Competition between companies is one such factor that will remain constant, as long as they offer similar products and services. This is pretty common in emerging and competitive fields such as Data Analytics. Having a majority customer share in the market and regularly updating your products and services does determine the revenue your organization receives. When it comes to the field of Database Management, the choice of MongoDB vs Snowflake is a relatively tough one.
MongoDB is a popular NoSQL database developed by MongoDB Inc. It is a source-available cross-platform document-oriented database program that uses JSON (JavaScript Object Notation)-like documents and optional schemas to store your data. Snowflake is a Cloud-based Data Warehousing company based in San Mateo, California. It was founded in July 2012 and launched in October 2014. Both these technologies are leveraged by organizations of all scales, both big & small, and depending on the situation, one can dominate over the other.
This article will provide you with a comprehensive guide on both technologies and highlight the key factors they differ in to help you make the MongoDB vs Snowflake decision easily. It also provides you with a brief overview of both databases. Read along how you can choose the correct database for your organization.
Table of Contents
Introduction to MongoDB
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MongoDB is a NoSQL database and has a document-oriented data model that stores data in the document with key-value pair and uses NoSQL query language to query data. MongoDB is a popular choice in organizations these days and an effective NoSQL database in the market.
NoSQL implies it doesn’t utilize rows and columns to store the information; instead, it stores the data in the reports and keeps a collection of records. The information put away in the record comprises many key-value matches and permits it to scale vertically and store them into a capacity design known as BSON (Binary Style of JSON report).
To learn more about MongoDB, click this link.
Introduction to Snowflake
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Snowflake is an ANSI-SQL-based Cloud Data Warehouse that uses leading cloud services like AWS, GCP, and Azure. Snowflake is entirely a Software-as-a-Service that means it handles all the hardware maintenance and performance tuning activities, thereby making the user a hassle-free experience.
To learn more about Snowflake, click this link.
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Factors that Drive the MongoDB vs Snowflake Decision
Now that you have a basic idea of both technologies, let us attempt to answer the MongoDB vs Snowflake question. There is no one-size-fits-all answer here and the decision has to be taken based on the business requirements, budget, and parameters listed below. The following are the key factors that drive the MongoDB vs Snowflake decision:
1) MongoDB vs Snowflake: Features
MongoDB contains a collection of documents, and each document can have varying fields depending upon the data stored. The two documents can contain the same structure or are different. MongoDB documents store the data in the form of key-value pairs. You can add as many key-value pairs as you want to your data and store them effectively in MongoDB.
The documents don’t need to comply with any schema, and you can add as many key and value pairs. The data model available within MongoDB allows you to represent hierarchical relationships, store arrays, and other complex structures more efficiently.
Snowflake uses partition and clustering techniques to optimize the query and enhance the performance. Snowflake caches every result so that when users re-execute the same query, it shows the cached result provided there is nothing changed on the data or the query side.
Snowflake allows secure data sharing through tables, views, UDF’s. Snowflake has extensive support for different file formats such as JSON, Avro, Orc, Parquet, and XML data. It also allows you to store semi-structured data in the data type called Variant.
Snowflake has good support for ANSI SQL and supports advanced SQL functionality like Merge, Lateral view, etc.
2) MongoDB vs Snowflake: Architecture
MongoDB has a NoSQL architecture built on collections and documents. It is based on the Shards Mongod servers that connect with Mongos and Clients to exchange data. MongoDB combines the capability of Relational Database and modern-day needs by providing flexible schemas, high availability, and scalability. The architecture of MongoDB is given below.
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Snowflake has the hybrid architecture of shared-disk and shared-nothing database architectures. It has a central data repository to access data from all the nodes and uses MPP (Massive Parallel Processing) to process the data individually in each cluster. This approach offers the data management simplicity of a shared-disk architecture and the performance and scale-out benefits of a shared-nothing architecture. The architecture of Snowflake is given below.
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3) MongoDB vs Snowflake: Database Model
MongoDB is a No-SQL database with a document-oriented data model and uses key-value pairs to store the data in a schema-less fashion. The addition of new key-value pairs is an easy process without disrupting the entire model.
Snowflake is a structured cloud data warehouse that uses the RDBMS concept of storing data, i.e. rows and columns, and stores structured and semi-structured data. Adding new columns requires re-setting of the entire schema, and hence it’s a tedious task.
4) MongoDB vs Snowflake: Pricing
MongoDB is open-source software and is available free to use on a shared cluster. You can find more about pricing here.
Snowflake is commercial software, and one has to subscribe to a plan before using them. However, it offers a 30 days trial version that you can use to explore around. Find out the trial link here. To get more detail about pricing plans, reach out here.
5) MongoDB vs Snowflake: Query & Programming Language
Being open-source, MongoDB supports various programming languages and allows developers to write code without any restrictions. Some of the popular programming languages it supports are – C, C++, Go, Groovy, Python, R, Ruby, Scala, and the list is long.
MongoDB uses MongoDB Query Language, commonly known as MQL. It supports CRUD operation, aggregations, and many more. To get in-depth knowledge about MQL, click here.
On the other hand, Snowflake allows only Python and Node.js to access its interface programmatically.
Snowflake uses Structured Query Language (SQL) to perform a query on the dataset. SQL has Data Definition Language(DDL), including Create, Alter, Drop table statements and Data Manipulation Language (DML) that includes Insert, Update, Delete.
6) MongoDB vs Snowflake: Schema Flexibility
MongoDB provides a flexible schema that allows you to handle dynamic changes in the content and store it in a hassle-free manner. In MongoDB, you can combine and store any data type without disrupting the existing schema, framework, and other applications.
Snowflake is amazing for data warehousing. It offers a structured schema to access the data that has a predefined and fixed schema. It doesn’t support dynamic schema.
7) MongoDB vs Snowflake: Performance
MongoDB provides excellent performance when it comes to unstructured data. As MongoDB stores data in documents, retrieval of data becomes faster than Snowflake, which stores data in rows and columns.
Snowflake has an excellent performance for huge volumes of data. Snowflake runs on the cloud, and you can choose among leading cloud providers to set up your framework.
Conclusion
This article gave a comprehensive analysis of the 2 popular Database technologies in the market today: MongoDB and Snowflake. It talks about both the databases and their features. It also gave the factors to judge each of the databases. Overall, the MongoDB vs Snowflake choice solely depends on the goal of the company and the resources it has.
Both MongoDB and Snowflake are best in their way. However, MongoDB seems to save a lot of time in the initial Database definition, whereas the well-defined, structured Snowflake database gives a reliable performance in some cases. Choosing a database purely depends on the business requirements and other factors such as organization structure, financial models, business types, etc.
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Vishal has a passion towards the data realm and applies analytical thinking and a problem-solving approach to untangle the intricacies of data integration and analysis. He delivers in-depth researched content ideal for solving problems pertaining to modern data stack.