Cassandra excels in a broad range of applications -- especially if you understand its data model and write your applications accordingly. It's an excellent choice for time-series data, and a poor choice for application queues. It performs the best if you can simply record history and compute from it, rather than going back and editing or deleting things a lot.
Scylla is well suited for high-throughput scenarios where keyed data must be read or written with consistently low latency. It's less appropriate for use cases requiring relational queries, secondary indexes, or more structured data sets.
High Availability - we utilize the data replication features of Cassandra. This enables us to access our data even when several nodes have gone down
Data Locality - our architecture combines Cassandra storage nodes and computation nodes in the same machine. This enables us to utilize data locality and limit expensive network IO to read data.
Elasticity - Cassandra is a shared nothing architecture. Nodes can be added very easily and they discover the network topology. As soon as a node has joined the Cassandra ring, the data is redistributed among the existing nodes and streamed to it automatically.
No Ad-Hoc Queries: Cassandra data storage layer is basically a key-value storage system. This means that you must "model" your data around the queries you want to surface, rather than around the structure of the data itself.
There are no aggregations queries available in Cassandra.
I would recommend Cassandra DB to those who know their use case very well, as well as know how they are going to store and retrieve data. If you need a guarantee in data storage and retrieval, and a DB that can be linearly grown by adding nodes across availability zones and regions, then this is the database you should choose.
Very easy-to-understand syntax--uses CQL (same as Cassandra), which has many similarities to standard SQL. There are some gotchas, however, that must be known during schema development.
Apache Cassandra has the best of both worlds, it is a Java based NoSQL, linearly scalable, best in class tunable performance across different workloads, fault tolerant, distributed, masterless, time series database. We have used both Apache HBase and MongoDB for some use cases which were within hadoop setup and JSON (JavaScript Object Notation) document store respectively, but given the overall factors favoring Apache Cassandra, it is a technology choice for multiple platforms!
Scylla has a quick learning curve (same as Cassandra) compared to other proprietary solutions like BigTable. It supports higher throughput and lower latency that other NoSQL databases like MongoDB, which sacrifice those features for more flexibility and unique features.
The open source version of Cassandra is only suggested for learning the basic concepts and play with its core features. Unless you really want to invest a lot in your developers and architects knowing every detail of Cassandra, I prefer the DataStax enterprise version. Although the license cost is relatively high, I think they it is worth it. I'm thinking about the support, the monitoring tool OpsCenter, and the integration of Solr and Spark (for data analysis).
Cassandra didn't fully replace our old and traditional relation database Oracle. In addition, it opens another door for us to deal with some special business use cases that NoSQL database can do better in a more feasible and efficient way.
