MongoDB, founded in 2007 by Dwight Merriman, Eliot Horowitz, and Kevin Ryan, uses flexible documents and collections to store your data. But how do you get a high-level view of your stored documents? Show
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A schema is what defines the structure and contents of your data in visual formats that make it easy for developers and data engineers to keep track of information. Using MongoDB schema designer tools, you can:
In this blog, we feature the top 7 MongoDB schema designer tools to help you build schemas in MongoDB for your applications. We will also share why schemas are important and some best practices for using MongoDB schema designer tools. Our curated list also includes tools that offer code auto-generation and MongoDB ER diagrams. Read on to get started. Table of Contents
What is MongoDB?MongoDB is a non-relational (NoSQL) database program. In a broad sense, MongoDB is a database without a schema; we cannot define a schema in MongoDB. Image SourceIf you work with big data, you know that fitting diverse data into a rigid relational model is a pain in the neck. SQL databases or Relational Databases (RDBMS) store information in rows and columns with a pre-defined schema that is not quite fit for storing large data volumes. Non-relational or NoSQL databases like MongoDB contain schemas that are dynamic, so developers can change them “on the fly.” MongoDB has emerged as a superior option to SQL databases with an acute focus on better scaling and fast queries. Because of such dynamic schema architecture, MongoDB allows for frequent application changes and makes programming simpler for developers. For developers MongoDB is a plus since it provides official support for all the popular languages— C, C++, C#, and .Net, Go, Java, Node.js, Perl, PHP, Python, Motor, Ruby, Scala, Swift, Mongoid. This allows developers to work with their favorite languages hence leading to faster development time and fewer bugs. To learn more about MongoDB, and its contrast to SQL Databases, visit our helpful guide here- MongoDB vs SQL Databases: 4 Comprehensive Aspects. For more information on the essential use-cases of MongoDB, visit our other guide on Best MongoDB Visualization, Reporting & Analytics Tools for 2022: The Must-Know List. Key Features of MongoDB
As the ability of businesses to collect data explodes, data teams have a crucial role to play in fueling data-driven decisions. Yet, they struggle to consolidate the scattered data in their warehouse to build a single source of truth. Broken pipelines, data quality issues, bugs and errors, and lack of control and visibility over the data flow make data integration a nightmare. 1000+ data teams rely on Hevo’s Data Pipeline Platform to integrate data from over 150+ sources in a matter of minutes. Billions of data events from sources as varied as SaaS apps, Databases, File Storage and Streaming sources can be replicated in near real-time with Hevo’s fault-tolerant architecture. What’s more – Hevo puts complete control in the hands of data teams with intuitive dashboards for pipeline monitoring, auto-schema management, and custom ingestion/loading schedules. All of this combined with transparent pricing and 24×7 support makes us the most loved data pipeline software on review sites. Take our 14-day free trial to experience a better way to manage data pipelines. Get started for Free with Hevo! Why Do You Need MongoDB Schema?The biggest selling point for MongoDB is its schema-less architecture. MongoDB allows developers to create collections (analogous to tables in the language of RDBMS) implicitly and add attributes to those collections on demand. This simplifies agile development and DevOps. But if MongoDB is schema-less and useful in such a state, why would you need to reinforce schema? The truth is that 80% of your web applications require access to organized data. If you are storing your information into random JSON documents, applications will have a hard time acquiring it. Without the MongoDB data modeling tools, data validation becomes nearly impossible. Certain types of CRM applications always require you to save data in prescribed formats. How your data is dispersed throughout documents determines how hard it is for an application to obtain or analyze data. So, to maintain structure, reduce preventable bugs and allow for cleaner code, schemas are a necessity. Designing your own MongoDB schema is a long process and takes judgment, experience, and experimentation to create the perfect data model. You have to consider performance, consistency, document size, application read/writes, and how your data will grow and scale in the future. The only thing that counts when creating a MongoDB schema is that you create one that will be useful for your application. If the applications are utilized differently, two separate apps that use the exact same data may have significantly different schemas. The following should be taken into account while building a schema: Luckily, there are a few MongoDB schema designer tools in the market like Hackolade, Moon Modeler, DbSchema, etc. to help you design your MongoDB schema with their GUI. Have a look at these tools in the upcoming section. Here’s a list of 7 best MongoDB schema designer tools with useful GUI, that can help you enforce schema to your MongoDB database: HackoladeHackolade is a popular and feature-rich MongoDB schema designer tool that makes it easy to create and enforce schema with MongoDB’s JSON Schema Validator. This MongoDB schema designer tool can repeatedly alter the schema as your applications evolve, and it provides you the tools you need to create and manage data models and schemas for effective application modernization. Key Features of MongoDB Schema Designer Tool
