Why Data Needs a Clean Up
Explore why data cleaning is critical for transforming raw, messy data into trustworthy information. This lesson helps you understand common sources of dirty data and learn techniques like parsing, deduplication, imputation, and validation to improve data quality and prepare it for accurate analysis.
We'll cover the following...
Just storing large volumes of data isn’t enough—it’s only the first step in making data truly usable.
Hadoop and HDFS help us gather and store huge volumes of data reliably, but what lands in those systems isn’t always neat. In fact, most real-world data arrives messy—full of missing values, inconsistent labels, irregular formats, and duplicates. It’s like ...
In fact, data professionals report spending 60 to 80% of their time cleaning and preparing data. Dirty data doesn’t just make things messy. It breaks pipelines, slows performance, and makes downstream consumers doubt the reliability of your work.
In this lesson, we’ll explore the main sources that make data dirty, the key dimensions of data quality issues we encounter, and why effective cleaning is crucial for reliable analysis.