Difference between Data Warehouse and Hadoop

Data Warehouse and Hadoop are two commonly used technologies that serve as the repositories of large amounts of data. In their essence, while both aim at addressing the need for data storage and analysis they are quite distinct in their structure, performance, and applications. This article will further explain the major differences between Data Warehouse and Hadoop to enable readers to distinguish between the right solution to use.

What is a Data Warehouse?

It is a technique for gathering and managing information from different sources to supply significant commercial enterprise insights. A Data warehouse is commonly used to join and analyze commercial enterprise information from heterogeneous sources. It acts as the heart of the BI system which is constructed for data evaluation and reporting.

Advantages of Data Warehouse

• Structured Data Handling: Most appropriate when dealing with data that is formatted in a specific way, and therefore, appropriate where the user knows the questions he or she will be answering in advance.
• Fast Query Performance: Meant for database or data retrieval to be precise and SQL-based which helps in running quick queries for analysis.
• Data Integrity and Consistency: Data quality is high since data is cleaned, transformed, and loaded within the same method hence maintaining its quality.
• Historical Data Storage: Records the information in the database and allows information sorting according to time intervals.

Disadvantages of Data Warehouse

• Costly Implementation: Data warehouse creation and management is a costly affair in terms of investment in hardware, software, and human resources possessing suitable skill sets.
• Limited Scalability: This means that with very large data sets there may be problems in scaling traditional data warehouses.
• Rigid Schema: Stands for predefined schema, and thus is not as adaptable when it comes to processing unstructured or semi-structured data.

What is Hadoop?

It is an open-source software program framework for storing information and strolling applications on clusters of commodity hardware. It offers large storage for any sort of data, extensive processing strength, and the potential to deal with actually limitless concurrent duties or jobs.

Advantages of Hadoop

• Scalability: There is also the ability of Hadoop to scale to large data sizes, that are of the petabyte order and can span different servers.
• Cost-Effective: This is an open source based system implying that one can implement it on absolute low cost PCs for storage and processing.
• Flexibility: It deals with structured, semi-structured as well as unstructured data making it very useful for different data types.
• Fault Tolerance: It makes copies of data that are mirrors of the original data and distributed on nodes, thus making data recoverable in the event of a nodal failure.

Disadvantages of Hadoop

• Complexity: Managing Hadoop is not easy and needs professional skills and effort to be made for setting up as well as for sustenance.
• Performance: Though scalable, Hadoop consumes more time than a typical data warehouse while doing real time query processing.
• Security Concerns: Hadoop has integrated security feature that are not very robust and thus can only be supplemented with third-party tools for data security.

Difference Between Data Warehouse and Hadoop

Conclusion

Even though both are used to store big data, Data Warehouse and Hadoop are used for different purposes Data Warehouse tool is used to store structured data while Hadoop is used to store unstructured data both can be used to store large amount of data. DW contains well formatted data that is more suitable for analysis for business intelligence while Hadoop is appropriate for the large amounts of unformatted data. It all comes down to the kind of data that you want to process, the amount of money you have to spend, and general rigidity of your program.