SQL vs NoSQL – Who owns this new era of data and why?

SQL and NoSQL are two different types of database management systems with different purposes and different characteristics. Is one of these better than the other? 

We asked SingleStore’s SVP of Product, Yatharth Gupta for his thoughts. 

Q1: Yatharth, can you describe some of the differences between SQL and NoSQL databases? 

A: For sure, let us consider each of these categories of databases based on a few, key dimensions. 

First, the data model: SQL databases are based on the relational data model, which means that data is structured and stored in tables with predefined schemas, where each row represents a record and each column represents a field. Every row has the exact same columns. NoSQL databases, on the other hand, are much more flexible when it comes to schemas. Each record, known as a document, can have any number of key-value pairs providing flexibility to what data is stored. Additionally data can also be stored in a column family or graph in NoSQL databases. 

Second, consistency: SQL databases are known for their strong consistency model, which ensures that all nodes in the database cluster have the same view of the data at all times. NoSQL databases may have different consistency models, such as eventual consistency, which allows for faster reads and writes but may lead to data inconsistencies in some cases.

Third, query language: SQL databases use a standard SQL language to query the data, which provides a rich set of features for filtering, sorting, aggregating, and joining data from multiple tables. NoSQL databases use their own query language, which may be more limited in functionality but provides better performance for specific data models.

Does that help? 

Q2: Definitely helps. Thank you. Can you tell us more about what the primary strengths of each system are and share some examples as to when you would use each?

A: SQL databases manage data in tables and are ideal for performing complex queries and joins. As such, SQL databases can be particularly useful for businesses that need to run complex analytics on large volumes of data. They are also ideal for powering modern applications that need to provide interactive analytics (in-app analytics) to their users. Further, many business intelligence (BI) tools have built-in connectors to SQL databases, which makes it easy to extract and analyze data using these tools. SQL databases are often a better choice for businesses that have well-defined schemas and data models.

For NoSQL applications, a primary benefit is the ease of getting started since you don’t have to define your schema in advance. So, NoSQL databases are often a better choice for businesses that need to be flexible and agile with their data models, as they can easily accommodate changes in data structure and schema. You’ll often see NoSQL databases used for marketing analytics, social media analytics, and content management systems.

Q3: How do SQL or NoSQL databases relate to Generative AI and Vector Databases?

A: Generative AI has recently become very popular and we have a lot of customers asking questions about this. But, Generative AI is not about the choice between SQL or NoSQL. To understand Generative AI, lets think about building an application similar to ChatGPT. We would first take a large corpus of data and store that data in a database. Second, we would tokenize that data in smaller chunks or convert that into vectors which again can be stored in a database. Third, you would create embeddings for each of the tokens which can be done using a LLM. Embeddings are essentially N dimension vector arrays. You need to store them and when a question is asked, you can leverage multiple techniques including cosine similarity to find the closest answer. SingleStoreDB supports vectors and vector similarity search using dot_product (for cosine similarity) and euclidean_distance functions. These functions are used by our customers for applications including facial recognition, visual product photo search, and text-based semantic search. We strongly believe that you don’t need a specialized vector database specifically for this, an enterprise grade database that supports vector functionality can do a great job.

Q4: So which is better? SQL or NoSQL? 

A: Each system has their strengths, weaknesses, and ideal use cases. Organizations should choose a database that matches the capabilities they need. For example, if you need to do analytics, SQL databases are a much better choice. That said, I think we will see the traditional divide between these two applications shrink. There are too many smart people developing innovative products for us to not find a way to better bridge each of these systems’ strengths with the other. For one example, I believe someone could soon find a way to do analytics in NoSQL databases. But we’ll see, and this is definitely a development in the database market to keep your eyes tuned to. 


Yatharth Gupta is Senior Vice President, Product Management at SingleStore. Yatharth oversees new product development and manages existing and prospective customer relationships. Prior to joining SingleStore, Yatharth spent 14 years at Microsoft driving vision, strategy and execution across a diverse set of consumer and enterprise products including Azure Databricks, building businesses with $400+ million in revenue. Yatharth received his BS in Electrical and Computer Engineering from Virginia Tech. He also holds a GMP from Harvard Business School. 

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