granules its also their secret for ridiculous fast queries
• Data compression its also great
• They provide especial table engines that allow you to read data directly from other sources like S3
• Since its written with C++ you have very granular data types and especial ones like enum, LowCardinality and etc, they save you a lot of storage since are stored as integer values
• ClickHouse functions besides the ones that respect ANSI Standards are also awesome and useful

Incentivized

• It is easy to use.
• You are able to input lots of data and it understands and populates information.
• Able to change settings on the fly to use with your needs.

Incentivized

• Avro data manipulation
• Kafka consistency
• DDL operations errors (by replica configuration)

Incentivized

• This is an open source software so there are obvious drawbacks, the biggest of which is a lack of documentation.
• MonetDB does not seem to be well known outside of the academic environment so there is less information when you are searching for answers of any type.
• I'd like to see more use cases and/or best practices so that commercial companies like ours can optimally use all of its highly performant features.
• The code is written in C/C++ and this can be negative if you are a mainly java-shop and need UDF - User Defined Function.

Incentivized

ClickHouse was not compared to them as a competitor but as the ideal partner to complete an information analysis system, providing users with the most complete and efficient tools. Therefore, in this case it was considered that it would be the ideal candidate due to its characteristics compared to the other competitors.

Incentivized

There is a plethora of choices when it comes to NoSQL and columnar based databases. We use not one but sometimes 2 or 3 of them to carry out a specific purpose. We chose MonetDB because our engineering team enjoys working with open source software and appreciates its simplicity although becoming familiar with it did take time. I would not deploy MonetDB to production but it's a great backup option.

Incentivized

• Queries that used to take more than 2 minutes now take less than 1 second
• Possibility to analyze use cases in real time (before was impossible)
• The applications are more complete and the users decisions are better

Incentivized

• If you are familiar with a general database concept and played with open source products before then MonetDB will give you immediate return in terms of productivity since developers can quickly develop and verify their test cases involving back-end database with a large sample data set.
• There is a stiff learning curve due to lack of documentation and sparse information available on the internet.
• Overall experience has been positive since MonetDB gives you another option when it comes to building out a data warehouse.

Incentivized