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."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."
"A consistent solution."
"I am getting much better performance than relational databases."
"Our primary use case for the solution is testing."
"Can achieve continuous data without a single downtime because of node to node ring architecture."
"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."
"I am satisfied with the performance."
"The time series data was one of the best features along with auto publishing."
"It maximize cloud economics for mission-critical big data analytical initiatives."
"The solution is quick, has good compression data, and is not expensive."
"We are also opening new areas of business and potential new revenue streams using Vertica's analytic functions, most notably geospatial, where we are able to run billions of comparisons of lat/long point locations against polygon and point/radius locations in seconds. "
"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."
"Bulk loads, batch loads, and micro-batch loads have made it possible for our organization to process near real-time ingestions and faster analytics."
"It's the fastest database I have ever tested. That's the most important feature of Vertica."
"Vertica enabled us to close large deals. Customers with large data sets had to be migrated from PostgreSQL to Vertica due to performance."
"Fine-tuning was a bit of a challenge."
"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."
"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."
"The initial setup of Cassandra can be difficult in the configuration. There might be a need to have assistance. The implementation process can six months for connecting to certain databases."
"Maybe they can improve their performance in data fetching from a high volume of data sets."
"Cassandra could be more user-friendly like MongoDB."
"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."
"The disc space is lacking. You need to free it up as you are working."
"It needs integration with multiple clouds."
"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."
"Vertica can improve automation and documentation. Additionally, the solution can be simplified."
"The biggest problem is the cost of cloud deployment."
"When it is about to reach the maximum storage capacity, it becomes slow."
"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."
"Some of our small to medium-sized customers would like to see containerization and flexibility from the deployment standpoint."
"We faced some challenges when trying to use the temporary tables feature."
Cassandra is ranked 4th in NoSQL Databases with 19 reviews while Vertica is ranked 4th in Data Warehouse with 83 reviews. Cassandra is rated 8.0, while Vertica is rated 8.2. The top reviewer of Cassandra writes "Well-equipped to handle a massive influx of data and billions of requests". On the other hand, the top reviewer of Vertica writes " A user-friendly tool that needs to improve its documentation part". Cassandra is most compared with Couchbase, InfluxDB, MongoDB, ScyllaDB and Chroma, whereas Vertica is most compared with Snowflake, SQL Server, Amazon Redshift and Teradata. See our Cassandra vs. Vertica report.
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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.
Morten, the most popular comparisons of SQream can be found here: www.itcentralstation.com
The top ones include Cassandra, MemSQL, MongoDB, and Vertica.