ClickHouse vs. Panoply

The most important thing when using ClickHouse is to be clear that the scenarios in which you want to use it really are the right ones. Many users think that when a database is very fast for a specific use case, it can be extrapolated to other contexts (most of the time different) in which a previous analysis has not been carried out.

ClickHouse is an analytical database, as such, it should be used for such purposes, where the information is stored correctly, the data volumes are really large and the queries to be performed are not the typical traditional queries on several columns with multiple aggregations. ClickHouse is not the solution for this.

On the other hand, if your case is not one of the above, it is quite possible that ClickHouse can help you. Where ClickHouse shines is when you are looking for aggregation over a particular column in large volumes of data.

Incentivized

Panoply is well suited for companies with large amounts of data that do not want all users of the company to see or view. It is appropriate for companies that have to store data for several years in case clients need the data. It is not made to constantly be downloading or looking at the documents.

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

• Ease of data integrations. Importing data into the data warehouse is a point-and-click effort.
• Automated data modeling. The data is modeled in a way that facilitates analysis without having to look into it myself.
• Query speed. Automated query optimization has really sped up our dashboards.

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

Incentivized

• The user interface could use some improvement for analytical tools. Apart from that, it’s great.

It’s a good choice for building a quick, multipurpose data stack for a variety of businesses needs.

ClickHouse outperforms, especially in costs, since its compression/indexing engines are so smart, and even with very low computing power, you can already perform huge analyses of the data.

Incentivized

Panoply has much more storage and better organization tools for searching. There are better folders and tools for keeping documents secure and stored for a long period of time. It is better to offload data in mass than singular or Zip drive documents like you would do in Google Drive.

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

• Saved time on Redshift setup
• Saved support costs (monetary and temporal) by not having to set up on-premise
• Widens the BI tool use in organization; lowers the barrier to entry and makes organization more productive in analytical terms