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dicecamp · 11 months ago

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SQL and NoSQL: Differences, Use cases and Databases

SQL and NoSQL are counter technologies meant to accomplish the same business goals. The difference mainly exists in the autonomy to ‘explore data deeply’ and ‘ease of scalability’

SQL has existed as a widely accepted industry standard for so long now. Its counterpart; NoSQL, emerged almost 12 years ago when the world needed a disparate system that could process unstructured data as well as comply with increasing storage and power requirements.

The rise of NoSQL almost diminished the decade old SQL paradigm of relational database management system (RDBMS) giving rise to non relational database systems.

Now as we see, SQL didn’t go away, instead its potential to manipulate data in databases is increasingly realized.

In this article, we explore sql and nosql difference, sql vs. nosql use cases, and comparison of databases.

SQL and NoSQL Difference

The differences in SQL and NoSQL exist among four key parameters: Language, Scalability, Structure, and Transaction Processing.

Language

SQL is used to query structured data only. Structured data exists in the form of rows and columns (2D tables) and therefore exert the constraint for a carefully built schema before querying data.

That’s because for data whose structure could not be defined; data from mobile applications, and SAP systems with a lot of varying fields where new fields occur every now and then, SQL fails given its syntax and control flow that works only for table based data.

Apart from the structure constraint on data, SQL has been well nurtured in the past 40 years to offer wide functionality for complex queries, and is secure in usage.

The learning curve of SQL is also short compared to other programming languages (including NoSQL). Moreover, all the variants of SQL including for example SQL server, and MySQL have a great amount of similarity in usage and therefore are easily learnt across the globe.

NoSQL has its own advantages over SQL. It offers the flexibility to query unstructured data (with a dynamic schema not limited to representing data in rows and columns). This means now each type of data could have alternate structure all stored beside each other in a single database.

NoSQL was introduced some 12 years ago with the aim of utilization of unstructured or loosely structured data in database applications.

There’s freedom from rigorous data engineering before the storage to use the data for BI and ML applications. NoSQL offers greater exploration of data in that the raw data is directly fed to the system for storage and after that the BI and ML engineers could build schemas as they like.

The spotlight on NoSQL waned when its users realized it lacked standardization and wide documentation leading to difficulty in carrying out complex queries.

Moreover, NoSQL language varies across databases where each database (MongoDB, Cassandra, etc) have their completely varying versions of noSQL.CategorySQLNoSQLLanguageStructured data onlyStructured and unstructuredScalabilityVerticalHorizontalStructureTable format4 Types incl. columnar formatTransactionsACID complianceCAP theoremSQL vs. NoSQL Difference

Scalability

Scalability is one of the most distinctive features of SQL and NoSQL databases. For the scope of this article, we define scalability as the capacity of a system to process a number of concurrent queries.

The ease of adding and removing processing resources for the purpose of supporting concurrent users determines the effectiveness of scalability.

SQL supports vertical scalability that means new processing resources could be added within the same server. It’s based on the actor model that uses multiple CPU cores. All the processor cores participate in processing over the same data.

NoSQL adopts the horizontal scalability that instead of adding cores to CPU adds new servers in parallel to old ones. It uses a master/slave architecture where a master processor divides data among slave processors.

In essence, horizontal scaling is more desirable due to its ability to divide data across parallel machines. This way it enables faster processing from utilization of all resources in less time. Whereas vertical scaling, although much easier to implement, lacks the kind of linear scalability as in horizontal scaling.

Learn more about differences in horizontal scalability and vertical scalability here.

Structure

SQL databases store data in rows and columns where a column represents a specific data type only. While making these tables some rules are defined to maintain the integrity of data as well as making it efficient for the querying process.

NoSQL databases don’t conform to this tabular structure, and don’t require data engineering such as above. Instead they use other formats that are more flexible to add any kind of data desired. These formats are:

Column-oriented structure: Data resides in columns however these columns support any kind of data type. It results in high dimensional data.

