Step into the future with NoSQL - an adaptive, scalable and flexible solution for managing complex data efficiently.
SQL, short for Structured Query Language, is the tool developers use to engage with traditional relational databases. If you're unfamiliar with what a relational database is, you can think of it as a collection of interconnected spreadsheets. Each row in this 'spreadsheet' is a record, and each column represents a type of data. While SQL excels with structured, consistent data, it can be rigid and unsuited for unstructured or varied types of data. This setup works perfectly for managing two-dimensional data. However, things get more complicated when we need to add more layers to the data.
Imagine, for instance, you have a table (like a spreadsheet) for employee details and you want to add the capability to store multiple email addresses for each person. You could extend the original table by adding new fields for 'Email1', 'Email2', 'Email3', and so on. Or you could create a new table specifically for storing all the email addresses, and link them to the right person via a unique identifier.
This second approach illustrates a 'one-to-many' relationship - one person can have several email addresses. It helps keep the data more organized and adaptable to changes. However, consider the performance implications of this setup. If you want to retrieve a specific person's details along with their associated email addresses, the SQL server will have to scan the person table to locate the correct record and then sift through the email address table. This effectively doubles the time it takes to query the data, which may impact overall system performance.
Now imagine managing a complex database containing millions of records. Changing or modifying these tables directly is not feasible due to the sheer size and volume. Instead, to accommodate new types of data, you would have to create new tables and link them using unique identifiers. This holds true even when the relationship between data is one-to-one.
For instance, let's say you have an employee table storing first and last names. A new business requirement comes up requiring you to record each employee's date of birth. Instead of directly adding a 'DOB' field to the existing table, you would need to create a new 'DOB' table and connect it to the employee table using a unique ID.
As additional business requirements arise, such as storing driver's license numbers or passport IDs, you'd end up creating more tables. This turns your initial single 'person' table into a network of four interconnected tables. And each time you need to retrieve comprehensive information about an individual, the system would need to traverse through all these tables, resulting in a process that is four times slower than it was originally. This can significantly impact the efficiency of data queries and overall system performance.
Traditional ITSM and ERP solutions can slow down over time, largely due to the increase in data they handle coupled with the number of customizations implemented. This combination can exponentially impact system performance, making data retrieval and processing significantly slower.
On the other hand, Nexoid employs NoSQL technology, which, despite its name, isn't technically a database in the conventional sense. Rather, it's a system for storing documents. They can handle high-speed, real-time applications and scale horizontally, distributing data across multiple servers as the volume of data grows. These documents are housed in what are called 'indexes', which you can think of as being similar to folders on your computer or tables in a traditional database. Each data record is stored as an individual file.
The data within these files is stored in a format known as JSON, a more compact alternative to XML. If you're not familiar with these, you can think of them as akin to text documents. In a sense, each data record gets its own unique document.
The data is stored in these documents as 'objects'. For example, a basic object could contain simple information such as a person's first and last name. Within this basic object, you might have another object named 'email addresses', which itself can contain several email addresses. The key advantage here is that all relevant data is stored in a single file, allowing for a single retrieval operation.
Another significant advantage of NoSQL is its flexibility. Unlike in a structured SQL database, data structures in a NoSQL system don't need to be uniform. You could even have varying data structures within the same index..
Moreover, NoSQL systems are not only more efficient from a query standpoint, but they also offer greater efficiency in terms of storage. Traditional SQL databases usually require all tables to be hosted on the same server to allow for filtered queries across multiple tables. In contrast, NoSQL systems can distribute 'records' or files across multiple servers without any performance penalty, even for large datasets. This scalability aspect gives NoSQL a significant advantage when handling vast amounts of data.