Large companies and organizations have started to move most of their data into Non-Relational Databases. They are developed to store hundreds of Petabytes of data and run millions of queries per second. Relational Databases, on the other hand, are built to store and manage relational data as efficiently as possible.
However, Relational Databases have a hard time scaling as they require a lot of memory and compute power. Here is the importance of migrating data from a Relational Database to MongoDB, one of the most popular Non-Relational Databases.
This article will cover the details of replicating data along with mapping the basic relational concepts from a Relational Database to MongoDB. Let’s get started!
What is a Relational Database Management System?
The Relational Database Management System is a type of Database Management System specially designed for creating and managing Relational Databases. The RDBMS is a Software Application used to store, create, manage, query, and administer data in Relational Databases. So, the next question arises, “What are Relational Databases?” The Databases based on the relational model are defined as Relational Databases.
A Relational Database stores the data in multiple tables that are linked by a common field. A table also called a relation, is a set of data organized into rows and columns. There are many popular Relational Databases out there in the market like MySQL, SQL Server, Oracle, etc. The RDBMS provides an interface for users to interact with the applications and the database with the help of administrative functions for managing data storage, access, and performance. Most of the commercial Relational Database Management Systems use Structured Query Language (SQL) to access the Database.
Key Features of RDBMS
- RDBMS ensures that all the data is stored in tables in the form of rows and columns. Data from these tables can be retrieved using SQL.
- RDBMS is great at enforcing consistency and maintaining data integrity. It helps in maintaining data across applications and Database copies.
- In RDBMS, multiple tables are related to each other with the help of primary key and foreign key concepts. This makes data non-repetitive, and hence, there is no chance for Data Duplication.
- RDBMS supports virtual table creation for storage of sensitive data and simple execution of queries.
- RDBMS allows multi-user accessibility that can be controlled by individual users.
What is MongoDB?
MongoDB is an open-source, cross-platform, and Non-Relational Database Management System. Developed in 2009 by MongoDB Inc, MongoDB uses the document-oriented Database Model to group data into documents and collections rather than tables. This allows it to store different types of data. It supports multiple languages, comprises excellent features, and provides high performance.
Key Features of MongoDB
Below are some of the key features of MongoDB that can be attributed to its growing popularity.
- Schema-less: MongoDB is a Non-Relational Database that stores and uses documents and collections to retrieve data. With MongoDB, you are not required to create a table or pre-defined schema.
- Scalability: Unlike other SQL Databases, MongoDB uses horizontal scalability which allows users to generate clusters with real-time replication. MongoDB supports the Sharding process, which makes it easy to horizontally scale the data across multiple servers.
- Cost-Effective: MongoDB is a free, open-source Non-Relational Database Management program that allows you to store large data sets in a cost-effective manner.
- Code-native Data Access: MongoDB stores all the collected data in a JSON-like document which allows changes/modifications over time. This makes it easy for developers to retrieve information in the data structure using any programming language.
- High-Performance: MongoDB is fast and easy to use because of its NoSQL nature. Data can be stored, manipulated, and retrieved quickly without compromising Data Integrity.
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Let’s look at some of the salient features of Hevo:
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- Fully Managed: It requires no management and maintenance as Hevo is a fully automated platform.
- Data Transformation: It provides a simple interface to perfect, modify, and enrich the data you want to transfer.
- Real-Time: Hevo offers real-time data migration. So, your data is always ready for analysis.
- Schema Management: Hevo can automatically detect the schema of the incoming data and map it to the destination schema.
- Scalable Infrastructure: Hevo has in-built integrations for over 150 sources that can help you scale your data infrastructure as required.
- Live Monitoring: Advanced monitoring gives you a one-stop view to watch all the activities that occur within Data Pipelines.
- Live Support: Hevo team is available round the clock to extend exceptional support to its customers through chat, email, and support calls.
Key Differences Between an RDBMS and MongoDB
Below are a few key differences between RDBMS and MongoDB that will help you understand them better.
