We performed a comparison between Cassandra and Vertica based on real PeerSpot user reviews.
Find out in this report how the two NoSQL Databases solutions compare in terms of features, pricing, service and support, easy of deployment, and ROI.
"The most valuable features of Cassandra are the NoSQL database, high performance, and zero-copy streaming."
"Some of the valued features of this solution are it has good performance and failover."
"The solution's database capabilities are very good."
"A consistent solution."
"Cassandra is good. It's better than CouchDB, and we are using it in parallel with CouchDB. Cassandra looks better and is more user-friendly."
"Cassandra has some features that are more useful for specific use cases where you have time series where you have huge amounts of writes. That should be quick, but not specifically the reads. We needed to have quicker reads and writes and this is why we are using Cassandra right now."
"The most valuable features are the counter features and the NoSQL schema. It also has good scalability. You can scale Cassandra to any finite level."
"The most valuable features of this solution are its speed and distributed nature."
"The most valuable feature of Vertica is the ability to receive large aggregations at a very quick pace. The use case of subclusters is very good."
"Allows us to take volumes and process them at a very high speed."
"Vertica is a columnar database where the query performance is extremely fast and it can be used for real-time integrations for API and other applications. The solution requires zero maintenance which is helpful."
"The most valuable feature is Vertica's performance and the ease of using the database."
"It's the fastest database I have ever tested. That's the most important feature of Vertica."
"It has improved my organization's functionality and performance."
"The hardware usage and speed has been the most valuable feature of this solution. It is very fast and has saved us a lot of money."
"The solution has great capabilities. The tool that instructs the internal database forward is easy to use and is very powerful."
"The solution doesn't have joins between tables so you need other tools for that."
"Cassandra can improve by adding more built-in tools. For example, if you want to do some maintenance activities in the cluster, we have to depend on third-party tools. Having these tools build-in would be e benefit."
"Fine-tuning was a bit of a challenge."
"It can be difficult to analyze what's going on inside of the database relative to other databases. It can also be difficult to troubleshoot sometimes."
"Cassandra could be more user-friendly like MongoDB."
"The secondary index in Cassandra was a bit problematic and could be improved."
"The disc space is lacking. You need to free it up as you are working."
"The solution is not easy to use because it is a big database and you have to learn the interface. This is the case though in most of these solutions."
"Vertica seems to scale well, except for one use case where you are on a multi-node cluster. For example, if you had a nine-node cluster, one node goes down, then the eight nodes don't scale, because the absence of the node is very apparent, which is a problem. If you have nine nodes or multiple nodes, the whole idea is that if one of those nodes goes down, then you should not see an impact on the system if you have enough capacity. Even though we have enough capacity, you can still see the impact of the one node going down."
"They could improve the integration and some of the features in the cloud version."
"Vertica offers a platform-as-a-service version, but their software-as-a-service solution is only available on AWS. They need to get a SaaS version on Azure and GCP as fast as possible."
"I have found that coding support could be simplified."
"It's hard to make it slow for a small data volume. For large volumes, it's hard to make it work. It's also hard to make it faster, and to make it scale."
"Support is an area where it could get better."
"Promotion/marketing must be improved, even though it is a very useful product at very good price, it is not as "popular" as it should be."
"We faced some challenges when trying to use the temporary tables feature."
Cassandra is ranked 3rd in NoSQL Databases with 10 reviews while Vertica is ranked 4th in Data Warehouse with 11 reviews. Cassandra is rated 8.2, while Vertica is rated 8.8. The top reviewer of Cassandra writes "Great time series data feature but it requires third parties to join tables". On the other hand, the top reviewer of Vertica writes "Enhanced capabilities, good customer service, large data scalability and stable". Cassandra is most compared with MongoDB, InfluxDB, Couchbase, Oracle NoSQL and Scylla, whereas Vertica is most compared with Snowflake, Teradata, Amazon Redshift, Oracle Exadata and VMware Tanzu Greenplum. See our Cassandra vs. Vertica report.
We monitor all NoSQL Databases reviews to prevent fraudulent reviews and keep review quality high. We do not post reviews by company employees or direct competitors. We validate each review for authenticity via cross-reference with LinkedIn, and personal follow-up with the reviewer when necessary.
SQreamDB is a GPU DB. It is not suitable for real-time oltp of course.
Cassandra is best suited for OLTP database use cases, when you need a scalable database (instead of SQL server, Postgres)
SQream is a GPU database suited for OLAP purposes. It's the best suite for a very large data warehouse, very large queries needed mass parallel activity since GPU is great in massive parallel workload.
Also, SQream is quite cheap since we need only one server with a GPU card, the best GPU card the better since we will have more CPU activity. It's only for a very big data warehouse, not for small ones.
Your best DB for 40+ TB is Apache Spark, Drill and the Hadoop stack, in the cloud.
Use the public cloud provider's elastic store (S3, Azure BLOB, google drive) and then stand up Apache Spark on a cluster sized to run your queries within 20 minutes. Based on my experience (Azure BLOB store, Databricks, PySpark) you may need around 500 32GB nodes for reading 40 TB of data.
Costs can be contained by running your own clusters but Databricks manage clusters for you.
I would recommend optimizing your 40TB data store into the Databricks delta format after an initial parse.