Why Choose Cassandra For Big Data Management?
A run down of SQL and NoSQL database systems
By Wali Usmani
The term “Big Data” has been part of common tech vocabulary for quite a while, and it’s not going away anytime soon. So, the question now is- how do you store it? And more importantly — what’s the best way to store it for easy access, retrieval and consumption?
Here’s a little background. When you have a large amount of data that needs to be constantly retrieved by your technology stack, there can often be confusion around how to transform, manipulate and store it. The main challenge is determining how to govern these large volumes of data while ensuring quality, accessibility and reliability. Many organizations struggle with this, and have difficulty deciding what database system to choose.
Depending on the application and its requirements, this choice often lies between SQL and NoSQL data language. SQL is sometimes unavoidable, especially when exposing significant free form querying to your application(s). However, if such scope can be narrowed down, the speed and ease of access of a NoSQL system is hard to argue against. Personally, when I’m not given specific requirements, and have freedom to use any NoSQL system, the choice is clear: Apache Cassandra.
Here, I’ll be exploring the basics of database management and vouching for the superiority of Apache Cassandra. Rest assured, these opinions are completely my own and nobody over at Apache is paying me to promote their product. I just really like Cassandra.
Disclaimer: I’d like to add that this is very high level overview of Databases and Cassandra. I won’t be diving deep into the details or getting technical, but rather providing a base level of insight on the system. Think of it as an easy introduction to the Big Data Management in NoSQL systems, zero prior knowledge required.
Understanding Database Systems
The contrast between data structures is an important element to recognize. SQL and NoSQL databases operate in different ways and offer different advantages. While they are both viable options for managing big data, key distinctions exist that could be critical when deciding between the two.
What is SQL?
Structured Query Language gives us methods for manipulating and querying data in relational databases. This type of database consists of one or more tables, where each table includes relational rows and columns of information. Let’s imagine an abstract example. Think of a table with two columns– “Name” and “Birthdate”. SQL will allow you to search up an individual’s’ birthday by only knowing their name and applying that to an SQL statement, or vice versa. As long as you’re sure of one value in a row, you can search the entire row based on that value.
SQL structure has been used in primary storage databases for decades. Popular SQL databases include MySQL and Oracle, among plenty of others. There’s a community of knowledge behind SQL, which is what helps the language maintain its popularity. Overall, SQL systems provide the simplicity and reputation that can satisfy a lot of big data management goals.
What is NoSQL?
Not only SQL databases provide the ability to store and retrieve data modeled in non-relational ways. While there are many different kinds of NoSQL databases, an easy way to think of a them is as a collection of data blobs, each with a unique key. In order to retrieve the blob of data, you must know this key. Working with our prior example, each row in the table would now have a key which would be used to obtain a name or birthday. This limits how much or how well you can query the data, but it allows for a much faster retrieval since you don’t have to search the database. Overall, it’s a more dynamic way to organize information and the ideal choice if you don’t require much structure.
Many organizations use NoSQL to manage their data, the most popular systems being MongoDB, HBase, Couchbase and Cassandra. There are four categories of NoSQL, including Key-Value stores, wide- column stores, graph databases and document databases. With its more free form structure, it’s a top choice for businesses that are constantly evolving.
The Key Difference
It’s apparent that certain datasets may be more suited for SQL, and others for noSQL. Most often the deciding factor is how complex or variable the queries executed by the application(s) using the database need to be. Think of a library app storing a collection of books and authors. Users of this application might need to perform multiple queries such as looking up all the books by a specific author or the ISBN of a particular series of books. Such applications that allow users to make complex queries are usually excellent candidates for SQL systems.
Alternatively, think of a simple dating app like Tinder or Bumble. To find matches, a single query needs to be executed many times and the results need to be delivered quickly. In this case, NoSQL system would be ideal to use. However, it’s important to note that when building complex systems, you will often require the capabilities of both SQL and NoSQL. It is common for organizations to use a combination of systems to fulfill their needs. For instance, one classic combination that has gained a lot of popularity is storing data on a reliable noSQL system like Cassandra and building search on it using a search engine like Elasticsearch or Solr.
With the basics of SQL/NoSQL out of the way, let’s dive into Apache Cassandra. This system is a highly scalable non-relational database that helps power Spotify, Netflix and Apple. Cassandra was originally developed at Facebook and became a popular Apache project in 2010. It’s open source, wide column storing and widely scalable. You use it if you’re not doing as many wild searches — when your priorities are scalability, operational simplicity and lightning fast lookups.
The Ultimate Performance
Using Cassandra is a personal choice. It may not be everyone’s preference, but in my eyes, Cassandra is something special. My affinity for Cassandra comes from a few different advanced aspects of the system, including key clustering, configurability and conditional updates. However, above anything, it is Cassandra’s performance that never fails to wow me.
Cassandra has, by far, the best speed and reliability compared to any other alternative NoSQL system. A Datastax comparison of the four most competitive NoSQL systems shows a clear superiority both in throughput by workload and load process. Cassandra has a reputation for fast read-and-write performance as well as an undeniable reliability when it comes to data storage, which is why it is my system of choice.
A Trusted Choice
Overall, it can be a confusing road to decide what structure and system to choose, but it’s one that is necessary. Big data is used in countless businesses and organizations, and they all need a way to store it. My advice is to consider Cassandra. The big data landscape is only getting bigger, and Cassandra is one of the best ways to navigate it.
Originally published at www.twotalltotems.com on May 24, 2018.