Data Merging | Web Scraping Tool | ScrapeStorm

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Introduction

Data merging is the aggregation and consolidation of information from different data sources or data tables into a single data set for better analysis, processing, or reporting. This process typically involves combining data from different sources to eliminate data redundancy, handle discrepant data, ensure data consistency and comparability, and use it in deeper analysis and formulated decisions. It involves integrating the data into a consistent data structure.

Applicable Scene

Data merging is commonly used in areas such as data analytics, business reporting, and data warehousing to provide a more comprehensive and accurate view of data, helping organizations better understand their data and discover correlations and trends. , to better support business decisions and issues. Resolving.

Pros: Improve data integrity, provide a global view, improve data quality, support decision making, save time and resources, increase data availability, discover new insights, improve data consistency, machine learning and Providing more data for forecasting and fostering cross-departmental collaboration.

Cons: Increased complexity, risk of data conflicts, data security issues, increased costs, potential for data loss, time and resource needs, need for standardization, additional data cleaning required, more storage If you need space, you may need specialized skills.

Legend

1. Data merging.

2. Data merging.

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Reference Link

https://nanonets.com/blog/what-is-data-merging/

https://bookdown.org/khueniken/Merging_with_dplyr/types-of-data-merging.html

https://sebastiz.github.io/gastrodatascience/Data_merge.html