Connections Manager 
Import Wizard 
Collections Tab 
Visual Query Builder 
SQL Query 
SQL Connection Manager 
Team Sharing 
LDAP/ Kerberos
Export Wizard .svg){kind=icon}
SQL to MongoDB.svg){kind=icon}
MongoDB to SQL
Data Compare & Sync 
Task Scheduler 
Intellishell 
Schema Explorer 
AI Helper 
Aggregration Editor 
Query Code 
Query Profiler 
Index Manager 
Visual Explain 
Reschema 
Users and Roles Manager 
Data Masking 
Not having a good view of the data is a common pain point for developers and database administrators working with MongoDB deployments. While MongoDB’s flexible document model offers powerful data storage capabilities, maintaining data integrity becomes increasingly challenging as schemas evolve, especially without data visualization tools.
Here’s why data visualization is essential for working more effectively with MongoDB.
1. See the bigger picture
With pipelines that take data from a variety of object stores and data lakes, you need a modeling tool that helps you design and manage schemas across your entire data landscape. When your schema evolves due to changing business requirements, you don’t want to break your data pipeline.
If you’re looking for a data modeling tool that covers schema design for NoSQL databases, then try Hackolade or DbSchema. With their entity-relationship diagrams, you’ll be able to visualize the hierarchical structures of all your nested objects and arrays in your MongoDB databases.
2. Understand how your data is stored
Storing data incorrectly in your database can lead to inappropriate data types or invalid data being inserted that’s not easy to detect and fix further down the line. It’s no good if your application can’t read or find the data.
You can use MongoDB schema validation to create rules for your fields so that data is stored with the correct MongoDB data type, for example preventing dates being stored as string values.
Schema visualization tools pinpoint the fields with multiple data types and show you the extent of the problem by reporting the percentages of values for each data type.
3. Understand the structure of your collection data
Flexible schemas make it difficult to identify common patterns in the documents in a collection. The ability to add, remove or change fields means that documents have different structures and determining which fields are present in most of the documents is a challenge and it’s harder to write queries that perform well.
Schema visualization tools help you visualize the fields in a MongoDB collection and show you the percentage of documents in the collection that contain each field.
4. Troubleshoot performance discrepancies between test and production environments
When indexes are different in test and production environments, it makes it harder to reproduce performance problems and delays the time to diagnose and fix issues. Different indexing strategies can make performance problems show up in one environment but not the other. For example, missing indexes can result in slower query performance due to full table scans. Additional indexes can speed up read performance but slow down write operations.
You can visually compare indexes when you connect to your test and production clusters and open the required collection. Some index management tools allow you to display indexes side-by-side rather than inspecting them one at a time.
5. Check data is written correctly to collections
If you’re writing migration scripts in other applications to manipulate data in MongoDB, it’s really useful to have a visual tool that allows you to quickly inspect the data before you deploy the scripts in your production environment. Not only does this improve your workflow while developing and testing the scripts but it reduces the risk of errors when modifying existing data or inserting new documents into MongoDB collections.
6. Discover undesired data
To assist data cleanup and maintenance, a visual tool highlights potential data quality issues and incorrect data. Missing values, unintended duplicate fields with similar names, and misspelled field names are more obvious in a visual representation rather than looking at the raw data where inconsistencies can be overlooked. Seeing this undesired data makes it easier to spot patterns while troubleshooting and provides better understanding about the root causes of data quality issues.
In summary, visualizing data helps you work smarter by exposing the underlying data and its structure, allowing you to explore it and derive fresh insights. It facilitates troubleshooting and smoother deployments, contributing to more robust and efficient systems.
