Most enterprises 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.

But let's first start with the basics:

What is Big data?

Big data is a collection of large set of data records which might consist multiple collections 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 data extremely efficient because of certain functionalities, say

• Why the structured data is incapable of scaling horizontally?
• Why indexes are incapable of super fast querying?
• Why does this process involve a specific schema?
• Why SQL operations fail to do hierarchical querying?
• Why is this process incapable of supporting non-relational data?

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 of relational databases. 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.

What is MongoDB?

MongoDB is a popular document-oriented and open-source database platform capable of working with non-relational data. MongoDB provides several features that make it more suitable for certain applications than traditional SQL databases. Some additional features of MongoDB are:

1. High availability: MongoDB provides high availability through replica sets. In a replica set, multiple copies of the same data, called replicas, are distributed across different servers. If the primary replica fails, a new primary is automatically elected from the available replicas, ensuring that the system remains available even in the event of a failure.
2. Automatic sharding: MongoDB supports automatic sharding, which is distributing data across multiple machines. This makes it possible to scale horizontally, meaning the system can handle large amounts of data by adding more machines.
3. Flexible schema: MongoDB allows for a flexible schema, which means that you can store data of different structures in the same collection. This makes handling data that does not fit into a traditional relational database structure possible.
4. Document-oriented: MongoDB is a document-oriented database, meaning data is stored in documents rather than tables. Each document can have a different structure, and the data is stored in a format that is easy to access and manipulate.
5. Geospatial indexing: MongoDB supports geospatial indexing, which means you can store and query geospatial data such as coordinates and shapes. This makes it possible to build applications that require location-based services, such as maps and location-based search.

Non-relational data

MongoDB is a document oriented and open source database platform which is capable of working with non-relational data. This database server is also called as no SQL database program.

Any database program which works with SQL requires its tables to store nested data and 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 single collection 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.

Eg: ,,,

In this case, the same data from multiple documents 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 traditional relational databases or database management system it might be difficult to alter the tables because not all the elements of a product have the same generic values.

Horizontal scaling

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 data consistency throughout all, hence the database has 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 create multiple databases, manage data, and sync all the tables into one because it consumes a lot of time.

Schema-less

However, with MongoDB, as all the data is just unstructured data 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 the data storage 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 key value pairs 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.

Secondary Indexes

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 relational database tables and management systems but what sets MongoDB apart is that it consists of primary and secondary indices of indexes which is much faster and helps in super fast querying.

Hierarchical Querying

With MongoDB, any level of hierarchical problems can be tackled. It supports rich and flexible data model 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 high volume data storage with 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 data availability any other database platform, this allows you to be flexible with your data collection formats.

FAQs

Q: What is MongoDB?

A: MongoDB is a document-oriented NoSQL database that stores data in a flexible, semi-structured format called BSON (Binary JSON).

Q: What are some advantages of using MongoDB over SQL databases?

A: MongoDB offers several advantages over SQL databases, including:

1. Schema flexibility: MongoDB's document model allows for dynamic schemas, which can accommodate changes in data structures without requiring a schema update.
2. Scalability: MongoDB can scale horizontally across multiple servers or clusters, which makes it easier to handle large datasets and high traffic loads.
3. Query performance: MongoDB's query language and indexing system are optimized for fast and efficient queries on large datasets.
4. Availability and fault tolerance: MongoDB supports replica sets and sharding, which provide high availability and fault tolerance for mission-critical applications.
5. Developer productivity: MongoDB's JSON-based document model is more natural for developers to work with than SQL tables, which can reduce development time and complexity.

Q: Does using MongoDB require special skills or training?

A: While MongoDB may require some learning and adjustment for developers who are used to more traditional databases working with SQL databases, it is generally considered easy to use and well-documented. MongoDB also offers extensive online resources, training, and support for developers getting started with the platform.

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