Apache Cassandra vs. DataStax Enterprise vs. etcd

Apache Cassandra is a NoSQL database and well suited where you need highly available, linearly scalable, tunable consistency and high performance across varying workloads. It has worked well for our use cases, and I shared my experiences to use it effectively at the last Cassandra summit! http://bit.ly/1Ok56TK It is a NoSQL database, finally you can tune it to be strongly consistent and successfully use it as such. However those are not usual patterns, as you negotiate on latency. It works well if you require that. If your use case needs strongly consistent environments with semantics of a relational database or if the use case needs a data warehouse, or if you need NoSQL with ACID transactions, Apache Cassandra may not be the optimum choice.

Incentivized

Real-time transaction processing (both reads and writes) is where DataStax Enterprise shines. It's very fast with linear scalability should more resources be needed. Additional nodes are added very easily. DataStax Enterprise on its own (without Solr or Spark enabled) isn't well suited for long complicated reports. The data model doesn't support joining multiple tables together which is common in BI reporting.

• Continuous availability: as a fully distributed database (no master nodes), we can update nodes with rolling restarts and accommodate minor outages without impacting our customer services.
• Linear scalability: for every unit of compute that you add, you get an equivalent unit of capacity. The same application can scale from a single developer's laptop to a web-scale service with billions of rows in a table.
• Amazing performance: if you design your data model correctly, bearing in mind the queries you need to answer, you can get answers in milliseconds.
• Time-series data: Cassandra excels at recording, processing, and retrieving time-series data. It's a simple matter to version everything and simply record what happens, rather than going back and editing things. Then, you can compute things from the recorded history.

Incentivized

• Datastax Cassandra provides high availability and good performance for a database. It is built on top of open source Apache Cassandra so you can always somewhat understand the internal functioning and why.
• Datastax Cassandra is fairly simple to start using, you can install/setup your cluster and be productive in 1 day.
• Datastax Cassandra provides a lot of good detailed documentation, and when starting, the detailed free videos on the Datastax site and documentation are very helpful.
• Datastax Enterprise Edition of Cassandra provides more tools, good support, and quick response SLA for enterprise business support.

Incentivized

• Cassandra runs on the JVM and therefor may require a lot of GC tuning for read/write intensive applications.
• Requires manual periodic maintenance - for example it is recommended to run a cleanup on a regular basis.
• There are a lot of knobs and buttons to configure the system. For many cases the default configuration will be sufficient, but if its not - you will need significant ramp up on the inner workings of Cassandra in order to effectively tune it.

Incentivized

• Cassandra is a bit difficult to learn and understand
• The costs are slightly higher for our company
• Hardware requirement is moderate to high at the beginning

Incentivized

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.

Incentivized

We will continue to use it because it scales well with commodity hardware and we are satisfied with the documentation and support.

Incentivized

It’s great tool but it can be complicated when it comes administration and maintenance.

Incentivized

There is a bit of a learning curve and tasks that are simple in traditional RDBMS systems can be complicated with DataStax Enterprise but once you get the hang of denormalizing data and getting the data model correct DataStax Enterprise is very usable. Usability from the developer's standpoint is very simple - the complication is on the architecture side with the data model.

Sometimes instead giving straight answer, we ‘re getting transfered to talk professional service.

Incentivized

DataStax has the best community. They have instant customer support to solve problems and are knowledgeable of the problems faced by the customer. The documentation is pretty top-notch.

Incentivized

We evaluated MongoDB also, but don't like the single point failure possibility. The HBase coupled us too tightly to the Hadoop world while we prefer more technical flexibility. Also HBase is designed for "cold"/old historical data lake use cases and is not typically used for web and mobile applications due to its performance concern. Cassandra, by contrast, offers the availability and performance necessary for developing highly available applications. Furthermore, the Hadoop technology stack is typically deployed in a single location, while in the big international enterprise context, we demand the feasibility for deployment across countries and continents, hence finally we are favor of Cassandra

Incentivized

DataStax Enterprise offered best-in-class write performance and scalability. The customer support team was very helpful in the adoption of new technology.

Incentivized

• I have no experience with this but from the blogs and news what I believe is that in businesses where there is high demand for scalability, Cassandra is a good choice to go for.
• Since it works on CQL, it is quite familiar with SQL in understanding therefore it does not prevent a new employee to start in learning and having the Cassandra experience at an industrial level.

Incentivized

• Highly Scalable Database, Highly Available Services, and Platforms.
• High Performance, Low Latency and Highest throughput across varying workloads.
• Configured, Tuned and Monitored correctly works to provide the best user experience!
• Negative: Maintenance and Debugging Corner Cases

Incentivized