What users are saying about

Apache Kafka

24 Ratings

Cassandra

Top Rated
61 Ratings

Apache Kafka

24 Ratings
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Score 8.6 out of 101

Cassandra

Top Rated
61 Ratings
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Score 8.2 out of 101

Add comparison

Likelihood to Recommend

Apache Kafka

Apache Kafka is extremely well suited in near real-time scenarios, high volume or multi-location projects. It can solve escalation problems for a fraction of the cost other solutions do and it has the flexibility of open source scenarios.
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Cassandra

Cassandra has excellent high availability and partition tolerance and has a robust architecture.It is well suited for storing immutable data as deletes are extremely inefficient. As such, it is well suited for data archive and deep storage.It is less appropriate for OLAP as has limited aggregation and filtering abilities, and no grouping whatsoever.
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Pros

  • High volume/performance throughput environments
  • Low latency projects
  • Multiple consumers for the same data, reprocessing, long-lasting information
Juan Francisco Tavira profile photo
  • Cassandra is very strong for saving the time series based transaction data model, simply by reversing the time series order when creating the data table, we can very quickly fetch the "latest" records even from millions of associated transactions because the latest record is always at the top of the search. By combining with the TTL feature of the Cassandra column, it is easy to "auto" delete the old data.
  • Cassandra combines the key-value store from Amazon's DynamoDB with the column family data model from the Google's BigTable, which makes it easy to manage both structured and non-structured data model efficiently.
  • By using the DataStax Enterprise version provided Solr integration, it can even solve some ad-hoc query needs which may not be fully taken into account at the beginning of the project when the data table is created. This extremely adds more room to play for a large enterprise or project which does require some flexibility in the practical context.
  • The linear scalability provided by Cassandra, allowing us to easily scale up/down the cluster by simply adding/removing the servers.
  • The throughput for both the read/write performance of Cassandra is quite good.
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Cons

  • Still a bit inmature, some clients have required recoding in the last few versions
  • New feaures coming very fast, several upgrades a year may be required
  • Not many commercial companies provide support
Juan Francisco Tavira profile photo
  • Managing the big cluster of Cassandra , even with the DataStax Enterprise Version, is still quite challenging for a maintenance team, considering the frequent version upgrade (even in the rolling fashion) and more frequent auto-repair, for me on this area, a powerful tool should be provided to "automate" this process as much as possible.
  • The TTL design is good, however the pain is if the TTL is set on some data already inserted, it can not be simply updated. Unless that data is reinserted again, this fact causes a lot of issues in case the business strategy is changed which requires the purge strategy to be updated also.
  • As the nature of Cassandra is still Java based, the GC sometimes eats some performance, if Cassandra can allow using more non-Heap memory space, to reduce the GC efforts which will free more power on the hardware.
  • The default indexing strategy for JSON formatted data in the DataStax's Solr integration is not available. At this moment we have to implement our own to support our JSON text stored. We extract the key field from our data which might be required to be ad-hoc searched, converting them into the JSON format (only one level Map), and save them into the Cassandra column. On top of that we want Solr to index the key of each token.
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Likelihood to Renew

Apache Kafka9.0
Based on 1 answer
Kafka is quickly becoming core product of the organization, indeed it is replacing older messaging systems. No better alternatives found yet
Juan Francisco Tavira profile photo
Cassandra8.0
Based on 11 answers
I used it while I was doing my academic projects, since the project is over I am no longer using Cassandra currently.
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Alternatives Considered

Kafka is faster and more scalable, also "free" as opensource (albeit we deploy using a commercial distribution). Infrastructure tends to be cheaper. On the other hand, projects must adapt to Kafka APIs that sometimes change and BAU increases until a major 1.x version comes out and adds stability to the product.
Juan Francisco Tavira profile photo
These are the features which makes Cassandra different from others:
  • Cassandra is a distributed datastore, with a built-in coordinator. This means that requests are intelligently forwarded to the correct node.
  • It is generally very fast, and especially shines with write heavy workflows.
  • It scales linearly. If you double the nodes, you’ll double your throughput.
  • Embraces eventual consistency.
  • Masterless replication across data centers means that your data is always accessible.
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Return on Investment

  • Easier deployment and horizontal scalability
  • Messaging cost reduction
  • Developments require adaptation and some paradigm shift to interoperate with Kafka
Juan Francisco Tavira profile photo
  • I have no experience with this but from the blogs and news what I believe is that in businesses where there is high demand for scalability, Cassandra is a good choice to go for.
  • Since it works on CQL, it is quite familiar with SQL in understanding therefore it does not prevent a new employee to start in learning and having the Cassandra experience at an industrial level.
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Pricing Details

Apache Kafka

General
Free Trial
Free/Freemium Version
Premium Consulting/Integration Services
Entry-level set up fee?
No
Additional Pricing Details

Cassandra

General
Free Trial
Free/Freemium Version
Premium Consulting/Integration Services
Entry-level set up fee?
No
Additional Pricing Details