To ensure data integrity and security, most organizations used SQL databases in the past. However, as the world undergoes a digital transformation, the popularity of cloud services and the growth of internet technologies have forced many organizations to switch to NoSQL databases. They can handle large amounts of structured and unstructured data.
Most IT departments still use traditional methods, despite the popularity of NoSQL. This is even though they have modern mobile applications. It can be challenging to decide which database type should be used, SQL or NoSQL. Each type of database has its advantages and disadvantages. They have different ways of accessing the data, and each is built with additional functionality. Organizations can only make the right decision about which solution is best for their business and future.
Introduction to SQL Databases
Because it is built on the relational model developed in the 1970s, the SQL database is called a relational database. The relational model is a method for structuring data into relationships (including rows and columns) and defining the relationships among those tables. While there are many other aspects to relational theory, it is essential to understand that it has become the standard for managing and storing data of businesses of all sizes and types.
Structured Query Language (SQL), a standard-based programming language, is the core of the relational model. It defines the schema for a database and connects tables. These tables can also be used to store, modify or retrieve data. SQL is widely used by the American National Standards Institute (ANSI) and the International Organization for Standardization and is well-supported and known by developers around the globe.
Many vital functions make relational databases well-suited to enterprise-level workloads. This is why businesses have used them for a long time. They're optimized for handling highly structured data, and their inherent characteristics--such as normalization, atomicity, and consistency--ensure the integrity of that data throughout its lifespan. These attributes help to increase storage efficiency and offer flexible query support via standard-based SQL.
They could be better. Although they are great for structured data, they can be challenging to scale for semi-structured and unstructured data at large scales. SQL databases can be challenging to scale horizontally, even for structured data. This makes it difficult to manage large distributed data loads. A relational database needs a precise schema. It cannot adapt to changing requirements and must be carefully planned. This leaves little room for current development tools and techniques.
Despite these challenges, SQL databases are still popular choices for many organizations. Vendors offer a wide range of sophisticated database products for relational tables, including Microsoft SQL Server and Oracle Database, IBM DB2, MySQL and PostgreSQL.
SQL Databases: Pros and Cons
Pros
Flexible Queries Allow support for different tasks. Allows engines to create queries that match the on-disk representations by abstracting data from other implementations.
Data Storage with a Smaller Footprint: Normalization and other optimizations improve the performance of databases while reducing resource consumption.
Clear and Simple Terms to Ensure Data Integrity: Consistency, isolation, durability, and atomicity. They are also known as ACID. Database properties that provide legitimate transactions.
Cons
Rigid data models require careful design to maintain good performance and resist evolution. SQL is an established schema. The process of changing it can take time and lead to downtime.
Horizontal Scalability Limitation: This is either completely unsupported, implemented using ad-hoc methods or based on unproven technologies.
One point of Failure: Non-distributed engines can be assisted by failover or replication techniques.
Examples of SQL Databases
- Db2
- MySQL
- PostgreSQL
- YugabyteDB
- CockroachDB
- Oracle Database
- Microsoft SQL Server.
Introduction to NoSQL Databases
Many companies choose NoSQL databases due to growing volumes of unstructured and semi-structured data. This is commonly called "not SQL" or simply "not SQL". Although this name convention is unclear, it indicates an array more adaptable than traditional SQL databases. NoSQL databases don't follow the same schema structure as relational databases. They are not tied to one model of data like these databases.
There is no reason to think that NoSQL databases must follow any particular model. It isn't easy to imagine all possible scenarios as the industry is still developing. Four NoSQL models have been established, and they are available in each one:
Key-Value Databanks: The critical value structure associates unique identifiers with specific data blobs. This allows you to store almost any type of data in whatever format. Redis, Berkeley DB and Amazon DynamoDB are examples of such products.
Document Databases: documents are stored in a format such as JSON or XML. Each record has its Key but is not restricted to specific fields or components. MongoDB and CouchDB are examples of such products.
