In this article, let’s identify databases comparing NoSQL vs SQL. In the present market, there are different kinds of databases to store data in various manners. So, we have to use the databases to manage different kinds of data as an individual or business community. In that case, we have to select what type of database which suits for achieving our purpose. It may be SQL or NoSQL database. In this article, you will get an idea of what database type most suitable for your purpose.
What is SQL?
SQL means is the Structured Query Language. It allows manipulating databases and it is the core of the relational database model. American national standard institute (ANSI) and International organization for standardization (ISO) awarded standard for SQL in 1986 and in 1987. Although SQL has above two stranded, SQL has different syntaxes according to the SQL versions. But all have the same structure.
In the manipulation of the database, SQL can do various things on the database. There are execute the query, retrieve data, create databases, create tables, create stored procedures, insert records, update records, delete records, viewing database data, and set permission on tables. For the manipulation database via the SQL, it has syntaxes and keywords. The Database handler can write structured queries using syntaxes and keywords. So, this language basically helps to retrieve the data from a database whose structure is completely design of tables with rows and columns. And also these tables interlink with other tables with specific key values.
For interlink with tables, SQL has relationship types. It allows maintaining the connection between tables. SQL has four relationship types which are one-to-one relationships, one-to-many relationships, many-to-many relationships, and self-referencing relationships. A one-to-one relationship is simply when a single row(a unique record) in the “A” table is related to a single row(a unique record) in table “B”. A one-to-many relationship is simply when a single row(a unique record) in the “A” table is related to many rows(records) in the table “B”. A many-to-many relationship is simply when many rows(records) in the “A” table is related to many rows(records) in the table “B”. And lastly, a self-referencing relationship is when rows(records) in table “A” are related to the same table itself. Because of these tables, syntaxes, keywords, and relationships, we can call for SQL is a well-structured database.
What is NoSQL?
NoSQL is commonly known as the Not Only SQL. It provides a mechanism for storage and retrieval of data and is the latest-generation database technology now. It has no specific schema. But it can handle huge amounts of data. NoSQL database also has no tables, rows, columns to storing data like SQL databases. It has collections instead of tables. And it also has a set of documents which are basically the data values packs instead of rows and columns. The most interesting part is NoSQL databases do not have to use the same schema for the presence of multiple documents in one collection. It has a dynamic schema for manipulation. It means multiple documents can be present in a single collection and each collection can have different documents with no structure-schema. Apart from this, NoSQL has no query language or complex relationships. Maybe sometimes few relationships are in there.
Let’s identify the above factor using an example. Think about we have a database named school_details. Students and teachers are collections in the database. We can add a student as a document into student collection which including student_id, student_name, and home_address. After that, we can add a new document in the same collection which does not have the sudent_id and just including student_name and home_address. Additionally, it can add new document data which including student_id, student_name, home_address, and student_DOB as extra data. It does not matter which data we entered in the document of data it all goes to the collection and storing it. We can put all the required information in a collection. So, basically, the language that you use to store and retrieve data is completely infrastructure-based.
Differences NoSQL vs SQL
NoSql and SQL have differences. Let’s identify what are the differences they have.
Type of database
SQL is a relational database. It also has organized and structured data fields to define columns. A relational database can have the number of tables and each table is related to another table by using relationships. And the table has the number of columns called data fields. That is why its called relational databases. But NoSQL is a non-relational database that provides a mechanism for storage and retrieval of data. In the NoSQL, database data is modeled in the form of collections and set of documents and hence it tries to avoid relations. So, we can identify NoSQL as a distributed database or non-relational database.
Language
SQL databases have a specific language called “structured query language”. This language uses to define and manipulate the database. Also, this language very powerful for complex queries. In version to version, some SQL syntaxes are deffer but every version of SQL has the same structure. It does not vary from database to database. But coming into the NoSQL database it has no specific language. The queries of the NoSQL database focus on the collection of documents. It depends on the database user’s requirements. That means it varies database to database. Because of this reason, sometimes called “unstructured query language” for NoSQL language.
