The term ‘big data’ typically refers to datasets that are too large and complicated for traditional data processing software. Often described with the three ‘V’s, big data is characterized by volume, velocity, and variety. Harnessing the voluminous data opens up possibilities of addressing business problems that would have been otherwise unmanageable. Thus, businesses rely on predictive analytics and other methods, capabilities, and skills to extract value from big data. The buzz around large data sets has been around since the 60s and 70s. But the popularity of YouTube, Facebook, and other social platforms during the mid-2000s introduced the world to NoSQL database, Hadoop, and similar concepts that spurred the growth of big data.
This article will explore the fundamentals of NoSQL and why NoSQL skills are indispensable for a flourishing career in big data.
What is NoSQL Database?
Not Only SQL or a NoSQL database is a high-performance, non-relational database with flexible data models. It is an approach to data and database design management involving large distributed data sets. In other words, a NoSQL database stores data differently than the typical tabular structure of relational databases. Instead, NoSQL databases store data within one data structure, like a JSON document. Thus, the non-relational database format of NoSQL does not require a schema, which, in turn, allows scalability across large and unstructured datasets. Moreover, NoSQL is a type of distributed database which means that data is stored on various local and remote servers. So, in addition to scalability, NoSQL ensures data availability and reliability.
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Types of NoSQL Databases
There are four main types of NoSQL databases: key-value databases, document databases, graph databases, and column-oriented databases. Let’s look at each in detail.
1. Key-value databases
A key-value database is one of the most elementary types of NoSQL databases that uses the key-value method for data storage. Thus, key-value databases store data as a collection of key-value pairs where the key serves as a discrete identifier. A key-value database is highly partitionable with unparalleled horizontal scaling abilities. Shopping carts and session stores are common use cases of key-value databases.
The most popular key-value databases are:
- Redis
- Couchbase
- Riak
- Project Voldemort
- Memcached
2. Document databases
As evident from the name, a document database stores data as documents. Document databases come in handy for managing semi-structured data and are generally stored in JSON, BSON, and XML formats. Each document typically contains pairs of values and fields where the values can be of various types, including numbers, strings, booleans, objects, or arrays. Popular applications of document databases include user profiles and content management systems.
Some of the most widely used document databases include:
- MongoDB
- OrientDB
- CouchDB
- RavenDB
- Terrastore
3. Graph databases
Graph databases store data using topographical data models. Such databases connect discrete data points (nodes) and use graphs to create relationships (edges) that users can pull with queries. The nodes typically represent persons, places, or objects, whereas the edge defines the relationship between the nodes. For instance, if nodes represent company A and its customers, an edge would describe the relationship between the firm and its customer. AI knowledge graphs and social networks are popular use cases of graph databases.
Some of the widely used graph databases include:
- Neo4J
- OrientDB
- Infinite Graph
4. Column-oriented databases
Column-oriented databases store data in columns. Thus, users can specifically access the columns they need without assigning extra memory to irrelevant information. However, column-oriented databases are extremely useful in analytical applications for quickly retrieving data columns. Moreover, column-oriented databases are an integral aspect of analytic query performance. They significantly reduce the overall disk I/O requirements and the amount of data one needs to load from the risk. Popular use cases of column-oriented databases include content management systems, blogging platforms, heavy write volume, and maintenance of counters.
Well-known column-oriented databases include:
- Apache Cassandra
- Apache HBase
- Amazon DynamoDB
- Hypertable
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Advantages of Using a NoSQL Database
A NoSQL database has its set of advantages. They are the perfect solution for most web, mobile, and gaming applications that rely on high-performance, highly functional, scalable, and flexible databases to offer a seamless user experience.
If you are wondering why NoSQL should be an integral part of your big data skillset, here are some reasons why you should learn about NoSQL databases:
- Flexibility: NoSQL databases enable fast and iterative development with flexible schemas. In addition, the flexible data model makes NoSQL databases the ideal fit for both unstructured and semi-structured data.
- Scalability: Unlike SQL databases, NoSQL databases are horizontally scalable. Therefore, a NoSQL database can manage increased traffic by adding more servers to the database. Thus, NoSQL databases are preferred for continuously evolving and large datasets.
- Speed: NoSQL offers fast and agile storage and processing to users, making it suitable for complex modern mobile apps, web applications, and e-commerce sites.
- Replication: The replication feature of NoSQL databases allows data to be copied and stored across multiple servers. Thus, replication ensures data reliability and accessibility, especially during downtimes or when servers go offline.
- Highly functional: NoSQL databases provide highly-functional data types and APIs purpose-built of every kind of data model.
- High-performance: In comparison to relational databases, NoSQL databases enable higher performance through specific access patterns and data models.
Job Roles Requiring NoSQL Skills
NoSQL is an in-demand skill across most big data job roles due to its advantages. Below is a list of careers where NoSQL is an essential prerequisite:
- Data Scientist: A data scientist’s job encompasses a wide range of skills, and NoSQL is one of them. The primary responsibilities of data scientists include collecting and analyzing data, presenting the data in a visually understandable form, and using the data to make predictions.
- Software Developer: Individuals skilled in NoSQL often work as software developers. Programming skills are as necessary as database management skills to be a software developer.
- Data Architect: A data architect primarily deals with data analysis, creation of data models, data migration, and data warehousing. Experience in database administration is often a desirable qualification for data architects.
- Database Administrator: Database administrators are among the highest-paid professionals and are in high demand in the big data industry. Ideally, a database administrator must have sound knowledge and experience of different database platforms, including various NoSQL databases.
Conclusion
From analytics to customer experience, big data makes it possible to address a wide range of business problems. Moreover, the emergence and evolution of machine learning and the Internet of Things (IoT) have given rise to burgeoning data volumes along with ready access to information. To corroborate the growing popularity of big data, Statista predicts the annual revenue from the global big data analytics market to cross the 68 billion USD mark by 2025.
With its flexible schema model, scalability, speed, replicability, high-performance, and high-functionality, NoSQL has been a boon to businesses dealing with rapid data processing and analysis.
A NoSQL database is one of the best choices for developing various modern and robust applications. Moreover, NoSQL technology enables businesses to become more flexible and agile in handling large volumes of complex and diverse data.
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