Some argue that “..relational databases are out of date and do not match current trends..”, while others contend that “..SQL cannot handle big data..” and “..SQL cannot handle unstructured data..”. There is no legitimate reasoning in it, and comparing new technology to SQL solutions is absolutely improper.
To be clear, a relational database management system (RDBMS) is a database management system based on the relational model. A relational model is a set of guidelines for storing, accessing, and managing data. It is a notion rather than a product. SQL, on the other hand, is a language designed to work with relational databases. Those who allude to SQL’s limitations in the manner described above are actually referring to SQL products. There are numerous SQL products in the market, and the bulk of them have ruled the world for decades and will almost definitely continue to do so in the future.
Obviously, new technologies such as Hadoop, NoSQL, etc. to handle the needs of the latest trends and solutions for the latest problems. But these cannot eliminate the need of SQL products. Hadoop is designed to handle huge and unstructured data and NoSQL databases store variety of data such as documents, graphs, key-value pairs, etc.
All these technologies were developed in response to specific requirements, and they perform admirably when applied to the appropriate purpose. Let’s look at what these technologies are for in order to avoid making incorrect comparisons.
RDBMS (also referring to SQL Products):
A relational database management system (RDBMS) is a software product (Oracle SQL*Plus, IBM DB2, Microsoft SQL Server, MySQL, PostgreSQL, etc)) whose principal role is to store and retrieve data as required by other software programs running on the same computer or over a network. The data is in tabular form, and must be stored in rows and columns in each cell of a table.
Normalization tries to offer several levels of data breakdown, but the tables will be related together by a primary key and foreign key constraints. Such integrity constraints assure the use, accuracy, and stability of data. As a result, the data is non-repetitive and more accurate than any other database management system that existed before or throughout the relational model’s evolution.
Since RDBMS do not need complex structuring and query processes, anybody with access to the data can query any table in the relational database and retrieve desired columns or rows to be included in the result, ensuring that only relevant data is displayed.
The ACID properties are maintained and work in such a way that they maintain Atomicity (the entire transaction occurs at once or never), Consistency (the database must be consistent before and after the transaction), Isolation (multiple transactions occur independently without interference), and Durability (the changes of a successful transaction occur even if the system fails) resulting in less redundant data with high consistency and correctness.
Most businesses find relational products to be dependable and appealing because of their various features and functions. Scalability, which aids in leveling up and growing to longer lengths, high security, data transformations, ease of data changes, data segregation, logical and physical data independence, aggregation, analysis, visualization, and so on are some of the characteristics or functionalities.
Even SQL products are capable of handling enormous volumes of data; all that is necessary is the proper product selection. Oracle databases can hold up to 8388224 terabytes of data, whereas Microsoft SQL Server databases can hold up to 524272 terabytes of data, with each file having a maximum capacity of 16 terabytes. Apart from that, SQL can convert unstructured data into tabular data up to a point.
NoSQL (also referring to all types of NoSQL databases)
A NoSQL database allows data to be stored and retrieved using methods other than tabular relations. It does not require a fixed schema, eliminates joins, and is scalable. It is used for distributed data repositories with large data storage requirements. Big data and real-time web apps both employ NoSQL.
A document database is a type of nonrelational database that stores data as JSON, BSON, or XML documents.
Columnar databases are useful in situations where data is stored in columns rather than rows, therefore reducing the number of disk seeks and reads. Because the needed column data is kept in the same blocks, it will be accessible fast and simply.
A graph database (GDB) is a database that represents and stores data using graph structures for semantic searches, such as nodes, edges, and attributes. The graph is a crucial notion in the system (or edge or relationship). Graph databases, in other words, are designed specifically to record and traverse relationships.
In conclusion, NoSQL is appropriate for data that does not fit into a tabular format, i.e. in a relational paradigm.
Big Data (referring to Big data technology)
By distributing the data among numerous nodes built of commodity hardware, big data technology allows for the cost-effective storage and processing of large volumes of unstructured and semi-structured data. It’s Hadoop we are talking about.
Hadoop is an open-source software program that allows you to store and analyze massive quantities of data on inexpensive hardware clusters.
Another definition is – Apache Hadoop is a collection of open-source software tools for dealing with huge amounts of data and processing on a distributed network of computers. It’s a software framework for storing and analyzing large amounts of data in a distributed manner.
Rather than relying on a single server, the data will be distributed and processed among several machines. This leaves us to understand – If a machine with four I/O channels and a processing speed of 100 MB completes a work in 45 minutes, ten equivalent machines will finish in 4.5 minutes. This solution’s name is – Hadoop.
I hope you found this post to be informative.
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