We performed a comparison between Cassandra and Elastic Search based on real PeerSpot user reviews.
Find out in this report how the two Vector 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."
"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 time series data was one of the best features along with auto publishing."
"Our primary use case for the solution is testing."
"The most valuable feature of Cassandra is its fast retrieval. Additionally, the solution can handle large amounts of data. It is the quickest application we use."
"Can achieve continuous data without a single downtime because of node to node ring architecture."
"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."
"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."
"The special text processing features in this solution are very important for me."
"Search is really powerful."
"The search speed is most valuable and important."
"The tool's stability and performance are good."
"A nonstructured database that can manage large amounts of nonstructured data."
"The UI is very nice, and performance wise it's quite good too."
"The most valuable feature is the out of the box Kibana."
"Implementing the main requirements regarding my support portal."
"Depending upon our schema, we can't make ORDER BY or GROUP BY clauses in the product."
"Doesn't support a solution that can give aggregation."
"Fine-tuning was a bit of a challenge."
"Cassandra could be more user-friendly like MongoDB."
"The solution is limited to a linear performance."
"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."
"There were challenges with the query language and the development interface. The query language, in particular, could be improved for better optimization. These challenges were encountered while using the Java SDK."
"The solution doesn't have joins between tables so you need other tools for that."
"The UI point of view is not very powerful because it is dependent on Kibana."
"Elastic Enterprise Search could improve the report templates."
"Elastic Enterprise Search could improve its SSL integration easier. We should not need to go to the back-end servers to do configuration, we should be able to do it on the GUI."
"The reports could improve."
"We have an issue with the volume of data that we can handle."
"Kibana should be more friendly, especially when building dashboards."
"It needs email notification, similar to what Logentries has. Because of the notification issue, we moved to Logentries, as it provides a simple way to receive notification whenever a server encounters an error or unexpected conditions (which we have defined using RegEx)."
"There are some features lacking in ELK Elasticsearch."
Cassandra is ranked 11th in Vector Databases with 19 reviews while Elastic Search is ranked 1st in Vector Databases with 59 reviews. Cassandra is rated 8.0, while Elastic Search 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 Elastic Search writes "Played a crucial role in enhancing our cybersecurity efforts ". Cassandra is most compared with Couchbase, MongoDB, InfluxDB, ScyllaDB and Oracle NoSQL, whereas Elastic Search is most compared with Faiss, Milvus, Pinecone, Azure Search and Amazon Kendra. See our Cassandra vs. Elastic Search report.
See our list of best Vector Databases vendors.
We monitor all Vector 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.