NoSQLBooster For MongoDB Simplified: A Comprehensive Guide 101

By: Published: February 16, 2022

If you are a Database Administrator or Developer, you must be aware of the fact that different SQL statements are required for Creating Schemas, Ad-hoc Querying, Initiating Backups, and Troubleshooting. Finding the appropriate GUI-based Database Management tool for such scenarios can help you complete your tasks faster and more efficiently. NoSQLBooster is one such tool that can offer SQL functionalities for your MongoDB database.

Upon a complete walkthrough of this article, you will gain a decent understanding of MongoDB and NoSQLBooster along with the unique features that they offer. You will also learn about the steps required to set up NoSQLBooster on your workstation and how you can leverage it to run SQL queries against MongoDB Collections.

Table of Contents

What is NoSQLBooster?

NoSQLBooster is one of the most popular cross-platform GUI (Graphical User Interface) tools for MongoDB that houses a built-in MongoDB Script Debugger, Comprehensive Server Monitoring Tools, Query Code Generator, and advanced IntelliSense support. NoSQLBooster aids in the translation of MongoDB Queries into various languages such as Java, Python, C#, and others. Furthermore, it enables users to use NPM packages within MongoDB Shell Scripts. This MongoDB tool appears as a tooltip as you type to recommend completions, keywords, methods, variables, etc.

Key Features of NoSQLBooster

Some of the key features of NoSQLBooster are discussed below:

  • Chaining Fluent Query Interface: NoSQLBooster for MongoDB includes a fluent Query Builder API similar to Mongoose, which allows you to construct a query using chaining syntax rather than specifying a JSON Object.
  • Collection Re-Schema Tool: The new re-schema Tool provides a Graphical User Interface for updating the MongoDB Collection Schema. It is essentially a code generation tool, similar to the other tools provided by NoSQLBooster, that helps you generate code to update the collection with a few clicks, and then you can use the generated code as a foundation for more complex programming, save it as a script file, and even add automated tasks.
  • One-Click Grouping/Filtering: The handy One-Click grouping feature of NoSQLBooster allows you to group the selected field in query results to calculate Counts, Totals, Minimums, Maximums, and Averages. It enables the user to filter query results based on the selected field-value pairs.
  • MongoDB Log Parser: NoSQLBooster offers two MongoDB Log Viewers- one for parsing and displaying the most recent logged MongoDB events, and another for parsing and displaying external MongoDB log files. NoSQLBooster quickly parses the log and output general information about its contents, such as the timestamp, severity, component, context, and command-specific messages. It also enables you to save parsed log entries to MongoDB’s collection for further analysis and querying using MongoDB’s find() method.
  • Test Data Generator: NoSQLBooster houses an in-built Test Data Generator tool that allows users to create random fake data for testing purposes. You can generate as many samples as you need with fake data, ensuring that what you see is statistically significant rather than an outlier. Another advantage of generating fake data is that anyone can replicate your work and verify (or extend) your findings. Real data cannot always be distributed due to restrictions imposed by the data’s owner. Fake data, on the other hand, is never restricted. You just need to make sure you explain the data generation process clearly enough so that others can replicate what you’ve done.

What is MongoDB?

MongoDB is a well-known Open-Source NoSQL Database written in C++. MongoDB is a Document-oriented Database that uses JSON-like documents with a Dynamic Schema to store data. It means that you can store your records without having to worry about the Data Structure, the Number of Fields, or the Types of Fields used to store values. Documents in MongoDB are similar to JSON Objects.

You can change the structure of records (which MongoDB refers to as Documents) by simply adding new fields or deleting existing ones. This feature of MongoDB allows you to easily represent Hierarchical Relationships, Store Arrays, and other complex Data Structures. Nowadays, many tech giants, including Facebook, eBay, Adobe, and Google, use MongoDB to store colossal amounts of data.

Key Features of MongoDB

MongoDB offers a wide range of unique features that make it a better solution in comparison to other conventional databases. Some of these features are discussed below:

  • Schema Less Database: A Schema-Less Database allows various types of Documents to be stored in a single Collection(the equivalent of a table). In other words, in the MongoDB database, a single Collection can hold multiple Documents, each of which can have a different number of Fields, Content, and Size. It is not necessary for one document to be similar to another which is a prerequisite in Relational Databases. Due to this feature, MongoDB offers great flexibility to the users.
  • Index-based Document: Each field in a MongoDB Database is indexed with Primary and Secondary Indices, which makes it easier to retrieve information from the pool of data. 
  • Scalability: Sharding in MongoDB allows for Horizontal Scalability. Sharding refers to the process of distributing data across multiple Servers. A large amount of data is partitioned into data chunks using the Shard Key, and these data chunks are evenly distributed across Shards that reside across many Physical Servers.
  • Replication: MongoDB offers high availability of data by creating multiple copies of the data and sending these copies to a different Server so that if one Server fails, the data can still be retrieved from another Server. 

