5 out of 5.0, Reviewed Nov 18, 2016
Our experience was AMAZING. Implementing Datastax Enterprise, saved us almost 6 months of work and headaches. We got access to DataStax Enterprise as part of their startup program. DSE had been instrumental in setting up our News and Social Media Analysis tools and crunching that data. Before DSE, we had been struggling for months with integrating different open source solutions for storing and analysing terabytes of news data. Setting up Datastax Enterprise was quick, with a lot of help from folks at Datastax and Training materials available in their website offered help for everything from Installation, designing the entire data center and defining schemas. We had multiple calls with Datastax Engineers, who helped us get the maximum out of DSE for our purpose.
The overall simplicity of initial setup and the amazing support.
I wish the components that came bundled with DSE, was a little more updated version.
If I could start over, I'd have signed up for DataStax Enterprise on Day 1. That would have saved us a year, we spent working with other vendors and open sourced versions of Cassandra, Spark and Solr.
4 out of 5.0, Reviewed Oct 25, 2016
Support staff is very skilled and friendly, response times are within SLAs. DataStax Enterprise is a great product and continues to improve on each release.
Make sure DataStax architects review your data model before going live in Production, it will save you lots of headaches.
Repair Service, Snapshot Backups
Ensure production readiness and offer some sort of installation certification for best practices.
Spend more time on database modeling and work closer with consulting services in this area.
5 out of 5.0, Reviewed Oct 6, 2016
Datastax's solution has allowed us to scale and do things we have never been able to do in record time. Their support has been there all along the way helping architect and solves challenges we had in order to hit our goals.
Integration of open source components to work in perfect unison with each other.
Always getting us excited about new things coming in the next version and having to wait a bit for them.
We would skip the open source solutions and start working with Datastax earlier in the process. Once they were involved in our Cassandra deployment we were able to get things up and running and more smoothly much quicker.
Always around to provide competent and thorough solutions to our problems both in our implementation and in their own via hotfixes.
5 out of 5.0, Reviewed Sep 28, 2016
DataStax has high expertise in the field of Big-Data and NoSQL databases. They are not just offering a 'product' but they offer a 'solution' for enterprises to implement and deploy big-data analytics in-house. Apart from the software solutions, their sales/support personnel are highly helpful.
If you are developing applications separate out the transactional component(payments, banking etc) of your business - which is usually a tiny part, logic to use an RDMBS but select a good Big-Data ready NoSQL solution for the rest of the application logic. Once you have NoSQL architecture ready, select the solution that lets you integrate into multiple tool sets for visualization, analytics, ETL, reporting to make your business run smooth without having to switch vendors mid-cycle.
DSE is not bloated with unnecessary components - it is lean. DSE is easy to maintain and understand for the admins. DSE adopts the latest Cassandra, Spark, and other key components. OpsCenter is a great tool too.
We wish DataStax came with powerful visualization tools and DAO-based Cassandra connectors for web-application frameworks. Expect more OpsCenter admin/maintenance operations.
We started using a popular Relational DBMS when we started developing our analytics application and found out it wasn't sufficient for our scaling needs- then we tried existing big-data solutions for a month before deciding on DataStax. If we could start over we would've picked DataStax/Cassandra from the get-go and saved a few months of time/effort.
Very on-time and informative. Remember getting responses with a couple of hours around the clock. Having experts to answer the questions was very helpful.
We deployed DSE on AWS and it was very convenient. Even though we started with smaller nodes for the cluster, we were able to replace them with large instance nodes without having a downtime - all done through the OpsCenter. It was easy and straight forward setup.
4 out of 5.0, Reviewed Sep 27, 2016
Overall support from DSE is satisfactory. I can see they keep improving their products.
Join the startup program.
Need more training material for DSE Analytics setup and performance tuning.
More training information and better debugging capabilities.
Subscribe to Datastax support earlier.
Response time is usually within the promised window.
3 out of 5.0, Reviewed Sep 27, 2016
Good product. Initial Support and documentation were lacking early on in 2012. Has improved, but a need for support has diminished with product experience.
Learn about compaction strategies and Java VM garbage collection optimizations
Safety of data and ease of scaling
Debugging performance issues very difficult
Indicated what operations are risky/resource intensive.
Would architect the system to avoid wide rows.
Stack overflow provided 90% of our support requirements
5 out of 5.0, Reviewed Sep 25, 2016
The system provides features that are good for large deployment. It is also fast in processing data and the performance is not compromised with the increase of usage. One good feature that we loved most is the redundancy system and the fast accessing and processing time.
Easy to deploy and use. Good redundancy system.
Queries was responded in timely manner and further assistance was provided if required.
5 out of 5.0, Reviewed Sep 24, 2016
DataStax continues to provide excellent guidance on deployment and best practices while giving insight into their roadmap.
Expect to dedicate multiple senior people to understanding the solution, scaling it, etc. This is typical of all complex data systems, since if you have scaling challenges then you should expect to understand your data systems at a deep level. If you don't have scaling challenges then you probably shouldn't consider complex systems.
Modify commonly held best practices sooner to fit organization's needs. In the early days of adoption, we held to common wisdom too strictly and should have adopted it to meet our challenges more efficiently.
Prompt responses to technical questions. Willing to go back to engineering team for guidance. Multiple technical resources were made available.
4 out of 5.0, Reviewed Sep 16, 2016
Successful setup of-of a Datastax Cluster with 2 Cassandra Datacenters (1DC with 14 nodes, another with 5 nodes). We also use the building Spark on top of Cassandra. We tried Solr. We do manage to make it works but it does not fit our requirements in term of performance (average response time was twice superior as MongoDB, 5ms with Mongo, 10ms with Cassandra and Solr). That beeing said our setup of MongoDB does not share the data and all the data can be held on a single server with the index in memory whereas Cassandra shards the data : that can explain why so is a little slower. We mainly use Cassandra to store a big history of data about our users. It gave us the ability to grow our cluster without any pain.
If possible test your use case on a dedicated platform and if not possible
The capability to be always online even when we perform the operation on the Datastax cluster and also the capability to grow our infrastructure without pain.
Still too much complexity (options, tricks, etc) that can be simplified in my opinion.
I think Datastax should work a make the product easier to use for developers. Cassandra simplifies a lot of work for ops (ability to add, remove, upgrade the nodes works really great) but for developers, it comes with an extra cost in data modeling and tricky configuration.
The data modeling is very painful and prevents us from having a model that match most our-our use cases. We some table as an index to help on that issue. We tried Solr but for a real-time workload and it does not fit. The configuration of a Solr index is still too complex (too many settings and tricks), it should be possible to declare it with a single instruction. We use Spark on Cassandra nodes (with the building option). At the beginning, it was the easy choice but now we think that it could be a mistake to mix application workload and spark workload with the same servers. Datastax recommends to use a dedicated DC for Spark but if comes with an extra cost for new servers and data stay licenses.
Support for startup is great, we always have a precise answer to our questions quicly.
5 out of 5.0, Reviewed Sep 14, 2016
Excellent startup program. Great customer service. The product just works which considering the combination of services provided is quite an achievement.
Make sure to get the configuration correct during implementation, and make use of the available tools for data modeling and schema planning.
Automated monitoring and configuration tools good be better.
Provided Datastax as a Service - a fully managed implementation.
Implement multiple data centers from the outset instead of adding on later.
Outstanding support, always quick to answer questions and help resolve issues.
Deployment and configuration were fairly straightforward, although documentation was not always up to date with all available configuration properties.