Schema
SQL has a pre-defined schema. And also SQL needs pre-defined schema for structured data. Before start using SQL, we want to make sure that the data was pre-defined. Then after we can view and manipulate data by given our parameters using SQL syntax and keywords. SELECT, CREATE WHERE, DELETE, UPDATE, INSERT INTO are some of the keywords using with SQL. But in NoSQL database have a dynamic schema for unstructured data. When we using the NoSQL database, there is no pre-defined schema. NoSQL dynamic schema completely depends on how we wish to store the data. Basically, which documents into the collection how we wish to store them.
Complex Queries
SQL databases are well-structured and have proper tables to get results for the queries. If we want to get results with the query, and it also has many sub-queries inside the query that does not matter to get answers. we can get clear results for using SQL syntaxes because it is fully-structured via having proper tables. So, SQL can handle complex queries easily. In NoSQL databases, documents are not good for complex queries because NoSQL has no standard interfaces to perform complex queries. And also it not has a specific language to generate queries. It completely based on the database infrastructure. In other words, dynamic schema. So, NoSQL is not as powerful as SQL for complex queries.
Database Categories (Variations)
SQL databases are the table based databases. These databases are row-oriented, each row has a row identifier(Id), and each field(column) in a row is stored together in a table. In brief, SQL databases have a number of tables and it has a number of rows and columns to store data. When we discuss NoSQL databases, we can identify four types. There are document stores, key-value stores, graph stores, and wide column stores.
Document stores seem most natural among NoSQL database types, because they are designed to hold everyday documents as they are, and they allow you to perform some queries and calculations with an already aggregated data form. Key-value stores are the simplest form of NoSQL databases. Every single item in the database is stored as an attribute name with its values. It has capable of storing huge amounts of data. Graph stores use to store information having connections. When data is highly interconnected, have to choose graph stores. Examples of that are social networks, scientific articles, clusters of fixed assets. It can become incredibly complex when there are enough relationships and types of objects. Wide column stores are optimized for queries over large data sets. It based on Column-oriented. Not like row-oriented in SQL, It is easy to add another column because it does not affect any of the existing fields(columns).
Hierarchical Data Storage
SQL databases are not best suited for hierarchical data storage. Because increasing the number of tables also increases the complexity of database relations. But its relations not as hierarchical data storage. It only increases SQL relationships and complexity. But the NoSQL database is better suited for hierarchical data storage, as it uses a key-value pair way of sorting data similar to the JSON data. It can make and fit better hierarchical data storage. NoSQL database also is very preferred for large datasets than the SQL databases.
Scalability
In most situations, SQL databases are vertically scalable. It means that can increase the load on a single server by increasing components. Simply we can optimize hardware requirements such as increasing RAM, CPU, storage like SSD on a single server. On the other hand, NoSQL databases are horizontally scalable. It means that it can load balancing by adding more servers into the cluster of servers. In this horizontally way, the NoSQL database able to handle traffic simply. NoSQL database already designs to handle very large data. So, horizontally scalable is very important to become much more powerful for handling big data.
Online Processing
When we discuss online processing, SQL is the best fit with heavy-transaction type applications. Because it is the most stable and having integrity of data. SQL is mainly used for OLTP which is the online transaction processing. OLTP is basically characterized by a large number of short online transactions such as insert, update and delete. The focus of OLTP systems are on very fast query processing, maintaining data integrity in multiple access environments, and efficiency measured by the number of transactions per second. OLTP databases have the fit details in the current data, and the schema used to store the transaction database is usually the 3NF(Normalizing Form) entity model. It is great for OLTP data handling.
NoSQL is not compatible and stable enough for high load for complex transactional applications. But NoSQL has powerful analytical capacity. So, NoSQL databases are mainly focused on OLAP which is online analytical processing. This is mainly characterized by a relatively small volume of transactions, and connections are often very complex and difficult aggregation. OLAP system’s response time is effective. OLAP applications are widely used by data mining techniques.
ACID properties vs CAP theorem
SQL databases consider ACID properties which are A for atomicity, C for consistency, I for isolation, and D for durability. Atomicity refers to transactions. A transaction is a logical unit of work that must be completed with all data changes or none of them completed. Consistency ensures that data must all the validation rules. It means the transaction never leaves the database without completing its status. Isolation means, changes to data performed by a transaction that must be free of another transaction. Durability means, after the transaction is completed, the results of the changes made by the transaction must be permanent with the database.
