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What is NoSQL?

What is NoSQL?

The global NoSQL market is expected to reach $3.4 Billion by 2020 with a CAGR of 35.1%.

The age of digitization has made the world generate massive amounts of data. Most of the conventional databases are not prepared to handle these data seamlessly which gives rise to the need to create the kind of databases that do not require modeling and structuring the data structures before storing it.

NoSQL (Not only SQL) came as a solution to this problem of relational database management systems and allowed companies to store massive amounts of structured, semi-structured and unstructured data in real time. It does not certainly imply that it restricts the usage of SQL for these databases. Some of the popular NoSQL databases are HBase, Cassandra, IBM Informix, MongoDB, Amazon SimpleDB, Cloudata, etc. Today most of the world-famous firms like Google, Facebook, Amazon, etc., are using NoSQL to provide cloud-based services in real-time.

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What was the problem earlier and how NoSQL is resolving it?

In order to better understand What is NoSQL, we should compare it with Relational databases :

Criteria Relational Database Management NoSQL Database Management
Data model Tables and schemas Partition Keys to retrieve data
ACID properties Strictly followed No strict adherence
Scalability Vertical scalability Horizontal scalability
Data manipulation Using queries in SQL and executed by RDBMS Using object-based APIs
Velocity of data Moderate Very high
Suitability Structured data Structured, semi-structured and unstructured data
  • NoSQL is agile because it does not create schemas nor it statically defines the data models
  • Instead of tables it uses objects, collections and nested collections
  • Deployed over multiple cheap Intel-based servers
  • Immediate failover with the help of uni-directional and bi-directional replication of data
  • Equipped with the big data, cloud, mobile and web technologies
  • Trades conventional ACID properties to incorporate more flexibility and agility.

Since relational databases were developed many years ago when there was no internet and digitization was meant to be deployed on single big server. However with the advent of internet and digital economy this technology fell short in fulfilling the dynamic requirements and that is when NoSQL systems came into limelight.

Initially when the applications used relational databases, the developers found difficulties in matching the data structures supported by the two platforms. They had to convert the in-memory data structures into relational ones in order to transfer the data to and from the database. This reduced the agility and performance of the systems in a big way.

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What are NoSQL Databases?

In contrast NoSQL databases resolve this issue by incorporating a wide range of technologies that make the systems scalable and suitable for big data operations. This is done by distributing the load among several Intel-based cheap servers which empowers the platforms for real-time processing.

Though in many platforms these two co-exist, but more and more companies are tending towards the adoption of NoSQL databases to deal with big data operations.

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Ways to deploy NoSQL databases

It can be deployed in four different manners :

Columnar Databases – Reads and writes columns of data rather than the rows. Each column is comparable to a container in RDBMS where a Key defines a row and single row has multiple columns.

Document Databases – These databases store and retrieve semi-structured data in the format of documents such as XML, JSON, etc. Some of the popular document databases like MongoDB provide a rich query language for ease of access and smooth transition of data models.

Graph Databases – Stores data as entities and relations between them allowing faster traversal and joining operations to be performed. However these graphs can be built using SQL as well as NoSQL databases.

In-Memory Key-Value Stores- Suitable for read-heavy workloads and compute-intensive workloads, these databases store critical data in memory which in turn improves the performance of the systems.

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How will learning NoSQL help you excel in your career?

Technology is changing everyday mandating people to acquire a good grasp of them. The recent advancements are the NoSQL databases which have made companies to crave for database management professionals. Combined with Hadoop, its growth trajectory is trending upwards every year.

According to a popular employment and research agency Payscale.com, the average salary of the NoSQL software engineers/developers/programmers is around 1,000,000 Rupees making it a hot and demanding job of current times. Moreover even the beginners and the professionals having one year experience are getting 650,000 rupees.

Some of the popular databases trending in the market are MongoDB, Cassandra, HBase, CouchDB, Redis, etc. Over past few years, it has gained considerable traction as it provides significant services to the companies. Especially when it has found its place in big data world, the need for these databases is witnessed to be growing every other day.

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Who should get trained in NoSQL database technologies?

Definitely there is no shortage of high-paying career opportunities that require proficiency in these databases. However some of the specific profiles for which learning these technologies will create new career avenues:

  • Database administrators
  • Data architects
  • Data scientists
  • System engineers
  • Software developers
  • Big data professionals
  • Big data analysts
  • Hadoop and NoSQL Operations Engineer, etc.

With the companies ready to take advantage of alternative database systems that provide better and flawless performance on the big data platforms, the demand for database professionals has risen dramatically. Now that the relational database age is long gone, the NoSQL technologies are trending creating immense career opportunities for aspirants wanting to make it big in this domain. Therefore mastering it is very much needed to take your career to a whole new level.

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About the Author

Technical Reseach Analyst - Data Engineering

Abhijit is a Technical Research Analyst specializing in Deep Learning. He holds a degree in Computer Science with a focus on Data Science. Being proficient in Python, Scala, C++, Dart, and R, he is passionate about new-age technologies. Abhijit crafts insightful analyses and impactful content, bridging the gap between cutting-edge research and practical applications.