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Pros & Cons of a Data Warehouse



Pros & Cons of a Data Warehouse

A data warehouse is an enterprise system used for business intelligence analysis and reporting of structured and semi-structured data.

With almost everything around us becoming a source of data, it’s proving to be quite a challenge for traditional data warehouses to support such fast changing and high volume data. So is data warehouse a thing of the past already?

A huge collection of data from various sources is known as a data warehouse. The data warehouse was initially created to store information of an organization. This information refers to the data that is about an organization’s sales, purchase, customers, employees, etc. The need for a data warehouse emerged as storing and retrieving this data became a tedious task.

What Are the Pros of a Data Warehouse?


A data warehouse has various exclusive features. Four of the most outstanding features of data warehouses are:

1. Structured Information Stored

Information that is stored in a structured manner. This means that the data stored here is divided according to their sources and stored in their respective data marts. If there is data that has to be added to sales, that information will go in the sales data mart that has been created in the data warehouse. There is no cluttered input of data in a data warehouse.


2. Integrated Data

Data integration is one of the prime features of data warehouses. Integrated storage of data means that data from multiple sources is stored together in a data warehouse.

3. Non-Volatile 

Data that is being stored in a data warehouse is non-volatile, as information stored in a data warehouse cannot be edited. Once the information has been inserted into a data warehouse, it can only be updated or deleted altogether.

4. Time-Variant 

Information that is stored in a data warehouse cannot be edited, and hence it can be stored inside a data warehouse for a long period of time. If an organization wants to assess why their sales have dropped over the last one year, while they had uncountable sales 2 years ago, they can refer to the strategy they used back then by referring to the information that is stored in their databases.

What Are the Cons of a Data Warehouse?


With time, organizations have realized data warehouse’s limitations. Here are three such drawbacks:

1. Data is Rigid 

Since information is stored in a specified file format, for the data to be used in a data warehouse, it has to be changed to that file format. This has led to the shortcoming that data warehouses cannot store data with mixed file formats.


2. High Maintenance Cost 

Whenever a small organization gets a large IT project, they require high maintenance systems. These high maintenance systems require financial resources. This leads an organization into spending more of their resources rather than making benefits.

3. Inability to Store Huge Amount of Data 

Its inability to store huge chunks of data is considered to be one of the major drawbacks of data warehousing. This led to big data not being supported by data warehouses.

How Can We Move Ahead of The Limitations of Data Warehousing?


The inability of data warehousing to store information with different file formats and huge chunks of data led to the innovation of Uniform Information Architecture. Using this kind of architecture, organizations can store information of various file formats, and data is volatile. This kind of database can also store information that was previously being stored in a data warehouse.

There are various organizations that are still using data warehouses, however, those who are dealing with big data at their hands have moved on to a better version. It would not be wrong to conclude that data warehouses can never be a thing of the past as newer techniques for data storage will form the basis of what data warehouses were initially brought in for.

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How Blockchain and Big Data Can Work Together



How Blockchain and Big Data Can Work Together

Big data and blockchain work well together by providing more security and integrity. 

One is transforming data management while the other is changing the nature of transactions altogether. Could they create an even more significant impact on the industries by binding together – big data for blockchain or blockchain for big data?.

Big data technologies first came into the picture at the dawn of this millennium to meet the computational needs of large datasets in the Internet-era. Proprietary applications like BigTable by Google and ZooKeeper at Yahoo showcased the potential of big data. However, the potential could only be tapped into after open-source projects such as the Hadoop File System (HDFS) and Hadoop MapReduce hit the market. Since then, big data has snowballed to transform how companies manage their data in the 21st century. Satoshi Nakamoto, an anonymous mystic individual, introduced the world to blockchain in 2008. It was developed in an attempt to solve the problem of double spending in transactions by eliminating the need for a third party in financial transactions. Blockchain also gave the world its first digital cryptocurrency – the bitcoin. Since then, the concept of blockchain has rapidly evolved to provide robust solutions to problems persisting in a wide array of industries. Now that both big data and blockchain are established as effective tools to tackle issues in different domains, we look forward to – possible methods of integrating both big data and blockchain to deliver even better solutions to specific problems, or as we’ve called it in this article, blockchain for big data and big data for blockchain.

How Big Data Works With Blockchain


A lot of governments have had trouble with the anonymity clause of blockchain. Despite being favored for its security and infallibility, blockchains are turned down for not being able to track stakeholders in transactions, thus being a preferred choice for illegal trade. Big data applications can help make blockchains trackable by managing structured datasets of wallet addresses and their owner details. This kind of infrastructure can convince governments to adopt blockchain as a platform for transactions that demand speed, safety, reliability, and traceability – thanks to big data for blockchain.

The Close Ties Between Blockchain and Big Data


Big data is comfortably dealing with huge sets of data, but some issues in its infrastructure have posed a problem in the widespread adoption of the technology. The big data infrastructure is centralized to a server location that offers complete unconditional control of data to the ones who have access to the server. This ‘ownership’ creates a problem when big data infrastructure is to be shared between different companies or even different regional offices of the same company. Besides, having multiple copies at different locations is not a solution because it puts a burden on resources and also creates confusion while determining the most updated data resource. Furthermore, now that big data resources are being traded among different entities, the legitimacy of a data resource poses a concern. With a blockchain for big data, we can create a decentralized data resource to which every one has full access. We can also track updates to the data resource on the blockchain, eliminating the need for and confusion due to multiple copies. Moreover, data transactions can be verified for legitimacy using blockchain concepts like proof-of-work or proof-of-stake and at the same time blockchain can provide a robust financial platform for data transactions between entities.

It is incredible how both of these technologies – big data and blockchain – can together significantly improve the usability of each other. The techniques can help create a hybrid infrastructure on pillars of big data and blockchain. The infrastructure will be flexible for different application types, like its parents – big data and blockchain.


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