Key-Value structure: Data objects are defined and a unique key is assigned to each object.

Document stores: specifically holds semi-structured data where some of the data is structured, while it may contain other data that has no defined column category.

Graph databases: It uses nodes and relationships to structure data instead of a table or a document. Nodes present data elements while relationships present how they are related to each other.

Learn more about Graph databases in this comprehensive article.

Transaction Processing

As you may be well aware that SQL databases process transactions in such a way as to minimize the erroneous events that might affect the data. For this, it uses the ACID rules. These are the:

Atomicity of a transaction prevents it from getting saved when it’s incomplete. A transaction is either saved when it’s completed only or failed otherwise.

Consistency prevents data from being corrupted by following rules at each step of transactional processing.

Isolation keeps away multiple users to change the same data at same time.

Durability records a transaction once it is made. No roll back can be done after a transaction is saved.

In distributed systems as a copy of data is stored on all distributed nodes through replication the transactional processing is a little different. NoSQL uses the CAP theorem that is specifically designed for successful transaction processing on distributed systems.

Consistency ensures the delivery of latest results where sometimes in case of a node separated from the system due to a network failure might not receive the updated information. An error message must be sent to the user instead of an unlatest value fetched from a node.

Availability, unlike a consistent system, ensures that a transaction is always successful (returning a non-error result) even when there’s network failure.

Partition Tolerance is a phenomenon for uninterruptible system performance in case of a network failure between parallel systems. More than one link is created among nodes such that if a link fails, there are other links that duplicate data to other nodes.

In NoSQL databases, only two properties are fulfilled at a time, that is, either CA, CP, or AP.

Learn more about the CAP theorem with example cases in this insightful article.

SQL vs NoSQL Use Cases

SQL databases are preferred when scalability requirements are not very large. This simply means it won’t support concurrent requests from a large number of users such as those in big data applications.

Big data applications demand storage of huge sized and highly varied data that arrives very frequently on databases. These applications require processing of both structured and unstructured data as well as require high scalability needs.

For big data applications, NoSQL databases have immense use cases and are preferred over SQL based solutions.

Quite surprisingly, today we see some unique database solutions that leverage the SQL language to query ‘big data’ stored across the distributed systems in the cloud. Examples include Snowflake’s relational database that is scalable as well as capable of storing semi-structured data.

Learn more about Snowflake’s unique approach in this comprehensive article.

Databases

Today the old SQL based databases have started to invent NoSQL like features to compete against the rising technology.

We call it ‘Combined Strength’ that uses the good points of both technologies to enable rich and more flexible database experience.

Today MySQL of Oracle has evolved to support semi-structured data such as JSON and document based files along with support for horizontal scaling

Meanwhile, on the non-relational end, NoSQL databases such as MongoDB also offer relational features such as indexing, aggregation queries, and ACID compliance in its document based data format.

Moreover, the noSQL databases have increasingly adopted a SQL-based syntax for attracting data analysts and data scientists who have extensive SQL experience and limited programing knowhow.

Adding to that, Cassandra has CQL, Spark developed SparkQL, and JIRA developed JQL.

Although the NoSQL databases have a lot to offer through its support for programming languages, building a SQL-like support can empower those with SQL know-how to use the best of their knowledge.

Conclusion

The SQL vs. NoSQL debate has evolved from a clear-cut dichotomy to a more nuanced understanding of their strengths and weaknesses. Modern databases often blend elements of both approaches, offering flexibility and scalability.

Dicecamp's Data Engineering Course Using SQL Server Tools could be a valuable resource for individuals looking to master SQL and its applications in the modern data landscape. By focusing on SQL Server tools, the course likely equips students with practical skills to work with relational databases and potentially explore their integration with NoSQL technologies.

Would you like to learn more about specific SQL Server tools or how they can be used in conjunction with NoSQL databases.

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