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Cassandra

Cassandra

Overview

What is Cassandra?

Cassandra is a no-SQL database from Apache.

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Learn from top reviewers

review of cassandra

Rating: 6 out of 10
April 7, 2021
IncentivizedVetted ReviewVerified User
Used for specific product (which is used by whole organization). Addressing for column store we need for uniqueness of proprietary information that Redis and Mongo does not su...
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Anson Abraham
Engineer - Engineering
Data Lord
PlaceIQ
Apache Cassandra7 years of experience

One of the Best NoSQL Databases!

Rating: 8 out of 10
June 30, 2019
IncentivizedVetted ReviewVerified User
Cassandra is currently used for our enterprise eCommerce platform. So far our experience is good with Cassandra its an extremely powerful NoSQL Database with high performance—...
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Priti Asai / Thakkar
Engineer - Engineering
Lead Software Engineer
macys.com
Apache Cassandra5 years of experience

Cassandra at scale

Rating: 8 out of 10
March 16, 2019
IncentivizedVetted ReviewVerified User
It’s one of the database platforms we offer to the development community in our organization. We have various selections when it comes to databases including DB2, SQL Server, ...
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Glen Kim
Consultant - Information Technology
Senior Software Engineer
USAA
Apache Cassandra1 year of experience

Cassandra - a tunable NoSQL datastore

Rating: 9 out of 10
March 7, 2019
IncentivizedVetted ReviewVerified User
Cassandra is a NoSQL database which is used to store a large amount of data quickly. It has a very fast write speed, allowing a large volume of data storage within a small amo...
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Dhruba Jyoti Nag
Analyst - Information Technology
Associate
Cognizant
Apache Cassandra2 years of experience

Pretty good software

Rating: 8 out of 10
February 26, 2019
IncentivizedVetted ReviewVerified User
Cassandra is used in my organization by my department to handle data that is not in a standard RDMBS format.
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Feng Cai
Advisor - Information Technology
DB2 UDB DBA
CVS Caremark
Apache Cassandra2 years of experience
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Pricing

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Cassandra

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What is Cassandra?

Cassandra is a no-SQL database from Apache.

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Product Demos

Presto and Cassandra: Doing SQL and Joins on Cassandra Tables

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CassandraDB Connector Demo | CassandraDB Integration

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Open Source BI Tools and Cassandra

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Spark and Cassandra: Doing SQL and Joins on Cassandra Tables

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Real-time IoT data analytics and visualization with Kaa, Apache Cassandra, and Apache Zeppelin

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Features

NoSQL Databases

NoSQL databases are designed to be used across large distrusted systems. They are notably much more scalable and much faster and handling very large data loads than traditional relational databases.

8
Avg 8.8
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Product Details

What is Cassandra?

Cassandra Technical Details

Operating SystemsUnspecified
Mobile ApplicationNo

Frequently Asked Questions

Cassandra is a no-SQL database from Apache.

Reviewers rate Scalability highest, with a score of 9.5.

The most common users of Cassandra are from Enterprises (1,001+ employees).
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Comparisons

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Reviews From Top Reviewers

View all reviews
(1-5 of 5)

Cassandra - a tunable NoSQL datastore

Rating: 9 out of 10
Incentivized
March 07, 2019
Use Cases and Deployment Scope
Cassandra is a NoSQL database which is used to store a large amount of data quickly. It has a very fast write speed, allowing a large volume of data storage within a small amount of time. It is tunable and can be used to store data. It is more suitable for storing flat data rather than relational data.
Pros
  • Write speed. Cassandra is very fast while writing data due to its unique architecture.
  • Tunable consistency - During data replication, consistency can be tuned for a particular data set to be available during an outage.
  • CQL - cassandra query language is a subset of SQL and eases the transition from a more traditional database.
Cons
  • Aggregation functions are not very efficient.
  • Ad-hoc queries do not perform well. Queries which were visualized while designing the databases only perform well.
  • Performance is unpredictable.
Cassandra Feature Ratings
NoSQL Databases (7)
78.57142857142857%
7.9
Performance
70%
7.0
Availability
90%
9.0
Concurrency
90%
9.0
Security
90%
9.0
Scalability
90%
9.0
Data model flexibility
60%
6.0
Deployment model flexibility
60%
6.0
Likelihood to Recommend
Cassandra is well suited to storing a large volume of data within a very small period of time. It is relatively fast and the data consistency can be tuned for datasets for custom availability during an outage. It can be interacted with using CQL-- Cassandra query language-- which is similar to SQL, and thus transition is easier. It however performs less during aggregation and querying.
Return on Investment
  • Low learning curve
  • Scalable with high performance
  • highly fault tolerant during outage
Alternatives Considered
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 Cassandra since it again has its own advantages.
Other Software Used
Likelihood to Renew
9
Cassandra has already been integrated into our environment. It has performed well and lived up to the requirements of the highly scalable application which the designers strived to achieve. It has performed admirably as a data store and a huge volume of transaction data is written to Cassandra per second. So it is quite natural to be renewed given its impact.

