ClickHouse vs. Google Cloud SQL

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.

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Does what it promises well, for instance, as a sidecar for the main enterprise data warehouse. However, I would not recommend using it as the main data warehouse, particularly due to the heavy business logic, as other dedicated tools are more suitable for ensuring scalable operations in terms of change management and multi-developer adjustments.

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• 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

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• It has a easily and user understandable interface which provides it every necessary feature to come up with.
• It's backend is very strong that can help us to run big quieres without any hesitation.
• It's integration with other tools are one of the powerful feature which makes it more suitable to use.

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

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• Increasing support for more database engines may enable a wider range of application needs to be met.
• Implementing and updating cutting-edge security features on a constant basis.
• Streamlining and enhancing the tools for transferring data to Google Cloud SQL from on-premises databases or other cloud providers.

It fits the current needs and bandwith of out lean organization.

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As with other cloud tools, users must learn a new terminology to navigate the various tools and configurations, and understand Google Cloud's configuration structure to perform even the most basic operations. So the learning curve is quite steep, but after a few months, it gets easier to maintain.

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GCP support in general requires a support agreement. For small organizations like us, this is not affordable or reasonable. It would help if Google had a support mechanism for smaller organizations. It was a steep learning curve for us because this was our first entry into the cloud database world. Better documentation also would have helped.

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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.

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Unlike other products, Google Cloud SQL has very flexible features that allow it to be selected for a free trial account so that the product can be analyzed and tested before purchasing it. Integration capabilities with most of the web services tools are easier regarding Google Cloud SQL with its nature and support.

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• 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

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• With managed database system, it has given us near 100% data availability
• It has also improved web layer experience with faster processing and authentication using database fields
• Google Cloud SQL also gels up well with Google Analytics and other analytics systems for us to join up different data points and process them for deeper dives and analysis

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