Column-Oriented Databases: Data is stored in columns rather than rows. This allows the database to search for and combine large amounts of data quickly. These databases can be referred to as wide-column stores or column stores. HBase and BigTable are examples of such products.
Graph Databases: These databases store data in graph-based structures that define how data is interconnected. They consider the connections between data to be vital. InfiniteGraph and Neo4J are two examples of such software.
Every NoSQL database type has advantages and disadvantages depending on the workload the organization is looking to accommodate. There are exceptions to this rule; however, that only means some NoSQL products will be a good fit for the above categories. Azure Cosmos DB offers five APIs that make the service easier for users. These APIs include APIs for SQL API and API for MongoDB.
No matter what platform, no matter the product, any NoSQL database can scale horizontally and manage large data loads. It will also be more flexible than SQL databases. NoSQL databases are flexible enough to allow developers to get started with their ideas. However, NoSQL databases have different data integrity and are more sophisticated than relational databases.
Want More Information About Our Services? Talk to Our Consultants!
NoSQL Evolution
Web 2.0 companies like Amazon, Google, and Facebook recognized the SQL scalability problem. They devised their solutions, including technologies such as BigTable, DynamoDB and Cassandra.
Many NoSQL Database Management Systems (DBMS) have been developed to address this growing interest. They are focused on consistency, reliability, performance, and consistency. Many existing indexing structures were reused to improve search and read versions.
There were the first proprietary (closed-source) NoSQL databases that big companies created to suit their needs. These included Google's BigTable and Amazon's DynamoDB.
These proprietary systems' success led to the development of many other open-source and proprietary database systems. The most popular are Hypertable, Cassandra and DynamoDB.
What makes NoSQL different?
NoSQL databases are different from traditional relational databases because they contain unstructured storage. NoSQL databases don't have fixed table structures like relational databases.
Types of NoSQL Data Storage
Key Value Store
A hash table is used to identify a key point for an item in the Key Value store category.
Keys can be arranged into logical groups, with keys only needing to be unique within each group. This allows identical keys to be placed in different logical groups. This table illustrates a key-value location. The Key is the name, and the value is the address of Ulster University.
Particular implementations of key-value stores provide caching mechanisms that significantly improve their performance.
The Key is all that is required to access the items in the database. Data can be stored as a string, JSON or BLOB (Binary Large OBject).
This database has a lot of things that could be improved. One is its need for more consistency at the database level. Although developers can add their code to this, it is more time-consuming, complex, and requires more effort. Amazon DynamoDB is the most well-known NoSQL database built on key-value stores.
Document Storage
Key-value stores and document stores are alike because they do not require schemas but are based on key-value models. Both have many of the same benefits and drawbacks. Both have inconsistent database levels, making it difficult for applications to provide more excellent reliability and consistency.
However, there are key differences.
Document Stores store the values (documents), which provide the encoding of the data. These encodings are available in XML, JSON or BSON (Binary encoded JSON).
You can also query data.
MongoDB is the most well-known database application that relies upon a Document Store.
Column Store
Column Store databases store data in columns instead of rows, which is what most relational database management systems do.
A Column Store comprises one or more Column Families, which logically group specific columns in the database. A key can point to and identify a certain number of columns within the database. It is associated with a keyspace attribute which defines its scope. Each column has several names and values, each one comma separated.
Column Stores allow fast read/write access to the data stored. A column store stores rows that correspond to one column as a single disk record. This allows for quicker access during read/write operations. Google's BigTable and HBase are the most used databases for column storage.
Graph Base
A directed graph structure is used in a Graph Based NoSQL Database to represent data. The graph is composed of edges and nodes.
A graph is simply a representation of a collection of objects. Links link some things. These connected objects can be represented mathematically as vertices. The links connecting some pairs of vertices are called edges. A graph is a collection of vertices and the edges connecting them.
This diagram shows the structure of a graph-based database, which uses nodes and edges to store and represent data. These nodes are organized according to their relationships, characterized by edges connecting the nodes. Each of the nodes and relationships has specific properties.
