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Begin by familiarizing yourself with the Wikimedia REST API, which provides access to pageview data. Visit the [Wikimedia API documentation](https://wikitech.wikimedia.org/wiki/Analytics/AQS/Pageviews) to understand the available endpoints, parameters, and the format of the data returned (usually JSON).
Set up a development environment on your local machine. You'll need a programming language capable of making HTTP requests and handling JSON data, such as Python. Ensure you have a text editor or an Integrated Development Environment (IDE) like Visual Studio Code or PyCharm.
Install any required libraries to facilitate HTTP requests and JSON parsing. For Python, you can use `requests` for making API calls and `json` for parsing data. You can install these using pip:
```bash
pip install requests
```
Write a script to fetch data from the Wikipedia Pageviews API. Use the `requests` library to make a GET request to the desired endpoint. Handle the response by parsing the JSON data. Here's a basic example in Python:
```python
import requests
response = requests.get('https://wikimedia.org/api/rest_v1/metrics/pageviews/per-article/en.wikipedia/all-access/all-agents/Python_(programming_language)/daily/20230101/20230131')
data = response.json()
```
Prepare the data for insertion into MySQL by transforming the JSON structure into a format compatible with SQL. This often involves extracting necessary fields and organizing them into tuples or dictionaries. Here"s a simple example:
```python
pageviews = [(item['timestamp'], item['views']) for item in data['items']]
```
Ensure you have a MySQL server running and accessible. Create a database and table to hold the pageview data. You can use the MySQL command line or a tool like MySQL Workbench. Here's a sample SQL command to create a table:
```sql
CREATE DATABASE wiki_data;
USE wiki_data;
CREATE TABLE pageviews (
id INT AUTO_INCREMENT PRIMARY KEY,
timestamp VARCHAR(255),
views INT
);
```
Use the `mysql-connector-python` library to connect to your MySQL database from your script and insert the transformed data. Install the library if you haven"t already:
```bash
pip install mysql-connector-python
```
Here"s an example of how to insert data:
```python
import mysql.connector
conn = mysql.connector.connect(
host='localhost',
user='your_username',
password='your_password',
database='wiki_data'
)
cursor = conn.cursor()
insert_query = "INSERT INTO pageviews (timestamp, views) VALUES (%s, %s)"
cursor.executemany(insert_query, pageviews)
conn.commit()
cursor.close()
conn.close()
```
This guide outlines a basic approach to moving data from Wikipedia pageviews to a MySQL database entirely through custom scripting, leveraging only essential libraries and MySQL's native capabilities. Adjust the scripts to fit your specific requirements and ensure proper error handling and optimization for production use.
FAQs
What is ETL?
ETL, an acronym for Extract, Transform, Load, is a vital data integration process. It involves extracting data from diverse sources, transforming it into a usable format, and loading it into a database, data warehouse or data lake. This process enables meaningful data analysis, enhancing business intelligence.
Page view statistics is a tool that is entirely available for Wikipedia pages, that helps to see how many people have visited an article during a given time period. Using Wikipedia Pageviews there are some limitations. There are many things which need to be considered before using such statistics to make conclusions about an ongoing discussion. There are also some software limitations and circumstances that may influence them, both from inside and outside Wikipedia. For aggregating per project and per project per country, a Pageview statistics are available.
The Wikipedia Pageviews API provides access to various types of data related to the pageviews of Wikipedia articles. Some of the categories of data that can be accessed through this API are:
1. Pageviews: The API provides access to the number of pageviews for a particular Wikipedia article over a specific time period.
2. Language: The API allows users to filter the data by language, enabling them to retrieve pageviews for articles in a specific language.
3. Device type: The API provides data on the type of device used to access the Wikipedia article, such as desktop, mobile, or tablet.
4. Geographic location: The API allows users to filter the data by geographic location, enabling them to retrieve pageviews for articles in a specific country or region.
5. Time period: The API provides data on pageviews over a specific time period, such as hourly, daily, weekly, or monthly.
6. Referrer: The API provides data on the source of the pageview, such as whether it was from a search engine or a social media platform.
Overall, the Wikipedia Pageviews API provides a wealth of data related to the popularity and usage of Wikipedia articles, which can be used for various research and analytical purposes.
What is ELT?
ELT, standing for Extract, Load, Transform, is a modern take on the traditional ETL data integration process. In ELT, data is first extracted from various sources, loaded directly into a data warehouse, and then transformed. This approach enhances data processing speed, analytical flexibility and autonomy.
Difference between ETL and ELT?
ETL and ELT are critical data integration strategies with key differences. ETL (Extract, Transform, Load) transforms data before loading, ideal for structured data. In contrast, ELT (Extract, Load, Transform) loads data before transformation, perfect for processing large, diverse data sets in modern data warehouses. ELT is becoming the new standard as it offers a lot more flexibility and autonomy to data analysts.
What should you do next?
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