Cassandra, put into the real business context

Rating: 8 out of 10
Incentivized
October 30, 2017
yS
yixiang Shan
IT Strategic Technical Advisor
FedEx Express (Package/Freight Delivery, 10,001+ employees)
Vetted Review
Verified User
Apache Cassandra
3 years of experience
View profile
Use Cases and Deployment Scope
We use Cassandra to build a fully functional POC (with the continuous production level volume of feeding data) for a shipment cloud concept for Fedex's EMEA region. This solution is composed of two parts, we use an IMDG product to keep the latest transaction of all shipments' latest "status" while we use Cassandra as our long-term transaction storage to keep all historical shipment status update events. On top of those InMemory and NoSQL storage, we built one unified RESTful based service, which depends on the user's query needs, either/and/or query the IMDG for the latest status of the shipment or query the Cassandra for the history of the shipment. Also, the Cassandra is used as the "backup" of the IMDG, in case the IMDG part is fully crashed (the worst scenario). Thanks to the time series way of persisting the data in Cassandra, we still can extract the "latest" status of a shipment from Cassandra's full transaction history with reasonable performance (slower than IMDG but much quicker than the traditional relational database).
Pros
  • 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.
Cons
  • 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.
Likelihood to Recommend
For the scenarios which need ACID support, maybe Cassandra is not the best, but for an insert only (time series based) transaction case and requirements to cope with the unpredictable data model/structure changes of the future, then Cassandra is one of the best options. If you only use the open source version of Cassandra, then without Solr integrated, you need to know your search query before you create the table, if that's not possible then Cassandra or other NoSQL DB might not your right choice.
Return on Investment
  • 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.
Alternatives Considered
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 web and mobile applications due to its performance concern. Cassandra, by contrast, offers the availability and performance necessary for developing highly available applications. Furthermore, the Hadoop technology stack is typically deployed in a single location, while in the big international enterprise context, we demand the feasibility for deployment across countries and continents, hence finally we are favor of Cassandra
Likelihood to Renew
8
In our POC Cassandra satisfies all our needs and expectations. We would like to do an additional POC to test its cross-continent cluster level replication features, measuring the performance and data consistency level to help us finally decide how to move to production.

Cassandra Usage and Needs

Rating: 8 out of 10
Incentivized
September 27, 2017
RR
Ravi Reddy
Lead Engineer Consultant
TEKSystems [Client : Target] (Retail, 10,001+ employees)
Vetted Review
Verified User
Apache Cassandra
4 years of experience
View profile
Use Cases and Deployment Scope
We are using Cassandra based on the requirements and data availability to the application (based on queries for search).
Pros
  • Cassandra lot of API's ready available for map reducing queries (like materialized queries).
  • Cassandra uses ring architecture approach, there is no master-slave approach (like HBase). If data is published on the node, the data will get synced with other nodes in the ring architecture, compared to HBase which has a dedicated master node to orchestrate the data into its slaves.
  • Write Speed
  • Multi Data Center Replication
  • Tunable Consistency
  • Integrates with JVM because it's written in Java
  • Cassandra Query Language is a subset of SQL query (less learning curve)
Cons
  • 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.
  • Not fit for transactional data.
Likelihood to Recommend
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. This can lead to storing the data multiple times in different ways to be able to satisfy the requirements of your application.
Alternatives Considered
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 compared with HBase because Cassandra has a lot of API's ready and available for map reducing queries (like materialized queries). Cassandra uses ring architecture approach, there is no master-slave approach (like HBase). If data published on of the node the data will get synced with other nodes in the ring architecture when compared to HBase which has a dedicated master node to orchestrate the data into its slaves.
Other Software Used
Likelihood to Renew
8
It depends on the data model [whether it makes sense] to structure the data in de-normalized approach.

