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All new startups and small businesses face a particular set of problems when they start. Among them, the primary challenge is the collection, organization, and maintenance of the tremendous volumes of data they collect through daily interactions with their consumers. Almost none of the startups have an accurate estimation of the amount of data they will be collecting within the first few months of operation. Hence, they don’t have the resources to modify and scale up their database accordingly.
Here are a set of the potential problems that a business startup might face during the initial days of operations –
At around this stage, startups begin to ask the obvious question – “is the choice of SQL vs. NoSQL going to influence the availability of remote DBA services”? When someone compares SQL with NoSQL, the person is usually referring to the comparison between relational and non-relational databases. The primary difference between them is what information they store and how they store it.
For decades, structured query language (SQL) databases have been the leading choice of all businesses for storing primary data. Website applications and opensource options including MySQL, PostgreSQL, and SQLite facilitated the explosion of SQL database variants in the early 90s. Soon, every personal user and business across the globe were using SQL databases for meeting all their data storage and management need.
NoSQL databases are the choices of the younger and more recent entrepreneurs. However, one must remember that NoSQL databases are, in no way, a replacement for the SQL databases. The NoSQL options include Apache Cassandra, Redis, CouchDB, and MongoDB. Those familiar with the name, are aware of the fact that they have been around since the 1960s.
Although both SQL and NoSQL do the same task, their approaches differ. NoSQL is an alternative to SQL. While some businesses require SQL databases for the management of their data, others require NoSQL databases for the same. A handful few can use both database types interchangeably.
Structured Query Language (SQL) is more structured and rigid than NoSQL databases. Their data storage mechanism fits right into multiple popular software stacks including Ruby-based stacks and LAMP. Due to their popularity, startups and other businesses can easily find experts to service these databases almost instantly. Running into problems while using a well-known problem is not as much of a hassle as facing challenges on an unknown system.
Sadly, there is no one-for-all solution when it comes to SQL database technology. Most businesses simply rely on both relational and non-relational databases for a variety of jobs. While NoSQL databases are fast and scalable, particular jobs ask for SQL databases particularly.
The only problem startups face with SQL databases is when their data begins to snowball. Scalability is not their strong-suite. During their rapid growth, most businesses adopt NoSQL databases along with their existing database system to support their expansion.
Many startups begin their journey with vast volumes of unstructured data. During the beginning, most companies don’t know how to deal with the data flow, and they are not aware of the data’s future. Unless you have the luxury of developing an exclusive schema for your relational database, it is advisable to begin working with a NoSQL database. This type of database offers flexibility, and it provides extensive options for on-point scale-ups.
However, NoSQL does not have the consistency or maturity of the SQL databases. They don’t always have the tools and add-ons necessary for testing performance or analysis. Sometimes, you might face compatibility challenges with SQL instructions while running queries on NoSQL databases.
While NoSQL is popular choices for most startups, you should go with SQL if your priority is structured and organized data storage. However, for the integration of Big Data, you need to think about adding NoSQL to your repository. The volume of data and its variety will determine your business’ need for SQL or NoSQL database. Don’t be hasty. Understand the data inflow of your company before making your database choice.