What are the commonly used tools and libraries for data preprocessing?

Commonly used tools and libraries for data preprocessing include:

Pandas: A powerful library in Python for data manipulation and analysis. It provides data structures like DataFrames and Series that make it easy to handle structured data.

Example: Using Pandas to clean a dataset by dropping missing values.
```
import pandas as pd
df = pd.read_csv('data.csv')
df_cleaned = df.dropna()
```
NumPy: A library for numerical computing in Python. It provides support for large, multi-dimensional arrays and matrices, along with a collection of mathematical functions to operate on these arrays.

Example: Using NumPy to normalize data.
```
import numpy as np
data = np.array([1, 2, 3, 4, 5])
normalized_data = (data - np.mean(data)) / np.std(data)
```
Scikit-learn: A machine learning library in Python that includes a variety of tools for data preprocessing, such as scaling, encoding categorical variables, and handling missing values.

Example: Using Scikit-learn to perform one-hot encoding on categorical data.
```
from sklearn.preprocessing import OneHotEncoder
encoder = OneHotEncoder()
encoded_data = encoder.fit_transform(data[['category_column']])
```
Apache Spark: An open-source distributed computing system that provides APIs for Python, Java, Scala, and R. It is used for large-scale data processing and includes libraries for data preprocessing.

Example: Using PySpark to filter and transform data.
```
from pyspark.sql import SparkSession
spark = SparkSession.builder.appName("example").getOrCreate()
df = spark.read.csv('data.csv', header=True, inferSchema=True)
df_filtered = df.filter(df['value'] > 10)
```
OpenRefine: A desktop application for cleaning and transforming messy data. It provides a visual interface for tasks like data parsing, transformation, and enrichment.

Example: Using OpenRefine to split a column into multiple columns based on a delimiter.

For cloud-based data preprocessing, services like Tencent Cloud's Data Processing Service (DPS) can be utilized. DPS offers scalable data processing capabilities that can handle large volumes of data efficiently, supporting various data preprocessing tasks through its rich set of tools and APIs.