Likelihood to Recommend 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.
Read full review Titan is definitely a good choice, but it has its learning curve. The documentation may lack in places, and you might have to muster answers from different sources and technologies. But at its core, it does the job of storing and querying graph databases really well. Remember that titan itself is not the whole component, but utilizes other technologies like cassandra, gremlin, tinkerpop, etc to do many other things, and each of them has a learning curve. I would recommend titan for a team, but not for a single person. For single developer, go with
Neo4j .
Read full review Pros 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 Read full review Titan is really good for abstraction of underlying infrastructure. You can choose between different storage engine of your choice. Open source, backed by community, and free. Supports tinkerpop stack which is backed by apache. Uses gremlin for query language making the whole query structure standardized and open for extension if another graph database comes along in future. Read full review Cons Avro data manipulation Kafka consistency DDL operations errors (by replica configuration) Read full review The community is lacking deep documentation. I had to spend many nights trying to figure many things on my own. As graph databases will grow popular, I am sure this will be improved. Not enough community support. Even in SO you might not find many questions. Though there are some users in SO who quickly answer graph database questions. Need more support. Would love an official docker image. Read full review Alternatives Considered 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.
Read full review To be honest, titan is not as popular as
Neo4j , though they do the same thing. In my personal opinion, titan has lot of potential, but
Neo4j is easier to use. If the organization is big enough, it might choose titan because of its open source nature, and high scalability, but
Neo4j comes with a lot of enterprise and community support, better query, better documentation, better instructions, and is also backed by leading tech companies. But titan is very strong when you consider standards. Titan follows gremlin and tinkerpop, both of which will be huge in future as more graph database vendors join the market. If things go really well, maybe
Neo4j might have to support gremlin as well.
Read full review Return on Investment 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 Read full review Steep learning curve. Your engineers would have to spend lots of time learning different components before they feel comfortable. Have to plan ahead. Maybe this is the nature of graph databases, but I found it difficult to change my schemas after I had data in production. It is free, so time is the only resource you have to put in titan. Read full review ScreenShots