PricingFor individuals, Hackolade offers a free community version, a pro-edition trial for 14 days, and a personal edition for €200. For organizations, Hackolade offers a pro-edition trial for 14 days, and subscription-based plans that start at €100/month. LinkHackolade Moon ModelerImage SourceIn addition to being a MongoDB schema designer tool, Moon Modeler supports data modeling for PostgreSQL, MariaDB, MySQL, SQLite, and GraphQL. It features an elegant and classy user interface with drag and drops features and dark mode. You also get to visually design new schema, collections, documents, and data models and define relationships between them. Key Features of MongoDB Schema Designer Tool
PricingMoon Modeler offers two paid plans for data modeling and visualization:
LinkMoon Modeler DbSchemaImage SourceDbSchema is a MongoDB client & schema validation designer. It is an intuitive design tool that can be used for your complex databases and documentation. Using its simple-to-use visual tools, developers, database administrators, and decision-makers can easily query, explore and manage their data in MongoDB databases. Key Features of MongoDB Schema Designer Tool
PricingDbSchema offers two editions for data modeling and visualization:
LinkDbSchema DataedoImage SourceDataedo is a MongoDB data modeling tool with a rich text editor and schema change tracking. Using Dataedo, you can discover collections and document structures in your MongoDB database in a clear tabular view with a few simple clicks. You can also visualize your data models in ER diagrams and describe document references. When you’ve finished a piece of documentation, you may utilize its HTML sharing capabilities to distribute it to other team members or members. Key Features of MongoDB Schema Designer Tool
PricingDbSchema offers three plans:
LinkDataedo DBeaverImage SourceDBeaver is your go-to option if you’re seeking a free MongoDB schema designer tool without the obligations of premium software. This Mongo DB designer offers an extensive set of features and multi-platform support for developers, database administrators, analysts, and all people who need to work with databases. It also supports SQL databases like MySQL, PostgreSQL, SQLite, Oracle, DB2, SQL Server, and many more. Key Features of MongoDB Schema Designer Tool
PricingDBeaver is open source, and free to use MongoDB schema design tool for all. LinkDBeaver Aqua Data Studio Entity Relationship ModelerImage SourceAqua Data Studio Entity Relationship Modeler (ER Modeler) is a MongoDB schema design tool and a data modeling tool for your MongoDB databases. It includes features like “forward engineer” to model entities and “reverse engineer” to visualize database models for your existing databases. There’s also a schema capture tool functionality in this MongoDB ER diagramming tool that allows you to compare multiple ER Models. Key Features of MongoDB Schema Designer Tool
PricingThe versatile IDE, Aqua Data Studio offers two pricing options for data management and visual analytics:
LinkAqua Data Studio Idera ER/StudioImage SourceIdera ER/Studio is a Mongo DB designer and data governance tool with a clean user interface. It helps to forward engineer, reverse engineer, analyze, and optimize your current MongoDB databases with support for data collaboration and sharing. Because of its comprehensive model management and collaboration features, your teams get to work on complex databases with ease. Key Features of MongoDB Schema Designer Tool
PricingER/Studio offers two customized software packages:
LinkER/Studio Using manual scripts and custom code to move data into the warehouse is cumbersome. Frequent breakages, pipeline errors, and lack of data flow monitoring make scaling such a system a nightmare. Hevo’s reliable data pipeline platform enables you to set up zero-code and zero-maintenance data pipelines that just work. Reliability at Scale – With Hevo, you get a world-class fault-tolerant architecture that scales with zero data loss and low latency. Monitoring and Observability – Monitor pipeline health with intuitive dashboards that reveal every stat of pipeline and data flow. Bring real-time visibility into your ELT with Alerts and Activity Logs Stay in Total Control – When automation isn’t enough, Hevo offers flexibility – data ingestion modes, ingestion, and load frequency, JSON parsing, destination workbench, custom schema management, and much more – for you to have total control. Auto-Schema Management – Correcting improper schema after the data is loaded into your warehouse is challenging. Hevo automatically maps source schema with destination warehouse so that you don’t face the pain of schema errors. 