IBM watsonx.data vs. Vertica Analytics Database

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.

Incentivized

As someone just starting out with data analytics and warehousing vertica is a great tool for a small scale business. It has amazing performance and can scale upto TBs of data. It works well for any organization which has about 100 - 500 DAUs of the system. The system doesn't require a lot of ops overhead. Scaling for PB data and 1000s of DAU is vertica's weak point. The system is just not designed for large scale usage and still has a long way to go to improve scalability. There are experiments to run Vertica query engine on top of HDFS which seem promising, however - if you have the the Hadoop ecosystem you are better off going the HDFS + Presto/Impala/SparkSQL route. But if you are in the Hadoop ecosystem, you probably are already investing a lot in ops.

Incentivized

• It doesn't just store data but unlocks potential. I am able to analyse a vast amount of information, identify trends, and predict future outcomes.
• It not only gives me high quality but accessible data as well. It handles missing values, outliers and feature engineering with case.

Incentivized

• Column-oriented storage organization, which increases performance of queries.
• Compression, which reduces storage costs and I/O bandwidth. High compression is possible because columns of homogeneous datatypes are stored together and because updates to the main store are batched.
• Shared nothing architecture, which reduces system contention for shared resources and allows gradual degradation of performance in the face of hardware failure.
• Easy to use and maintain through automated data replication, server recovery, query optimization, and storage optimization.
• Support for standard programming interfaces ODBC, JDBC, ADO.NET, and OLEDB.
• Integration to Hadoop with the capability to perform analytics on ORC and Parquet files directly.

Incentivized

• Integration complexity with Security Tools while watsonx.Data is well-suited for native tools, but integration with third-party security tools requires custom connectors or manual ETL pipelines. which leads to an increase in setup time.
• User interface and query time can be improved.

Incentivized

• One time, one of the nodes wasn't coming up because of some ambiguity with the local data. Vertica wasn't able to fix it by itself and we were trying to remove the node out of the database and we couldn't do it. It would be great if that could be addressed. Luckily when we rebooted the whole server, some of the dead transaction got flushed because of which vertica was able to recover and the node came up.

Incentivized

As an open source technology Cassandra can be readily used with or without any commercial support. DataStax provides value-added services and features, and in the end it is up to individual situations to strike a balance between the desirability of such support/service versus the associated cost.

Incentivized

DataStax has a good community built around it and has amazing scalability options. Though the initial setup is a bit costly, in the long run, it makes up for it. It also has powerful monitoring tools and a clean UI.

Incentivized

good recovery features

Incentivized

scalable product

Incentivized

We have had a few situations where we caused an outage or something has gone wrong and we are able to get a support person to offer live help within minutes. The escalation process is excellent - the best I've seen - and the support team is incredibly strong. Outside of emergencies, the team is very helpful with general questions and working through data model exercises and the subscription I believe still comes with some hours to help get the data model reviewed.

HP/Micro Focus Vertica support is in par with other bigger vendors. In addition to this, there is enough best practices documentation available for some of the most common ways you will use Vertica that makes it easy to get Vertica up and running.

Incentivized

easy to follow documentation, support is there when needed

Incentivized

use saas service

Incentivized

I believe DataStax Enterprise is the best in class. There are some things that are different with the schema-less systems but I found DataStax Enterprise easiest to implement while evaluating. The replication is on par or better than others in practice. We are evaluating Astra in our test environment and that has additional benefits we are looking forward to using.

MySQL and MS SQL Server are both fantastic RDBMS products. MS SQL Server goes a bit further since it has the builtin analytical functions. But it only scales so far. Once the data goes beyond capacity, getting results out just does not happen anymore. IBM Netezza and Teradata were both appliances that required different expertise than we had in house. Vertica was able to do the same, and in some cases better, on commodity hardware (frankly in our case old servers that were slated for recycling!) and at a small scale. In other words, Vertica we could grow slowly over time. Infobright is a great log processing database but for the functions we were looking to serve it just didn't have some of the features Vertica had that we felt were show stoppers.

Incentivized

cognos integration works great

Incentivized

• for one automation project, we managed to cut cloud storage costs by a third through IBM watsonx.data's lakehouse optimization
• data integration projects have had a 20 % reduction in turnaround times. Can only imagine how that will improve with the Claude partnership

Incentivized

• Vertica increased our productivity in analyzing the data and validating simple proof of concepts with our data.
• Results of analytical queries produced from Vertica are used by all departments as well as part of some of our products.

Incentivized