Analysis of Data Engineering Techniques With Data Quality in Multilingual Information Recovery - Rangineni, Sandeep; Bhanushali, Amit; Marupaka, Divya; Venkata, Srinivas; Suryadevara, Manoj (International Journal of Computer Sciences and Engineering, 2023)
It is very important for current businesses that use data that data engineering and data quality management work together. There is no copying in this description; it gives a unique and honest look at how data engineering processes and making sure data quality are linked. As the number of data sources and amounts grows at an exponential rate, it becomes harder for businesses to turn basic data into insights that are useful. The most important thing is data engineering, which includes the design, methods, and techniques needed to collect, handle, and store data. Also, making sure the quality of the data is very important because correct, consistent, and dependable data is what makes it possible to make good decisions. Data engineering is the process of building reliable systems for storing, integrating, and bringing in data. Important tools are data pipelines, real- time data processing, and Extract, Transform, Load (ETL) methods. Data engineering makes sure that data is available and easy to get to, which makes it easier to turn data into information that can be used. Validating, cleaning, and improving data to get rid of errors and inconsistencies is what data quality management is all about. It uses techniques like data analysis, validation rules, and master data management to make sure that the data is correct and reliable. Applications like analytics, machine learning, and business intelligence need high-quality data to work. Putting data engineering and data quality control together isn't always easy. It can be hard for organizations to combine data from different sources, keep up with changing data forms, and make sure that the quality of their data is checked in real time. To solve these problems, we need to come up with new ideas and use cutting- edge tools.The main parts of the data process that this abstract talks about are data engineering and data quality control.