It was packaged with the vendor product we bought. Also, it’s good for high performance transactional systems. I'm part of our NoSQL team and Cassandra quickly became a favorite for developers with agile teams.
DynamoDB is good and is also a truly global database as a service on AWS. However, if your organization is not using AWS, then Cassandra will provide a highly scalable and tuneable, consistent database. Cassandra is also fault-tolerant and good for replication across multiple …
Cassandra has its own use case. It performs very well as a data store. Data can be written to it at a high rate. It cannot be compared to traditional RDBMS like Oracle, because they all have their own usage. Even MongoDB, which is somewhat similar, cannot be stacked up against …
We evaluated MongoDB also, but don't like the single point failure possibility. The HBase coupled us too tightly to the Hadoop world while we prefer more technical flexibility. Also HBase is designed for "cold"/old historical data lake use cases and is not typically used for …
Technology selection should be done based on the need and not based on buzz words in the market (google searching). If your data need flat file approach and more searchable based on index and partition keys, then it's better to go for Cassandra. Cassandra is a better choice …
Cassandra is the only NoSQL database I have extensive experience with. In terms of other open source database solutions, I can say that I like Cassandra as much or equally as traditional Oracle MySql, and a lot more than PostgresSQL. The decision to use Cassandra was driven by …
Against HBASE, writes were faster. Reads not so much. Also ability to store in other formats would be good (such as objects). Compared to aerospike, does not compare. Aerospike blows it out of water.
Cassandra does one thing very well. It's able to collect any type of metrics and analytics and store them at very fast speeds. But when it comes to reading the data, there are minor performance issues. That's when other databases such as couchdb or couchbase come in. They can …
Apache Cassandra has the best of both worlds, it is a Java based NoSQL, linearly scalable, best in class
tunable performance across different workloads, fault tolerant, distributed, masterless, time series database. We have used both Apache HBase and MongoDB for some use cases …
Four years ago, I needed to choose a web-scale database. Having used relational databases for years (PostgreSQL is my favorite), I needed something that could perform well at scale with no downtime. I considered VoltDB for its in-memory speed, but it's limited in scale. I …
We also evaluated mySQL and mongoDB. Both of them have their strengths and weaknesses but they are less suited for storing massive amounts of time series data. In addition, they are not elastic by nature and we required a "future-proof" solution as it was difficult to estimate …
I have not used anything like Neo4J because of how unique it is in the work that it allows me to do. I am not aware of any other graph database platforms and it might be because it is a growing area (especially in the world of pharmaceuticals). I would be open to trying other …
Neo4j is a graph store and has different use cases compared to another NoSQL Document store like MongoDB. MongoDB is a bad choice when joins are common as existing operators for joining two documents (similar to tables in a relational store) as Mongo 3.5 use SQL like join …
Neo4j is ahead of any of the leading competitors I know. The only one which comes close, in my opinion, is the "Titan-Distributed Graph Database" which is completely open source and free to use. Titan works on top of Apache Cassandra so it has some huge learning curves to do, …
We've done some BOE comparisons between Neo4j, Titan, and OrientDB. The general consensus was that Titan is too unweildy and that Neo4j beat out OrientDB by being more active and having a large community.
Cassandra excels in a broad range of applications -- especially if you understand its data model and write your applications accordingly. It's an excellent choice for time-series data, and a poor choice for application queues. It performs the best if you can simply record history and compute from it, rather than going back and editing or deleting things a lot.
Its very well suited for storing graph types relationship information, such as a group of people and their relationships. Data modeling this sort of information in a traditional SQL database is a pain and inefficient. Using Neo4J allows for efficient modeling of data while providing rich querying capabilities using Cypher. Its also a great fit for any programming language because of its support for REST API. It's less appropriate for any other data structure other than Graph data. So as with any DB, evaluate the data structure and query and if the querying revolves around relationships, then Neo4J is a fit. If there is more need for looking up individual nodes and their associated information, Neo4J might not be the most efficient solution in the market.
High Availability - we utilize the data replication features of Cassandra. This enables us to access our data even when several nodes have gone down
Data Locality - our architecture combines Cassandra storage nodes and computation nodes in the same machine. This enables us to utilize data locality and limit expensive network IO to read data.
Elasticity - Cassandra is a shared nothing architecture. Nodes can be added very easily and they discover the network topology. As soon as a node has joined the Cassandra ring, the data is redistributed among the existing nodes and streamed to it automatically.
No Ad-Hoc Queries: Cassandra data storage layer is basically a key-value storage system. This means that you must "model" your data around the queries you want to surface, rather than around the structure of the data itself.
There are no aggregations queries available in Cassandra.
It would be nice to have some concept of namespaces, or some way of roughly making a single instance multi-tenant. It'd be nice to make sandboxing easier.
I would recommend Cassandra DB to those who know their use case very well, as well as know how they are going to store and retrieve data. If you need a guarantee in data storage and retrieval, and a DB that can be linearly grown by adding nodes across availability zones and regions, then this is the database you should choose.
Apache Cassandra has the best of both worlds, it is a Java based NoSQL, linearly scalable, best in class tunable performance across different workloads, fault tolerant, distributed, masterless, time series database. We have used both Apache HBase and MongoDB for some use cases which were within hadoop setup and JSON (JavaScript Object Notation) document store respectively, but given the overall factors favoring Apache Cassandra, it is a technology choice for multiple platforms!
I have not used anything like Neo4J because of how unique it is in the work that it allows me to do. I am not aware of any other graph database platforms and it might be because it is a growing area (especially in the world of pharmaceuticals). I would be open to trying other softwares though.
The open source version of Cassandra is only suggested for learning the basic concepts and play with its core features. Unless you really want to invest a lot in your developers and architects knowing every detail of Cassandra, I prefer the DataStax enterprise version. Although the license cost is relatively high, I think they it is worth it. I'm thinking about the support, the monitoring tool OpsCenter, and the integration of Solr and Spark (for data analysis).
Cassandra didn't fully replace our old and traditional relation database Oracle. In addition, it opens another door for us to deal with some special business use cases that NoSQL database can do better in a more feasible and efficient way.
For experimentation purposes, it had a positive impact on my company. It was very natural to work with Neo4j and so intuitive to visualize the data.
Neo4j community edition is free, which is what we experimented on. So there was no investment up front apart from employee's time. But this quickly gave results and it was time well spent.
Neo4j is a cool but very new technology. It was hard to have people onboard, especially some of the leadership and relational folks.