Most often, graph databases are used in social networking apps. Graph databases enable developers to concentrate more on relationships between objects than the objects themselves. They are scalable and simple to use in this context.
InfoGrid, InfiniteGraph and InfoGrid are currently the most used graph databases.
Read More: 12 Key Technologies that Enable Big Data for Businesses
# NoSQL Databases: Pros and Cons
Pros
- NoSQL databases offer many benefits over traditional relational databases.
- The fundamental difference between NoSQL and other databases is their simple, flexible structure. They do not require schemas.
- NoSQL databases do not use relational databases. Instead, they are built on key-value pairs.
- There are many store types for NoSQL databases, including column store and document store as well as key value store, key store, graph store and object store.
- Each value in the database usually has a key. Developers can store serialized objects in some NoSQL databases. This is not the same as string values.
- NoSQL databases that are open-source can be deployed on low-cost hardware and don't require costly licensing fees.
- NoSQL databases are also easier to expand than relational databases. This is because the load is distributed horizontally and scaled on all nodes rather than being replaced by a larger host in relational databases.
Cons
- NoSQL databases may not be perfect and not always the best choice.
- One reason is that most NoSQL databases don't support reliability features natively supported in relational databases. These reliability features include atomicity and consistency as well as isolation, consistency, isolation and durability. These features are not supported by NoSQL databases. This means that consistency is traded for performance and scalability in NoSQL databases.
- Developers must create their own code to ensure consistency and reliability. This adds complexity to the system.
- This could limit the number of applications that can use NoSQL databases to perform secure transactions.
- Incompatibility of SQL queries is another form of complexity that can be found in many NoSQL databases. This can lead to additional complexity and time.
- Access methods are not flexible: NoSQL engines can only optimize queries with abstraction.
# Examples NoSQL Databases
- Redis
- FaunaDB
- CouchDB
- MongoDB
- Cassandra
- Elasticsearch
When Should you Use SQL?
SQL is the best database for big data, especially when the relationship between data sets is clear. It's also the best way to verify the integrity of data. SQL databases can be used to query data in many ways. SQL databases are generally vertically scalable, i.e. You can increase the workload on a single server by increasing RAM, CPU SSD, and so forth. ).
Some SQL databases support NoSQL-style workloads via special features (e.g., native JavaScript Object Notation (JSON) data types). They are great for non-relational purposes, even if you don't need the horizontal scaling offered by NoSQL data storage. They can handle structured and relational data without storing multiple data systems.
NoSQL is simple, but developers must be aware of data storage's consequences when creating applications. It would help if you also considered consistency in writing and the eventual effects of sharding. These concerns are not relevant to SQL databases, which makes it easier to build applications around them. They are also flexible and easy to use, making it easier for you to create complex queries.
When Should you use NoSQL?
NoSQL databases are best suited to store large amounts of data or to manage changing data sets. You can also use it when you have other data models or needs that don't fit into the traditional relational database model. Document databases (e.g. CouchDB, MongoDB or Amazon DocumentDB) can manage large amounts of unstructured information.
Redis is an excellent choice if you need quick access to key-value databases that do not have strong integrity assurances. Do you need a flexible or complex search that spans multiple data sources? Elasticsearch is your best choice.
Many NoSQL databases have horizontal scalability as a fundamental principle. Contrary to SQL or other databases, the built-in sharding capabilities of NoSQL databases and their high availability requirements permit vertical scaling (i.e. "scaling out"), contrary to SQL. Cassandra and other NoSQL databases do not have one point of failure. This allows applications to respond quickly to individual issues.
Want More Information About Our Services? Talk to Our Consultants!
The Key Takeaway
Database professionals, both in the majority and "SQL", have to make a decision or suggest the correct database. NoSQL is a great tool to help you make a decision. It is essential to consider the data requirements of each database and make informed decisions about acceptable trade-offs to achieve uptime and performance goals. IBM Cloud supports cloud-hosted versions of several SQL and NoSQL databases via its cloud-native database.