RDBMS vs MongoDB
|Model||RDBMS is a Relational Database Management System that works on Relational Databases.||MongoDB is a Non-Relational, NoSQL Database Management System that works on a Document-based Database.|
|Scalability||RDBMS is vertically scalable and its performance increases with an increase in RAM.||MongoDB is horizontally scalable as well and its performance increases with the addition of servers/processors.|
|Schema||In RDBMS, the schema needs to be defined in advance before one starts working with the Database.||MongoDB allows schemas to be dynamically created and accessed.|
|Joins||RDBMS possesses a great ability to query data and execute complex joins.||MongoDB doesn’t support complex joins execution.|
|Query Language||RDBMS supports SQL as a query language to query the Database.||MongoDB uses BSON to query the Database.|
|Hierarchical Data Storage||RDBMS is not suitable for storing hierarchical data.||MongoDB provides inbuilt support to store hierarchical data.|
Why Integrate a Relational Database to MongoDB?
Relational Databases came into existence 30 years ago and they have been the foundation of Enterprise Data Management since then. But in today’s world, processing data, building applications, and analyzing results have become much more complex. With data incoming from a variety of sources, organizations find it difficult to manage their increasingly complex user loads with traditional Relational Databases. And, this is the reason why it becomes necessarily important to migrate from a Relational Database to MongoDB. Setting an example, industry leaders such as Cisco, Verizon, etc., have migrated successfully from Relational Database to MongoDB.
MongoDB is a NoSQL solution and it doesn’t require a Relational Database Management System (RDBMS). New world engineering applications demand the persistence of complex and dynamic forms of data to match the highly flexible and powerful languages used for software development. MongoDB, the leading NoSQL Database, addresses the above challenges and meets the demands of modern apps with a flexible and scalable solution.
Here’s what you’ll be getting after migrating from a Relational Database to MongoDB.
- The Document Data Model provides you with the best way to work with data.
- A Distributed Systems Design allows you to intelligently put data wherever you want to.
- A unified MongoDB experience gives you the freedom to future-proof your work and eliminates vendor lock-in.
You can read our article on the 7 best real-world use cases of MongoDB.
The primary change in migrating from a Relational Database to MongoDB is the way in which the data is modeled. In MongoDB, different documents within a collection can have different schemas. For instance, one document can have 3 fields and the other document can have 5 fields. MongoDB supports dynamic schemas and there is no constraint on the data types of the fields.
Coming from the RDBMS background, one may find it difficult to transition from a pre-defined relational data model that stores data into tabular structures of rows and columns to a rich and dynamic document data model with embedded sub-documents and arrays.
For example, consider the following documents having different schemas within the same collection.
The first document contains the address and dob fields which are not present in the second document. On the other hand, the second document contains gender and occupation fields that are not present in the first document. Modeling this dynamic schema in RDBMS would require you to have 4 extra columns for address, dob, gender, and occupation. And, some of these columns would store null values, and hence occupy unnecessary space. MongoDB can easily design and model such complex schemas making it highly scalable in terms of design.
Mapping Tables, Rows, and Columns
In MongoDB, Databases consist of collections that are analogous to SQL tables in an RDBMS Database. Further, every MongoDB Collection stores data in the form of documents that correspond to rows in an RDBMS Table. While an RDBMS row stores data in a set of columns, a MongoDB document has a JSON-like structure (also known as BSON in MongoDB). And similarly, the fields in MongoDB are equivalent to the columns in RDBMS.
- MongoDB Collections are equivalent to RDBMS Tables.
- MongoDB Documents are equivalent to the RDBMS Rows.
- MongoDB Fields are equivalent to the RDBMS Columns.
This is what a MongoDB Document looks like. This document is equivalent to a single row in RDBMS. The only difference is that this is in JSON format.
Mapping Joins and Relationships
Coming from an RDBMS background, one must be familiar with relationships. Relationships in RDBMS are created by using primary and foreign key relationships, and by ultimately querying those using joins. On the other hand, relationships in MongoDB are developed using embedded and linking documents.
