DataStax Enterprise is the primary database for all transactional processing. DataStax Enterprise provides linear scale as well as multi-datacenter real-time replication of data such that we can maintain uptime even with the loss of multiple data centers. Keeping the system up and the data fresh is of paramount importance for our clients. Performance is also top of mind and DataStax Enterprise delivers best-in-class performance.
Pros
Scaling
Speed of data access
Ease of use with those familiar with traditional SQL
Best in class support team
Cons
Hybrid on-prem / cloud solution with Astra.
Better compatibility with prior versions in terms of codebase.
Likelihood to Recommend
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.
VU
Verified User
C-Level Executive in Information Technology (11-50 employees)
The design and development of new aircraft engines require large amounts of sensor data to be stored, managed, and queried. The availability of the data and the query performance is critical to the safety of people and equipment. Traditional RDBMS systems proved to be inadequate for the job as the volumes and velocity of information increased. We selected DataStax Enterprise for its high availability, and best-in-class write performance. There is a learning curve to be overcome, but in the end, the new system matched and exceeded expectations. As a result, the company has a new reliable platform to support the business needs for years in the future.
Pros
Horizontal scalability
High availability
Fast writes
Cons
Query flexibility
User experience
Node density
Likelihood to Recommend
DataStax Enterprise's NoSQL database (Cassandra) shines where scalability is critical to success. Data write performance is best-of-class. However, be prepared to go through a steep learning curve. The data model and query options are difficult to master. It is not easy to find skilled people to design and support the database. It will take courage and dedication to implement the solution correctly in your organization. The bottom line is that DataStax will not be suitable for all use cases. Make sure you understand the limitations. Start small and grow as you learn. If you use it for the right reasons, it will serve you with excellent results.
<div><div><div><div><div><div>We are a small startup company. Datastax is being used by a company to address the need to rapidly ingest data. Due to the high up-time and easily scalable options, our company is able to grow better.</div><div>
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Pros
Easily and highly Scalable
Simple UI
High uptime
Cons
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
Likelihood to Recommend
DataStax has a good scalable option with multiple clusters and a good write rate. Cassandra also is improving and is an open-source technology that has good community support. The UI is also easy to understand and implement required functions.
VU
Verified User
Employee in Information Technology (1-10 employees)
We used Datastax in a POC and then, after appreciating its scalability and performances, we decided to use it for a real project. The project has a strict requirement to have the minimum possible latency and to reach a peak data rate in writing of more than 25k write per second. Moreover, we have the requirement to have replication between two data centers. We were able to reach our targets with a small cluster of 3+3 nodes.
Pros
Scalability: it provides near linear scalability being based on open source Cassandra
Opscenter: it is a powerful and complete tool for monitoring
You can use Spark for analytics workloads
Cons
Requires investment on hardware
Initial setup could be cumbersome
You need to be careful to use it only in the right context
Likelihood to Recommend
The best scenarios where to use it are when you need a really high write rate and you know the queries you are going to execute in advance. If you don't know how you will access the data in advance it is better to look at other solutions.
Datastax Enterprise Edition for Cassandra is currently being used across multiple departments at our organization. It is used for various critical use cases and platform solutions where we are creating highly available, linearly scalable systems and services with good performance. We have used it for services used by tax domain and small businesses. Profile platform, AB testing platform and other services across product groups use Datastax Cassandra with good success.
Pros
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.
Cons
The move from SQL to NoSQL paradigm is always difficult for people who have been using SQL for most part of their technical lives. Even if NoSQL has better performance and is more scalable, the database interface/functionality needs to be seamless for users. This has always been the top challenge. Now with the advent of ACID and horizontally scaling Google Spanner, the competition is rife for what a database can provide.
Though one can be immediately productive, if you get corner cases in your usage with Datastax Cassandra, you have to really know it better. There is a learning curve. Understanding Cassandra server logs, audit logs and sstables helps.
Debugging can be longer especially if you hit corner cases, like not using Light Weight transaction correctly, timestamp ties or getting RuntimeException on scrub/repair/compaction (java.lang.RuntimeException: 30623431613136352d656433372d343939322d393066342d366632313961393530353062 is not defined as a collection) and such.
Datastax Cassandra has great benefits in product, and features but there are costs on infrastructure maintenance and regular operational tasks. Not that there is any technical component that can self heal :-), but this time investment in Datastax Cassandra is more compared to SQL db, say MySQL.
Likelihood to Recommend
Datastax Cassandra is a Java based linearly scalable NoSQL database, best-in-class tunable performance, fault tolerant, distributed, masterless, time series database and has easy-to-use administration and monitoring functionality with opscenter. Configured correctly there is no downtime and no data loss. The documentation is exhaustive, and the community is agile and supportive, and Datastax provides good support. For all these reasons, Datastax Cassandra has become a NoSQL technology of choice for many platforms.
However it has some time investment on infrastructure and regular operational tasks, and if you do not have bandwidth for it, a managed NoSQL solution like Dynamodb might be more appropriate. Also if you have search needs on Cassandra and do not have corresponding Spark/Solr setup, Datastax Cassandra might not be ideal for you.
VU
Verified User
Professional in Information Technology (5001-10,000 employees)
It is being used by a department to address the need to rapidly ingest data.
Pros
It is always up, very fault tolerant.
Tunable consistency, flexible in that regard.
Nice management tools.
Cons
Support for aggregation functions is still limited.
Likelihood to Recommend
DataStax is best for simple data schema with high speed of writes. Data layout and modeling is centered around how it is going to be queried. It's not very appropriate for complex ad-hoc querying and analytics.
