What Type of Database Would Be Used for Organization That Use Big Data?


Angela Bailey


In today’s digital age, organizations are generating and accumulating vast amounts of data at an unprecedented rate. This explosion of data, commonly referred to as Big Data, has opened up new possibilities and challenges for businesses across various industries.

To effectively handle and analyze this massive volume of data, organizations need a robust and scalable database solution. In this article, we will explore the different types of databases that are suitable for organizations that use Big Data.

Relational Databases

Relational databases have been the traditional choice for storing and managing structured data. They provide a structured way to organize data using tables with rows and columns.

Relational databases use Structured Query Language (SQL) to query and manipulate data. While relational databases can handle large amounts of data, they may face scalability limitations when dealing with Big Data due to their fixed schema design.

NoSQL Databases

NoSQL databases, on the other hand, offer a more flexible approach to store unstructured or semi-structured data. NoSQL stands for “Not Only SQL” and encompasses various database technologies such as document stores, key-value stores, columnar databases, and graph databases. These databases allow for horizontal scaling by distributing data across multiple servers or nodes in a cluster.

Document Stores

One type of NoSQL database is the document store, which stores semi-structured documents in formats like JSON or XML. Document stores are well-suited for organizations that deal with complex hierarchical or nested data structures.

Key-Value Stores

Key-value stores, as the name suggests, store data as key-value pairs. They offer fast access to individual pieces of information based on a unique key. Key-value stores are ideal for organizations that need high-speed data retrieval and can tolerate eventual consistency.

Columnar Databases

Columnar databases store data in columns rather than rows, which allows for efficient compression and faster analytical queries. They are particularly useful for organizations that require fast analytics on large volumes of data.

Graph Databases

Graph databases are designed to handle highly connected data, such as social networks or recommendation systems. They store entities (nodes) and their relationships (edges) as well as properties associated with both. Graph databases excel in traversing relationships between entities quickly.

Distributed Databases

In addition to NoSQL databases, distributed databases have gained popularity for handling Big Data. Distributed databases distribute data across multiple servers or nodes, allowing for horizontal scalability and fault tolerance.

Hadoop and HBase

One popular distributed database solution is Hadoop. Hadoop is an open-source framework that allows organizations to store and process large datasets across distributed clusters of computers. It utilizes the Hadoop Distributed File System (HDFS) for storing data and MapReduce for parallel processing.

Within the Hadoop ecosystem, HBase is a column-oriented distributed database built on top of HDFS. It provides random access to Big Data stored in Hadoop clusters, making it suitable for real-time applications that require low-latency queries.


In conclusion, organizations that deal with Big Data have several options when it comes to selecting a database solution. Relational databases can still be viable options depending on the nature of the data and requirements.

However, NoSQL databases offer more flexibility and scalability when dealing with unstructured or semi-structured data. Additionally, distributed databases like Hadoop and HBase provide the necessary infrastructure for handling large volumes of data across clusters. Understanding the specific needs and characteristics of the data is crucial in choosing the right database solution for an organization that uses Big Data.

  • Relational databases are suitable for structured data but may face scalability limitations.
  • NoSQL databases offer flexibility and scalability for unstructured or semi-structured data.
  • Distributed databases like Hadoop and HBase enable horizontal scalability and fault tolerance.

By selecting the appropriate database type, organizations can unlock the true potential of their Big Data resources and gain valuable insights to drive informed decision-making.

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