NoSQLBooster - MongoBooster - A Shell Centric Cross Platform GUI Tool For MongoDB
By Brian Laleye · October 12, 2022 · 7 min read
NoSQLBooster for MongoDB, formerly called MongoBooster, is one of the shell-centric cross-platform GUI tools for MongoDB v2.6-3.6.
It helps correct the syntax of ES2017 to get significant intelligence experience whose app falls in the development category.
There are various well-functioning alternatives of NoSQLBooster for MongoDB, giving an excellent performance on multiple platforms, including Mac, Windows, Linux, and MongoDB.
The one which is counted as one of the best alternatives is Studio 3T which is free of cost to use.
Some of the other best apps which work similarly to NoSQLBooster for MongoDB are MongoDB Compass, MongoHub, Nosqlclient, Mingo.io, etc.
To become a skilled Database Administrator or Developer, it is recommended to have in-depth knowledge of multiple SQL statements for creating schemas, initializing backups, troubleshooting, Ad-hoc querying, etc.
For all these purposes of getting these in one tool, you need to look for the best GUI-based Database Management tool that can productively help complete your task efficiently and faster.
NoSQLBooster is one such type of tool that imparts all the crucial SQL functionalities for managing the MongoDB database.
In this article, we share with you the meaning and all the features of MongoDB and NoSQLBooster.
What is MongoDB?
MongoDB is one of the best open-source NoSQL databases, which is completely written in C++.
Moreover, we can also define MongoDB as a document-oriented database that uses JSON with the help of dynamic schema for storing data.
We can say that you can keep records without much issue with Data Structures, the Number of Fields, and the type of fields to store values. Documents in MongoDB are all similar to JSON objects.
MongoDB: Features Overview
There are multiple features of MongoDB that provide an excellent solution compared to other conventional databases.
Some of its best features are discussed below:
Sharding in MongoDB provides the best feature of horizontal expansion.
The concept of sharding refers to the process by which you can simultaneously distribute data with various servers.
Moreover, most of the data is separated into multiple data chunks with the help of the Shard key.
MongoDB creates large amounts of data by creating various copies and forwarding the documents to multiple servers.
Unfortunately, if one server fails, the data will be retrieved using a different server.
- Creating an index-based document
In MongoDB, each field is indexed with primary and secondary indices, which helps retrieve crucial information from the large data collection.
- Using feature schema-less database
The feature of a schema-less database gives the option of storing multiple types of documents in a single aggregate form which is equivalent to a table form.
In simple words, the MongoDB database holds various documents simultaneously.
But the merit in this is that it is not required to have one document similar to another, which is a basic prerequisite in Relational databases.
With this feature, MongoDB imparts great flexibility to its users.
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What Exactly is NoSQLBooster?
As already described in the above part of the article, NoSQLBooster is one of the cross-platform GUI tools for the intelligence use of MongoDB.
This tool has an in-built feature of MongoDB:
- Script Debugger
- Query Code Generator
- Comprehensive Server Monitoring tools
- Advanced Intelligence support.
NoSQLBooster also possesses an excellent feature of translating MongoDB queries in multiple languages like Java, C#, Python, etc.
Moreover, this tool has an inbuilt feature of activating NPM packages within MongoDB shell Scripts.
NoSQLBooster: Features Overview
There are multiple excellent features of NoSQlBooster that make it a unique tool among all its alternatives:
1. Filtering (One-click Grouping)
With this feature, NoSQLBooster helps you aggregate all the selected fields in query results to calculate a total number of counts, maximums, minimums, and averages.
Users can also filter the query results by selecting field-value pairs.
2. MongoDB Log Parser
NoSQLBooster tool provides two MongoDB Log viewers:
a- for parsing and displaying the recently logged MongoDB events
b- for displaying external MongoDB log files
The tool instantly parses the log and shows the general information as input in output, like:
- Command-specific texts
NoSQLBooster also persists in saving the parsed log entries in your MongoDB’s collection for future analysis, if any, you can query with the help of the find() method in MongoDB.
3. Chaining Fluent Query Interface
NoSQLBooster for MongoDB comprises an excellent fluent Query Builder API like Mongoose.
With the help of this feature, you can build a query by using proper chaining syntax other than specifying a JSON object.
4. Generating Test Data
NoSQLBooster comprises the excellent inbuilt developed feature of the Test Data Generator tool, with the help of which, users can generate sample data for testing.
You can create as many samples as you want with fake data that can look at least statistically featured more than simply outlier.
Moreover, the next important benefit of generating fake data is that anyone can easily copy your work and replace it with his name, which verifies its findings.
5. Re-Schema Tool
The recently released tool of re-schema is the best since it provides an excellent GUI to update MongoDB collection schema.
It is one of the code generation tools offered by NoSQLBooster, which all help generate the code to update the collection with simple clicks.
In this way, a resultant generated code is made, which allows for complex programming, or you can also save a script file and add automated tasks.
In this article, you have been introduced to the NoSQLBooster for MongoDB. Everything to know about the NoSQLBooster, along with its key features.
By being data-driven, it is obvious that data in your databases would grow exponentially.
Thus to meet the demand of this ever-increasing storage, it is of utmost importance for you to invest part of your engineering bandwidth in integrating data from all sources and then clean & transform it, which at least you need to load on the cloud data warehouse for business analysis.
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