24×7 Customer Support – With Hevo you get more than just a platform, you get a partner for your pipelines.Discover peace with round-the-clock “Live Chat” within the platform. What’s more, you get 24×7 support even during the 14-day full-featured free trial. Transparent Pricing – Say goodbye to complex and hidden pricing models. Hevo’s Transparent Pricing brings complete visibility to your ELT spend. Choose a plan based on your business needs. Stay in control with spend alerts and configurable credit limits for unforeseen spikes in the data flow. Sign Up Here for a 14-day free trial! Some Best Practices for Using MongoDB Schema DesignerYou have to make considerations when using Mongo DB designer tools because the way your data is structured is instrumental for applications that depend on it. Schema design is critical for improving the performance and scalability of your MongoDB database. As more and more applications get added to your roster, it’s a good practice to design a schema that’s organized, so that you don’t have to reinvent the wheel again. Here are some recommended practices to follow in MongoDB schema designer to guarantee that your data is properly structured and utilized: Favor Embedding Over Referencing, Unless NeededEmbedding is about using one to few/many relationships in your data. As an example, if you are storing multiple phone numbers for the same person in your database, you might want to embed this information in the same document, so you can retrieve it all at once in a single query. Referencing on the other hand is dividing information into multiple documents and linking them together using the document’s unique object ID and $lookup operator (similar to JOIN operator in SQL query). You’ll have smaller documents and less data duplication by dividing up data, but you’ll need at least two queries or $lookup operators to access all of the data in the linked documents. Don’t Embed Objects Requiring Independent AccessEmbedding independent objects that are usually large in size would create an unnecessary hassle for your existing database model. Try and keep them separate from your embedded documents. Prefer Referencing for One-to-Squillions and Many-to-ManyIf you have an event logging system that collects log messages for different machines, there’s a high chance that any host can generate enough messages to overflow the 16 MB MongoDB document limit size. This creates millions of subdocuments under a one or one-to-squillions relationship. So, rather than keeping track of the relationship between the host and the log message in the host document (which calls for a one-to-squillions relationship), let each log message keep track of the host with which it is related. In such cases, you no longer have to worry about an unbounded array interfering with your application because the data is getting stored in the log. Similarly, for many-to-many relationships, for example between your customer and products (a customer can purchase various products, and a product can be purchased by many customers), you need referencing, to be able to relate one customer to many products and one product to many products. For more information on modeling document schema in MongoDB, you can refer to this extensive guide by DigitalOcean-How To Design a Document Schema in MongoDB. Also check out our other blog on Top 6 MongoDB GUI Tools to use in 2022 Simplified: A Comprehensive List. ConclusionOverall, we hope this blog has helped clarify MongoDB schemas and their requirements with MongoDB data modeling tools at your disposal. While MongoDB advertises no formal process and no rules for building a schema, there are tons of possibilities that you can explore while creating your own. A good MongoDB schema designer and ER designer tool can make your MongoDB database a truly powerful and scalable model that works completely unique and best for your application. With burgeoning applications and the data associated with them, there comes a need to integrate and store data in truly accessible ways, using Cloud Data Warehouses. While you can build your own ETL pipelines, and extract data from your sources, why not deploy Hevo to extract, load, and transform your data in minutes? 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. Visit our Website to Explore Hevo Hevo Data with its strong integration with 150+ 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.
Why not give Hevo a try? Sign Up here for a 14-day free trial and experience the feature-rich Hevo suite firsthand. You can also check our pricing and make a decision on your best-suited plan. Post your comments on learning about MongoDB schema design or MongoDB schema designer tools below. We’d like to hear your thoughts and ideas. |
