It’s not a secret that organizations are in love with data. The decision-making process can be sloppy and market information are lost when companies have a small amount of information. However with active and massive databases, with requests ranging in hundreds or thousands ensuring that databases perform at a high level is becoming increasingly challenging.
One open source program, Apache Cassandra, enables companies to process massive amounts of rapidly moving data in a secure and efficient way. That’s the reason why companies like Facebook, Instagram and Netflix make use of Apache Cassandra for mission-critical features. Let’s examine three key advantages, disadvantages, and use examples from Apache Cassandra, and the most straightforward method of getting it working in production.
What exactly is Apache Cassandra?
To begin this brief overview, let’s look at the basics of Apache Cassandra. Apache Cassandra is a database that is designed to provide reliable performance speed, speed, and scaling. It can quickly store huge quantities of data coming in and handles several hundred thousand write per second.
Cassandra lets organizations manage massive quantities of data in a short time – offering the benefits listed below for its users.
The 3 most important advantages of making use of Cassandra
Speed – Performance
Certain architectural decisions Certain architectural decisions make Cassandra an ideal technology for processing data more quickly than other database options. There are two methods Cassandra can achieve a speedy processing:
It takes quick decisions about how to store data. It does this by with an algorithm that hashs data
It allows any node to make storage decisions for data. This removes the requirement for an uncentralized “master node” which needs to be consulted regarding storage decisions.
Cassandra is extremely scalable, and it is possible to increase the performance simply by adding a rack. In the first place, there isn’t a “master” that has to be super-sized in order to manage data and orchestrate it. That means that all nodes are able to be less expensive and common servers.
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In addition, it increases scaling by placing less emphasis upon data quality. Consistency generally requires a master server to determine and control what it means, using rules or data that has been stored 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 adding new nodes easy.
Reliability – Data replication and the ability to replicate data
In addition, it’s a solid storage of data. The hashing algorithm is able to store information as well as creates duplicates and then stores the data in various places. That means that if the node is down and Cassandra is able to make the reasonable assumption that eventually the node will fail 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 must be an approach to ensure that all copies are current.
Reliable, fast, and scalable Reliable, fast and scalable Cassandra can help modernize your cloud
Problems with making use of Apache Cassandra
Rapidity, scalability, and durability are not free. The choice of availability over consistency is made through Apache Cassandra so it is possible for data to be contradictory. As it attempts to verify information over time, the system could be slow in doing this. This causes a slowing of the reading process for the data that is that is already stored. The database has to look through all the data it has stored, which includes 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? Below we’ve outlined some typical usage scenarios:
The time series data Cassandra has a great record of storing time-series information, in which the data is not required to be changed. A good example of this is log files that are stored on cloud infrastructure and applications. There’s no reason to modify a log once it’s been stored. If it’s not correct it’s much easier to find the right version and save it with a more recent timestamp.
Globally-distributed Data geographically distributed data, where the local Cassandra cluster is able to store data, and reach consistency in later times. Since it does not have a “master node” and can be scaled by using storage that is common This allows for a cost-effective, geographical expansion of the database
Network costs are very high. Cassandra is a cost-effective option when the network (e.g. shifting data across data centres) costs are very high as it doesn’t have to continue sending data to a master node that is far away.
Organizations can modernize their cloud and alter the way that data is stored and processed by 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 to handle hundreds of thousands of writing per second.