It’s not a secret that organizations have a love-hate relationship data. It can lead to unguided decisions and market information can be lost when organizations have a small amount of information. However with active and massive databases, with requests ranging in hundreds of thousands maintaining the performance of databases becomes increasingly difficult.
One open source program, Apache Cassandra, enables companies to process massive amounts of data that are moving fast with a reliable and scalable manner. This is why companies such as Facebook, Instagram and Netflix make use of Apache Cassandra for mission-critical features. Let’s take a look at three main advantages, drawbacks and usage examples from Apache Cassandra, and the most efficient method to get it working in production.
What exactly is Apache Cassandra?
To begin an overview of the database – Apache Cassandra is a database that is designed to provide reliable performance speed, speed, and capacity. It can quickly store huge volumes of data that is incoming and handles several hundred thousand writing per second.
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Cassandra lets organizations manage massive quantities of data quickly, providing the following benefits to its users.
The top 3 advantages of making use of Cassandra
Speed – Performance
Certain architectural decisions specific architectural choices make Cassandra an excellent technology for processing data more quickly than other database options. There are two methods Cassandra can achieve a speedy processing:
It quickly decides how to store data. It does this by with an algorithm that hashs data
It allows any node to take data storage decisions. This means that there is no requirement for an uncentralized “master node” which needs to be consulted for storage decisions.
Cassandra is extremely scalable and it is possible to increase the performance simply by installing a rack. In the first place, there isn’t a “master” that has to be super-sized in order to handle the orchestrating and management of data. All the nodes are able to be less expensive as common servers.
In addition, it increases scaling by placing less emphasis upon data quality. Consistency usually requires a master node in order to monitor and regulate what it means, by relying on rules or stored data previously.
It also uses peer-to-peer communication, using the cleverly called “gossip protocol”. This allows nodes to communicate and transfer metadata among themselves, making the process of the process of creating new nodes extremely simple.
Reliability – Data replication and the ability to replicate data
In addition, it’s a solid database and the hashing algorithm is able to store data and also makes backups of it and puts them into different places. If there is a downtime for a node – and Cassandra is able to make the reasonable assumption that eventually the node will be down and there’s a backup of it.
The process of relaxing consistency can achieve this. Traditional databases must be extremely careful (and slow) when it comes to replicating data since there is a strategy for how to ensure that all copies are current.
Rapid, scalable and secure Reliable, fast and scalable Cassandra can help modernize your cloud
Problems with making use of Apache Cassandra
Rapidity, scalability, and durability cost money. The choice of availability over consistency is made in Apache Cassandra so it is possible for data to be contradictory. When it attempts to validate information over time, the system may be slow in doing this. This can slow down the process of reading the data that is that is already stored. The database has to search through all the information it holds, which could include several entries for the same data which could contradict.
Why should you use Apache Cassandra – modernise your cloud
The above outline highlights the advantages and drawbacks of Apache Cassandra but how does it integrate into your existing infrastructure? We have listed some common applications:
Data from time-series: Cassandra has a great record of storing time-series information, in which the data doesn’t need to be changed. A good example of this is log files created by cloud infrastructure or apps. There’s no reason to alter a log after it’s been stored. If it’s not correct it’s much easier to find the more accurate version and then store it with a fresher time stamp.
Globally distributed data geographically distributed data, where local Cassandra cluster is able to store data, and reach consistency at later times. Because it doesn’t have a “master node” and it is able to be scaled with commodity storage which is cheap, it allows expanding the geographic area of the database
Network costs are very high. Cassandra is a cost-effective option when the network (e.g. transfer of data between data centers) costs are very high as it doesn’t need to send data continuously to a master node that is far away.
Organizations can modernize their cloud and alter the way that data is stored and processed using Cassandra. This allows you to manage huge quantities of data across the globe.
Apache Cassandra lets your cloud reach “hyper-scale”. It offers practical solutions to achieve performance, scaling, and availability required for hundreds of millions of write per second.