Consider an example wherein you need to store user information and corresponding contact information in the Database. Designing this in RDBMS would require you to have 2 tables, say user_information and contact_information with primary keys id and contact_id respectively. To establish a relationship, the contact_information table contains a user_id column which would act as a foreign key linking to the id field of the user_information table.
Now, let’s discuss how you can model such relational data in MongoDB using Linking Documents and Embedding Documents.
In MongoDB, you can use the auto-generated _id field as the primary key for identifying the documents uniquely.
You’ll need 2 collections to implement this, say user_information and contact_information both having their unique _id fields. Similar to the RDBMS case, the contact_information document contains a user_id field which links to the _id field of the user_information document.
The user_id field in the contact_information document is simply a field that holds some data, and all the logic associated with it has to be implemented by the user.
Another way of implementing relationships or relational data in MongoDB is to embed the contact_information document inside the user_information document as shown below.
This way, large complex documents, and hierarchical data can be embedded to relate entities.
As your business scales, the data to be embedded is expected to grow larger in size. Hence, it is recommended to use the Linking Documents approach rather than Embedding Documents Approach to avoid the document becoming too large. The Embedding Documents approach is generally used in cases where limited information needs to be embedded.
To summarize, the following Mapping Chart will help you easily migrate from a Relational Database to MongoDB.
Migrating Data from Relational Database To MongoDB
Now that you’ve designed the schema, you can move towards migrating data from a Relational Database to MongoDB. Integrating data from a Relational Database to MongoDB, however, can be a challenge. But the process becomes a lot easier with MongoDB drivers and tools.
Organizations have multiple options for migrating data from existing Relational Databases to MongoDB. The tool of choice largely depends on the use case, stage of the project, and the existing environment.
- Method 1: Many Developers are comfortable in creating their own migration scripts. These scripts are used to transform source Relational Database data into a hierarchical JSON structure. This can then be imported from Relational Database to MongoDB using the mongoimport tool.
You can have a look at our guide on how to connect PostgreSQL to MongoDB.
- Method 2: Third-party Extract Transform Load (ETL) tools are also generally used by companies to migrate data from a Relational Database to MongoDB. A number of ETL vendors including Informatica, Pentaho, and Talend have developed MongoDB Connectors that extract data from the source Database, transform it into the target MongoDB Schema, and then load it into MongoDB Collections. This is generally the recommended way of migrating from Relational Database to MongoDB.
- Method 3: Another way of migrating from a Relational Database to MongoDB involves parallelly running the existing RDBMS with the new MongoDB Database, incrementally transferring production data. The records are retrieved from RDBMS and are written back to MongoDB in the required document schema by the application.
So, this is how one can migrate from a Relational Database to MongoDB.
Relational Databases have been in existence for 30 years, but the way the applications are built and run in today’s world is pushing Relational Databases beyond their limits. This is the reason why NoSQL Databases like MongoDB have emerged tremendously in the last few years. MongoDB stores data into collections of self-describing JSON documents. MongoDB has become widely accepted as a leading Database owing to its dynamic schema, high scalability, and faster access.
This article introduced you to Relational Databases, MongoDB, and helped you differentiate between both. It then took you through various aspects of migrating from a Relational Database to MongoDB. Nowadays, many more companies want to associate themselves with MongoDB. To get a complete overview of your business performance, it is important to consolidate data from MongoDB and other Data Sources into a Cloud Data Warehouse or a destination of your choice for further Business Analytics. This is where Hevo comes in.
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Hevo Data with its strong integration with 150+ Sources & BI tools such as MongoDB, allows you to not only export data from sources & load data in the destinations, but also transform & enrich your data, & make it analysis-ready so that you can focus only on your key business needs and perform insightful analysis using BI tools.
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Share your experience of migrating from a Relational Database to MongoDB in the comments section below.