• B.Yasaswi
• B.Hanu Krishna
• B.Vamsi Krishna
• B V Subba Rao
• S.Sai Kumar

The problem of data cleaning, which consists of removing inconsistencies and errors from original data sets, is well known in the area of decision support systems and data warehouses. This holds regardless of the application - relational database joining, web-related, or scientific. In all cases, existing ETL (Extraction Transformation Loading) and data cleaning tools for writing data cleaning programs are insufficient. The main reason for using the computers is to organize the data in an efficient and effective manner .In early days for valuable data can be organization sake we have to use the tools like Queries. In these some problems are arises. That is why these languages are called as Data Management systems. There were so many limitations in the management system like data inconsistency, inconvenience in retrieval of data etc. Because of all these limitations we have to face the problems like memory inefficiency and heavy in consumption of time and also lack of quality. To overcome all these problems we have designed a software(what I mean is ETL tool) which organizes the data in a very efficient manner with respect to redundant data. Our project deals with the data organization by giving all data oriented features and by solving the problems like data inconsistency and data redundancy. Data from different data sources are usually first transformed and cleaned before being loaded into the data warehouse Data Cleaning (cleansing or scrubbing) is the process of detecting and removing errors, inconsistencies and data redundancies from data in order to improve the quality of data.

data cleansing, data warehouse, ETL, redundancy

"Exterminate Inconsistencies and Errors from Data Sets with Data Sanitization", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 12, page no.f1-f4, December-2023, Available :http://www.jetir.org/papers/JETIR2312501.pdf

"Exterminate Inconsistencies and Errors from Data Sets with Data Sanitization", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 12, page no. ppf1-f4, December-2023, Available at : http://www.jetir.org/papers/JETIR2312501.pdf