If you are curious about databases that store, process, and/or mine Big Data, you must have heard of MongoDB and Cassandra — the most commonly known NoSQL databases. Whether you are just starting with a Free MongoDB basics program or are slightly further in your big data project, it will be highly useful to familiarize yourself with the purpose, similarities, and differences of these two resources.
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To answer this question, let us first have a basic idea of a database. Any framework that contains a large chunk of data, or “Big” Data, may be termed as a database. This database will have its unique mode of storing data, the old convention being a tabular format. SQL or Standard Query Language is one tool used to create and manipulate such databases. However, such a method of data management can no longer keep up with the present-day demand for storing large amounts of unstructured data.
Data often does not lend to easy quantification and/or categorization. It is oftentimes a struggle to decide if something qualifies as data in the first place. The need often arises for finding a way to process and structure vast amounts of information beyond the confines of a tabular format. This is where NoSQL databases, like MongoDB and Cassandra, come in. It is highly fruitful to familiarize oneself with these two database systems if a beginner seeks to take part in Data Mining projects.
While some say the term “NoSQL” denotes “non-SQL”, it is described by others more aptly as “not only SQL”. NoSQL databases manage to store data beyond a singular tabular format, allowing one to make sense of big, unstructured data according to an infinitely varied set of parameters. Other than data processing, NoSQL databases hugely aid in data storing across a vast network of cloud-connected servers. Using databases like MongoDB and/or Cassandra, one may easily scale data collection and management to a global level, with the data changing according to the location of its storage.
MongoDB is the most popular example of a NoSQL database and falls into the sub-category of a Document Database. It allows for a flexible collecting and storing of data, with its schema shifted to the needs of the particular data mining project it is utilized in. It is similar to JSON (JavaScript Object Notation) documents, which provide greater flexibility of altering data sets, thereby making it easy for developers to work with.
Each document of MongoDB contains data in a field of values, which may range from a variety of things, including strings, arrays, objects, and booleans. They range in types to the projects one is working on, or to the format the developer finds ease in. This flexibility, further, aids in its scaling to accommodate more and more massive data sets, helping in a data mining project that constantly expands from its initial portfolio.
In MongoDB, there’s an interesting dichotomy between the pervasive adaptability to change and the universality of a JSON-like structure. The document structure is universal in the way it accommodates local, intricate forms and models of data processing. Any data stored in MongoDB is automatically assumed to be scalable, and it hence uses a distributive system from the outset. This “horizontal scaling” allows for far greater accuracy and adaptability than “vertical scaling”, which only admits scaling at a later stage.
Cassandra is a column-oriented database, which allows flexibility of storage and predictability of incoming data. While it utilizes the more traditional, tabular mode of storing data, it far exceeds its inspiration. For example, each column may not have the same number of rows.
Cassandra takes its inspiration of data modeling from Google’s Bigtable and its distribution design on Amazon’s dynamo. It modifies and betters those aspects into establishing a mode of data storage and mining that is infinitely scalable, with high flexibility in its new nodes of adding, as well as predicting data. Since it uses a linear mode of scaling on columnar data, it is extremely fast in functions ranging from finding a specific data node to predicting and scaling.
Cassandra began as a way to better Facebook’s inbox search and to this day has a high degree of use in online social media. Furthermore, it is used by various large firms ranging from online selling companies like eBay to OTT entertainment platforms like Netflix. If one’s data mining projects involve any such service, they would do well to acquaint themselves with Cassandra.
Both Cassandra and MongoDB have their unique uses, which often overlap. They do not compete for the same functionality but have their own unique uses.
The first and most notable difference between the two is their mode of storing data; while MongoDB uses a JSON-like format, Cassandra specializes in Dynamo-format. MongoDB uses descriptive data sets, while Cassandra features data in columns. While MongoDB further specializes in endless scalability, Cassandra holds the edge in speed of predictability.
One’s data mining project may involve creating data sets for either or both of the two, or just modify and work with established data sets. Either way, it is useful to learn the functionalities of both, as well as other examples of NoSQL databases, to have a greater idea on which data system to use for the specific projects you may undertake in the future.
It can be said without a doubt that by familiarizing yourself with both databases, you will be inspired to formulate newer and better ways of managing Big Data and find far more ambitious projects!
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