Can Big Data Database Replace Data Warehouse?

Can Big Data Database Replace Data Warehouse?

April 3, 2020

Big Data is a special kind of database that differs from the regular database. The standard databases are effective for the storing and processing of structured data. It uses tables to store data and SQL or Structured Query Language for accessing and retrieving the data. Big Data includes semi-structured and unstructured data. There are special kinds of databases called NoSQL databases along with tools that store as well as process Big Data.  Data analytics is being used for deploying Big Data like logos, text, images, and other formats of data like JSON, XML, and more. Big Data information is beneficial for the development of data-driven intelligent applications.

Note that any database is an organization of collected structured data. It contains related data and stores and accesses data with electronic means. Now the question is, can Big Data replace any database?

A database is a large collection of data. There are 2 types of databases and they are Relation Database and Non-Relation Database. The latter is also known as the NoSQL database. The structured data is generally stored in SQL databases where unstructured data is kept in NoSQL databases. When it comes to Big Data and Data warehousing, there is a lot of similarities between the two. Both-

  • Store a lot of data
  • Both are used for creating reports
  • Both can be effectively managed by electronic devices

However, Big Data cannot replace Data Warehousing, and the following are the reasons why-

Before getting into why Big Data cannot replace Data Warehousing, you first need to understand these concepts clearly.

Data Warehouse- This is the process of data extraction from one or more heterogeneous or homogeneous sources of data. This data is transformed and loaded on a data repository for data analysis that helps businesses make improved choices for better performance and reporting. The process of this data is known as data warehousing.

Big Data- Big Data implies the variety, velocity, and volume of the information or data. The size of the data, its speed, and the variety of data are known as Big Data.

Key differences between the Big Data and Data Warehouse

The following are the primary differences between Big Data and Data Warehouse that you should note-

  1. In a data warehouse, the data comes in a structured form, whereas in Big Data, it is in its unstructured form.
  2. The data quality changes in Data Warehouse, whereas the data in Big Data, is raw.
  3. Data warehouse stores big amounts of data whereas Big Data stores huge volumes.
  4. Big Data is cost-effective than Data Warehouse.
  5. The data in Data Warehouse is secured highly over Big Data. The latter is open-sourced and gets better over time.

Note that technologies that deal with Big Data focus on analytics that is advanced. These technologies are often viewed as a good strategy for modernization for data archives. Data warehouses are created for reporting, performance management, and OLAP. It is a complementary technology that does not replace Data Warehouse. Both of them can also exist together, depending on the requirements of the business.

Why do organizations need both Big Data and Data Warehousing?

Big Data is required by large organizations because there is extensive data in large corporations. If this data is tapped in properly, it contains valuable information that can result in improved decisions leading to better profits, revenue, and customers. This is the goal of all organizations.

A data warehouse is needed by companies to make informed choices. It is smart to know what really is taking place in your corporation. The information or the data you require is accessible, reliable, and trustworthy for everyone.

From the above, you will find that both of them look the same; however, there is a difference between them. Big data is actually a repository that stores a lot of data and is not certain what to do with them. Data warehouse, on the other hand, is designed with a defined intention on how to carry out informed decisions with the data that has been collected. A professional from credible data administration and Maintenance Company says that Big Data can be used for the purpose of data warehousing.

Big Data solutions are technology, whereas data warehousing is architecture. The former helps companies to manage and store huge amounts of information and data. They use big data solutions to store an extensive volume of data at reduced costs.

A data warehouse is a framework to manage data and organize it. It has the sole purpose to consolidate data from a variety of sources so that the data can be organized in such a way that it can be read with ease. This process is a data lineage capability that aids the company to trace the data sources i.e., its origins. A data warehouse is able to bring data from both operational and transactional sources. It can be consolidated and presented in its real version to business owners who are in charge of the decision- making process at all levels in the company. If the design of the data warehouse is done well, this decision makes are able to report and analyze this information to drive improvement in business procedures and practices.

There are salient differences between a data warehouse and big data. They are not the same and cannot replace one another. An organization can have either big data or data warehouse or a solution of both.

In conclusion, it can be said that on face value, both Big Data and Data Warehouse seem to be similar. However, there are differences between the two. In the past, Data Warehouse had a significant place in businesses; however, in the present times, Big Data has taken over in a large way. Big Data technology focuses on the three V's i.e., variety, volume, and velocity, whereas Data warehousing refers to an architectural concept in the field of data computing. Both store data can be effectively used for making reports and can be successfully managed by electronic devices for storing this data.

Pete Campbell is a social media manager who has worked as a database administrator in the IT industry and has written numerous articles and blog posts on topics related to DBA services for small businesses. He loves to travel, write and play baseball.

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