NoSQL databases consider the CAP theorem(Brewer’s theorem). CAP means C for consistency, A for availability, and P for partition tolerance. These three are basic requirements of the CAP theorem. Consistency means that the data in the database remains stable after executing an operation. Availability means the database system is always on and working. It is the guarantee that every request receives a response about whether it is successful or fails. Partition tolerance is that the system continues to operate despite arbitrary message loss or failure of any part of the system. It means that, if some servers or other equipment is not working properly or unreachable, but the system is still running continuously. According to this theorem, the system can not fulfill all three requirements. So, the system always considers two requirements. It can be CA(Consistency and Availability), CP(Consistency and Partition tolerance), or AP(Availability and Partition tolerance).
Community
SQL is the oldest database solution than NoSQL. And also many communities were using SQL for a long time ago. Because of SQL community is huge than the NoSQL community. It has thousand of community forms and chats services providing all SQL vendors and SQL developers. But NoSQL databases have no huge community or helping services. Also, the world has a little bit of NoSQL developers with comparing SQL developers. Further only limited experts available in NoSQL.
Database options NoSQL vs SQL
Now you know about differences between SQL vs NoSQL. Let’s discuss using database options for SQL vs NoSQL.
SQL Database options
Let’s discuss SQL databases. This part of the article will consider about four SQL database options. There are Oracle databases, MySQL, MS SQL Server, and PostgreSQL.
Oracle Database
Oracle database is the best option for enterprise-level organizational database management systems based on relational database management systems (RDMS). Because the enterprise-level organization has a lot of data to handle every day. So, the Oracle database has some abilities to handle big data and it also designed for commercial options like banking and financial sector databases. Oracle database targeted audience is enterprise-level because this database option is very expensive and not have a free version. But the oracle team gives frequent updates and excellent customer services for its customers.
Oracle database has some unique language methods (own dialect of SQL) known as PL/SQL (Procedural Language/SQL). This is little bit differs from normal SQL with syntaxes and keywords. Oracle has database objects. It divides into sub-objects (called subsets) according to schemas. In the Oracle database, we can easily rollback and change database values. Further, the oracle database offers bitmap indexing, function-based indexing, reverse-key indexing, partitioning, and star query optimization. By considering all the factors, we can choose the oracle database for the largest data needs companies. But small and medium level businesses are not recommending this option because of the very expensive cost effect.
MySQL
MySQL is a mostly free database option nowadays. Oracle has MySQL ownership and they manage and release MySQL updates and other related development tools. MySQL is available for all of most major operating systems and platforms. There are windows platforms, Linux platforms, Mac, Solaris, and BSD. Nowadays, most web development in SQL uses MySQL for its developments. Because MySQL can establish connections with most web development tools and languages like PHP, Node.js, Python, and also some other languages like C#, C++, Java, etc. And other hand it is free for use.
MySQL has a huge community and it is an extremely established database. It is a very scalable and available database option even under pressure workload. MySQL can handle database sharding. So, MySQL is good for business in cost-effective. Most suitable businesses are multi-row transaction businesses such as accounting systems and inventory systems. It is also suitable for legacy systems that have a pre-defined structure.
Microsoft SQL Server
MS SQL Server is a very popular database option for small and medium level companies and organizations that has a relational database model. Microsoft manages and releases SQL server updates and other related tools and features. This is a commercial relational database management system like Oracle system but mostly focus on small and medium level business. It has great customer support service and frequent update system. And also has good documentation and live project support.
MS SQL Server has its dialect of SQL keywords and syntaxes (little bit differs from SQL normal standard version) like Oracle database. This unique dialect called T-SQL (Transact SQL). Sometimes SQL commands hard to adjust and goes bad because of SQL server has a separate execution method in every command. And its only support with Windows, Linux, and container image with Docker. However, MS SQL Server has a better user interface in SQL management studio for being easy to use. And also, it has a Database tuning advisor, SQL server profiler, and BI tools.