Simplify MongoDB ETL & Analysis Using Hevo’s No-code Data Pipeline

Hevo Data is a No-code Data Pipeline that offers a fully managed solution to set up Data Integration from 100+ Data Sources (including 40+ Free sources) and will let you directly load data from sources like MongoDB to a Data Warehouse or the Destination of your choice. It will automate your data flow in minutes without writing any line of code. Its fault-tolerant architecture makes sure that your data is secure and consistent. Hevo provides you with a truly efficient and fully automated solution to manage data in real-time and always have analysis-ready data. 

Get Started with Hevo for Free

Let’s look at some of the salient features of Hevo:

  • 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 maps it to the destination schema.
  • Connectors: Hevo supports 100+ Integrations to SaaS platforms FTP/SFTP, Files, Databases, BI tools, and Native REST API & Webhooks Connectors. It supports various destinations including Google BigQuery, Amazon Redshift, Snowflake, Firebolt, Data Warehouses; Amazon S3 Data Lakes; Databricks; and MySQL, SQL Server, TokuDB, MongoDB, PostgreSQL Databases to name a few.  
  • Secure: Hevo has a fault-tolerant architecture that ensures that the data is handled in a secure, consistent manner with zero data loss.
  • Hevo Is Built To Scale: As the number of sources and the volume of your data grows, Hevo scales horizontally, handling millions of records per minute with very little latency.
  • 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.
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How to Setup NoSQLBooster?

Before you start configuring or debugging a MongoDB Script, you need to install and set up NoSQLBooster for MongoDB. Follow the steps given below to do so:

  • Click here to download NoSQLBooster for your preferred operating system.
NoSQLBooster Downloads Page
Source: Self
  • Navigate to the Downloads section of your browser and open the downloaded file.
  • Follow the instructions on the Installation Wizard and keep on clicking Next.
  • Once you have successfully installed NoSQLBooster, open it and click on the Connect option to create a new connection.
  • The Connection Editor will automatically fetch details from the MongoDB service and once all the appropriate fields have been filled, click on the Save and Connect button.

How to use NoSQLBooster for MongoDB?

MongoDB does not natively support SQL features but with a GUI tool like NoSQLBooster for MongoDB, you can easily execute SQL queries against MongoDB Collections. NoSQLBooster for MongoDB validates and converts the SQL Query into a MongoDB Query(MQL) before executing it. So, instead of writing complex JSON-like scripts, you can easily manipulate the required data using a set of simple SQL statements.

Let us understand this with the help of an example:

Consider a collection named employees consisting of fields like number, first_name, last_name, salary, department, and hire_date.

Now, insert the following demo data into the employees collection:

 {"number":1001,"last_name":"Smith","first_name":"John","salary":62000,"department":"sales", hire_date:ISODate("2016-01-02")},
 {"number":1002,"last_name":"Anderson","first_name":"Jane","salary":57500,"department":"marketing", hire_date:ISODate("2013-11-09")},
 {"number":1003,"last_name":"Everest","first_name":"Brad","salary":71000,"department":"sales", hire_date:ISODate("2017-02-03")},
 {"number":1004,"last_name":"Horvath","first_name":"Jack","salary":42000,"department":"marketing", hire_date:ISODate("2017-06-01")},

Once you have successfully prepared a set of demo data, you can query all the fields of the collection using SQL statements. All you have to do is, click on the SQL Query Tab and you will see a default SQL Select Statement on the editor:

SQL Query Tab
Image Source

You can also retrieve some specific fields in ascending or descending order. The following example fetches the first name, last name, and salary fields from the employees collection and sorts the results by salary in descending order.

Image Source


This article introduced you to NoSQLBooster for MongoDB and the salient features that it offer. Furthermore, it introduced you to the steps required to get started with NoSQLBooster. As your business begins to grow, data is generated at an exponential rate across all of your company’s SaaS applications, Databases, and other sources. To meet this growing storage and computing needs of data,  you would require to invest a portion of your engineering bandwidth to Integrate data from all sources, Clean & Transform it, and finally load it to a Cloud Data Warehouse for further Business Analytics. All of these challenges can be efficiently handled by a Cloud-Based ETL tool such as Hevo Data.

Visit our Website to Explore Hevo

Hevo Data, a No-code Data Pipeline provides you with a consistent and reliable solution to manage data transfer between a variety of sources like MongoDB and a wide variety of Desired Destinations, with a few clicks. Hevo Data with its strong integration with 100+ sources (including 40+ free sources) allows you to not only export data from your desired data sources & load it to the destination of your choice, but also transform & enrich your data to make it analysis-ready so that you can focus on your key business needs and perform insightful analysis using BI tools.

Want to take Hevo for a spin? Sign Up for a 14-day free trial and experience the feature-rich Hevo suite first hand. You can also have a look at the unbeatable pricing that will help you choose the right plan for your business needs.

Share with us your experience of learning about NoSQLBooster for MongoDB in the comments below!

Former Research Analyst, Hevo Data

Rakesh is a Cloud Engineer with a passion for data, software architecture, and writing technical content. He has experience writing articles on various topics related to data integration and infrastructure.

No Code Data Pipeline For MongoDB