Logo

Data Wrangling vs. Data Engineering

March 30, 2022

Introduction

Data analysis is an essential aspect of any business that wants to make informed decisions based on relevant data. However, data is often not readily available as it needs to be collected and prepared for analysis. That is where data wrangling and data engineering come in. In this blog post, we will compare data wrangling and data engineering, highlighting their similarities, differences, and importance.

Data Wrangling

Data wrangling is the process of cleaning and transforming raw data into a format suitable for analysis. It involves tasks such as filtering, merging, aggregating, and imputing data. Data wrangling is essential because it ensures that the data used for analysis is accurate, complete, and free from errors.

According to a survey by Forrester Consulting, data wrangling takes up about 80% of a data analyst's time. Data wrangling can be done both manually and with the use of software.

Data Engineering

Data engineering, on the other hand, is the process of designing, building, and maintaining the infrastructure needed to collect, store, and process big data. Data engineers are responsible for creating data pipelines, designing databases, and building data warehouses.

Data engineering is essential because it ensures that the data used for analysis is accessible, scalable, and secure. In comparison to data wrangling, data engineering requires specialized skills and expertise in database architecture and computer programming.

Similarities and Differences

While data wrangling and data engineering have different focus areas, there are a few similarities between the two. Both processes are essential to ensure that the data used for analysis is accurate, complete, and free from errors. Additionally, both processes require collaboration between different teams and departments within an organization.

The main difference between data wrangling and data engineering is the focus area. Data wrangling focuses on preparing data for analysis, while data engineering focuses on designing and building the infrastructure needed to collect and process data. As a result, data engineering requires more technical expertise than data wrangling.

Importance

Both data wrangling and data engineering are essential in the data analytics field. Data wrangling ensures that data is cleaned and transformed before analysis, while data engineering ensures that the infrastructure needed to collect and process data is designed and built correctly.

Without data wrangling and data engineering, data analysts would not have access to clean, accurate, and complete data. Ultimately, this would lead to inaccurate analysis and flawed decision-making.

Conclusion

In conclusion, data wrangling and data engineering are both essential processes in the data analytics field. Data wrangling ensures that the data used for analysis is clean and error-free, while data engineering ensures that the infrastructure needed to collect and process data is designed and built correctly.

While data wrangling and data engineering have different focus areas, they are similar in that they are both critical to successful data analysis. By understanding the differences between these processes, businesses can ensure that they are effectively collecting, preparing, and analyzing relevant data.

References

© 2023 Flare Compare