PostgreSQL
PostgreSQL is a free and open-source database system developed by the PostgreSQL Global Development Group. It is not exactly a relational database management system. It is some kind of hybrid system between SQL and NoSQL. But its way of an interacting database is the SQL. This database option has no own client support unit. But It has an active community to get ideas and solutions. And also, well paid support options are available from third-party service providers. PostgreSQL is highly compatible with ACID compliance.
PostgreSQL supports many operating systems. There are Windows, Linux, Unix, Solaris, OS X, OpenBSD, FreeBSD, NetBSD, and HP-UX. Like in MySQL, it supports some other programming languages. There are C, C++, .Net, Java, Perl, PHP, Delphi, Python, JavaScript, Node.js, etc. It uses pure SQL it has no dialect SQL version. PostgreSQL system is better on a large database with performing complicated quires.
NoSQL Databases
Let’s discuss NoSQL databases. This part of the article will consider about four NoSQL database options. There are MongoDB, Apache Cassandra, Google Cloud BigTable, and Apache HBase.
MongoDB
MongoDB is a free and famous database option for non-relational database systems. It is horizontally scalable. Because of its users can reduce the workload and manage database factors properly. Like in SQL databases, MongoDB doesn’t want schema for data storage. It gives the flexibility to manipulate the database whatever we want without modifying any existing data. It can add more data without any columns or fields limits. MongoDB has a good speed querying feature to get data faster.
MongoDB has a global cloud database technology that is called MongoDB Atlas. This feature gives the ability to deploy fully-managed MongoDB via GCP (Google cloud platform), AWS (Amazon web services), Azure (Microsoft cloud service). MongoDB Atlas can fully-manage your database with more time efficiently using cloud facilities. MongoDB is a good solution for the fastest growing business with no pre-defined database management which means your database requirement has a lot of unstructured data. Examples for that real-time analytics, rapidly growing mobile apps, CMS (content management systems).
Apache Cassandra
Apache Cassandra founded by Facebook and now it released to the world as free and open-source database management software. Cassandra offers powerful data protection. It commits log can identify data changes and lost that means commit log to make sure data is not lost. Cassandra offers high data scalability. It can make the larger and wide database even it starts the simplest way of designing. It also has a fast read and writes the ability to give high-quality availability service even it is a massive project.
Cassandra has redundancy data service and node functions. It offers constant uptime and it stops all points of failure. Additionally, Cassandra supports some SQL features that are DDL, DML, and SELECT statements. The Internet of things (IoT) is very famous nowadays. Cassandra is also popular as a fast support database option for IoT technology. Cassandra offers real-time insight for IoT systems.
Google Cloud BigTable
Google cloud BigTable is a very powerful NoSQL database option. However, it is not free. You have to buy it from the google cloud platform. In the google cloud platform, price is calculated according to the usage of it. BigTable is a faster and massively scalable NoSQL solution. It offers a consistent sub-10ms latency. It also gives higher availability, durability, and resilience when something went wrong. Google cloud BigTable has interesting functions which are machine learning applications, data analytics tools, highly scalable features, and also google product compatibility.
According to google’s explanation, BigTable technology, and tools including BigQuery great for some special activities. The first one is the fintech. In this fintech, you can identify financial patterns and marketing data, trading information, and see your real-time transaction information. And the second one is the IoT. You can build good quality IoT applications using this google services and its analytical power. And the third one is the advertisements. You can gather the best customer behaviors in particular fields and make patterns using the above technologies and then show ads to your customer audience.
Apache HBase
Apache created HBase specifically to work with large datasets. HBase is a free and open-source NoSQL database option. Hadoop distributed file system (HDFS) is an excellent feature in HBase. HBase has clustering algorithms to scale databases via clusters. They are used to obtain machine learning insights from data. HBase can store both unstructured and semi-structured (this has some structured and pre-defined data architecture) data.
HBase is also providing high consistency. Read-write commands are very responsible. After writing data into the database, read requests on the data will take the same response. To reduce and prevent undesirable impact, HBase uses replications to offer failover. And it has a configurable sharding option for tables. HBase is most suitable for with Big data handling process.
