Data Analytics

Database Workloads in Azure: RDBMS vs. Big Data

OLAP is a technology that helps by organizing large databases for businesses and supporting complex analysis. Hence it is known as Online Analytical Processing. This technology can be used to perform several complex analytical queries where the transactional systems will not be affected negatively.

Whereas, Azure offers a choice of completely managed NoSQL, relational, and in-memory databases, spanning open-source and proprietary engines that can fit the requirements of app developers. Thus, Azure OLAP can be really beneficial for any company due to its unique features.

What is RDBMS?

A DBMS or Database Management System that can incorporate the data model that is relational while normally including a SQL or Structured Query Language application programming interface.

It is a specific type of DBMS in which the database is accessed and organized according to the connections and relationships between different data items. In an RDBMS or Relational Database Management System, the relationships between data items are shown through tables. Relationships among these tables are expressed usually by the values of data rather than by pointers.

Thus, by implementing an RDBMS or Relational Database Management System within Microsoft Azure OLAP, you will gain the power to quickly configure and provision your relational database within the cloud.

The characteristics of RDBMS

  • The sequence of columns is not significant.
  • Values are atomic.
  • All rows are unique.
  • All columns have different and unique names.

Advantages of RDBMS?

There are many advantages of RDBMS in Azure OLAP, such as:

  1. Flexibility

This helps in saving a lot of time as you only have to update data in one place.

  1. Maintainability

This enables admins to control, maintain, and update data with ease.

  1. Data Structure

Data is stored in a structured way.

What is Big Data?

Big data is a collection of data that has a huge volume yet grows quickly with time. It is a data with very large size and complexity that no traditional data management procedures or tools can process or store efficiently. Big data is one kind of data but has a huge size.

What are the types of Big Data?

There are mainly three types of Big Data, such as:

  1. Unstructured: Any data that has an unknown form or structure is known as unstructured data.
  2. Structured: Here, any data that can be accessed, stored, and also processed in the form of a format that is fixed; hence, it is known as structured data.
  3. Semi-structured: This type of Big Data can contain both forms of data, Structured and Unstructured. An example of Semi-structured data is the data that is represented in an XML file.

What are the characteristics of Big Data?

Big data can be defined and described by the characteristics like:

  • Variety

Variety refers to the heterogeneous nature and sources of data that are both unstructured and structured. In the previous times, databases and spreadsheets were the only sources of data; however, nowadays, data can be contained in the forms of photos, emails, videos, PDFs, monitoring devices, etc. This variety of data can pose some specific issues for mining, storage, and analysis of data.

  • Volume

As mentioned earlier, Big Data works with a huge size of data. Thus, size or volume plays a crucial role in determining the value of data.

Advantages of processing Big Data

  • It has improved customer service.
  • It utilizes outside intelligence while making decisions.
  • Efficient operations.
  • Early identification of risks, etc.

Therefore, database workloads in Azure OLAP can help a lot in controlling, maintaining, and updating the database efficiently.

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