5 powerful functionalities MongoDB provides which SQL operations fail to deliver
Most entreprises are well acquainted to collecting data for studying their business with analytic tools. In the early days, businesses have been collecting few generic types of data which allowed them to study their business on the most basic level. As the years passed by, the `analytics game grew strong and businesses began to collect a wide range of data in large numbers, i.e. in billions. This large volume of data is termed as big data.
Big data is a collection of large set of data records which might consist of millions and millions of data stored in tables. Dealing with big data grew to be tedious as the structural formatting of data is, was incapable of certain functionalities, say
These are a few of the issues raised by SQL users on the areas that puts them on a stalemate in terms of scaling and expansion. All these issues narrowed down to one answer and it is a database management system with no SQL. Yeah, in this blog we will be talking about MongoDB and the best practices to implement it effectively.
MongoDB is a document oriented and open source database platform which is capable of working with non-relational data. This is also called as no SQL database program.
Any database program which works with SQL requires its tables to be relational. For instance, the data gathered by your business is basically a record of your organization details. When these data is loaded in SQL, it segregates the data into different tables which consists of employee, manager, department, jobs respectively. Consolidation of all these tables into one is termed as “Collections” in MongoDB.
The structuring of data in SQL is done vertically where one table consists of all the employee details and the others consist of manager details, job type, departments respectively. This is where No SQL database program is different from SQL database program.
MongoDB generally works in “Key-Value” (Dictionary) format for all the data. Key marks the category i.e. employee name and Value marks the name of the employee “XXXXX” and when Key marks the age, then the Value will be the age of the employee “XXX”, etc. The next Key-Value is allocated for the next categories respectively.
In this case, the same data will be loaded into the database employee specific. This is easily scalable where you can keep adding “Key-Value” to a specific individual whereas in the relational database management system it might be difficult to alter the tables because not all the elements of a product have the same generic values.
Let’s take another example, your online store comprises of groceries, cosmetics, electronic gadgets, clothing etc. Price, seller, brand and quantity are the only common attributes that fit for all the products in your online store. However, when it comes to clothing there are additional data to products, say, size, fit, product material, etc.
While using relational database, scaling data horizontally might bring disorder throughout all the tables as few products might consist of size, fit, product material attributes whereas few products wouldn’t have such attributes which leaves them represented as “null values”
This leads to unnecessary usage of storage and moreover, when a table consists of millions of records, it is tedious to sync all the tables into one because it consumes a lot of time.
However, with MongoDB, as all the data is unstructured and doesn’t have any fixed schema, it is capable of loading all the data in a table which is capable of scaling horizontally. This only takes up space where it is required and there is no need for complex data table joins as they are already under one table in JSON format.
An example of how a vertically scalable relational database looks like (SQL)
An example of how a horizontally scalable non relational database looks like (JSON)
In the above example, the name Key consists of a Value named notebook. However, in the third row, the Key called “rating” consists of a couple of “Key-Value” in it. A Key might consist of ‘n’ number of sub “Key-Value.” This is one of the biggest advantages of JSON format where any number of details can be added without having to create attributes like in the SQL database.
MongoDB database is known for its quickness in operation and this is due to the presence of indexes. Each index represents a table which makes it easy to pull the data quickly. Indexes are common and available in all sorts of database management systems but what sets MongoDB apart is that it consists of secondary indexes which is much faster and helps in super fast querying.
With MongoDB, any level of hierarchical problems can be tackled. It supports rich and expressive object model which allows the database to query and represent any object from any level in your domain. This hierarchy might lead to a number of sublevels which allows your business to be more data oriented.
MongoDB has a built-in Aggregation framework to process extracting, transforming and loading on its own. This is mainly to transform the data that is being stored in the database. This might come in hand when your business deals with small amount of data, but when it involves millions and millions of records to deal with, the debugging process turns out to be complicated.
In order to deal with large volumes of data in the ETL process, a specialized tool must be used to work with your data in a seamless manner. This is where Sprinkledata comes into play, an analytic platform built for the cloud is capable of integrating data from any sources, combine datasets, automate data pipelines and provide actionable search driven insights. Moreover, with Sprinkledata, data from any database platform can be combined with any other database platform, this allows you to be flexible with your data collection formats.