What makes Cassandra different!!!!

Rating: 6 out of 10
Incentivized
June 07, 2016
Verified User
Engineer in Engineering
Computer Software Company, 51-200 employees
Vetted Review
Verified User
Apache Cassandra
2 years of experience
Use Cases and Deployment Scope
I had used Cassandra in my academic projects which were related to cloud computing. I used it for a few projects on Salesforce where multi tenancy features are implemented. In such scenarios Cassandra was one the best choices for NoSql. Although we have used RDMS, the performance while using Cassandra was better.

I have simulated a few real time running apps like Facebook and Uber where I have used RDMS and Cassandra, and checked the performance using Jmeter. It clearly shows that Cassandra boosts the performance over RDMS. One thing I find difficult in Cassandra is following the documents, which are not so understandable.
Pros
  • Undoubtedly performance is an important reason
  • We have not encountered a single point of failure
  • Scalability of Cassandra is good which is the most important for the companies where demand is scaling day by day.
Cons
  • Cassandra has a wide range of asynchronous jobs and background tasks that are not scheduled by the client, the execution can be eccentric.
  • Because Cassandra is a key-value store, doing things like SUM, MIN, MAX, AVG and other aggregations are incredibly resource intensive if even possible to accomplish.
  • I think querying options for retrieving data is very limited.
Likelihood to Recommend
Well Suited
Tunable Consistency
Write Speed

Less Appropriate
Ad-Hoc Queries
Unpredictable Performance
Return on Investment
  • 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.
Alternatives Considered

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.
Other Software Used
Likelihood to Renew
7
I used it while I was doing my academic projects, since the project is over I am no longer using Cassandra currently.
Users and Roles
175
We simulated the Facebook Application by continuously adding users .

Cassandra: A highly available and scalable database

Rating: 8 out of 10
Incentivized
March 15, 2019
Verified User
Team Lead in Engineering
Financial Services Company, 10,001+ employees
Vetted Review
Verified User
Apache Cassandra
3 years of experience
Use Cases and Deployment Scope
We use Cassandra as the NoSQL database for our use cases. We stream a lot of API data into this database and rely on the availability it gives us. It has proven to be consistent, which we use to our advantage. Cassandra can distribute data across multiple machines in an app-transparent manner, thus helping us to expand it on demand.
Pros
  • Cassandra is a masterless design, hence massively scalable. It is great for applications and use cases that cannot afford to lose data. There is no single point of failure.
  • You can add more nodes to Cassandra to linearly increase your transactions/requests. Also, it has great support across cloud regions and data centers.
  • Cassandra provides features like tunable consistency, data compression and CQL(Cassandra Query Language) which we use.
Cons
  • The underlying medium of Cassandra is a key-value store. So when you model your data, it is based on how you would want to query it and not how the data is structured. This results in a repetition of data when storing. Hence, there is no referential integrity - there is no concept of JOIN connections in Cassandra.
  • Data aggregation functions like SUM, MIN, MAX, AVG, and others are very costly even if possible. Hence Ad-hoc query or analysis is difficult.
Cassandra Feature Ratings
NoSQL Databases (7)
85.71428571428571%
8.6
Performance
80%
8.0
Availability
100%
10.0
Concurrency
90%
9.0
Security
90%
9.0
Scalability
100%
10.0
Data model flexibility
60%
6.0
Deployment model flexibility
80%
8.0
Likelihood to Recommend
You should be very clear where you want to use Cassandra because there is no referential integrity (JOIN) in Cassandra. You have to model data based on how you want to query it, hence what use cases it can be used for should be considered carefully.

You can use it where you want to store log or user-behavior types of data. You can use it in heavy-write or time-series data storage. It is good in retail applications for fast product catalog inputs and lookups
Return on Investment
  • Highly available and scalable database. Hence highly reliable in the organization for data.
  • Good performance - low latency and great throughput with varying workloads.
  • Maintenance and monitoring need investment and do not work great out of the box.
Alternatives Considered
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 data centers, which you get in DynamoDB as well.
Other Software Used
Likelihood to Renew
9
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.
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