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Ensure that you have an AWS account and the necessary permissions to access S3. Install the AWS Command Line Interface (CLI) and configure your credentials. Run `aws configure` and provide your AWS Access Key, Secret Key, region, and output format.
You will need the `requests` library to fetch data from Wikipedia and `boto3` to interact with AWS S3. Install these libraries using pip:
```bash
pip install requests boto3
```
Use the Wikipedia Pageviews API to fetch the desired data. This can be done using the `requests` library in Python. For example:
```python
import requests
def fetch_wikipedia_pageviews():
url = 'https://wikimedia.org/api/rest_v1/metrics/pageviews/per-article/en.wikipedia/all-access/all-agents/Python_(programming_language)/daily/20230101/20230131'
response = requests.get(url)
if response.status_code == 200:
return response.json()
else:
raise Exception("Failed to fetch data from Wikipedia API")
pageviews_data = fetch_wikipedia_pageviews()
```
Convert the fetched JSON data into a format suitable for storage (e.g., CSV or JSON string). You may use Python's `json` or `csv` modules for this. Here’s an example of converting JSON to a string:
```python
import json
def prepare_data_for_s3(data):
return json.dumps(data, indent=4)
prepared_data = prepare_data_for_s3(pageviews_data)
```
Create an S3 client using Boto3 to handle the upload of data to your bucket. Ensure you specify the correct AWS region:
```python
import boto3
s3_client = boto3.client('s3', region_name='us-west-2') # Replace with your AWS region
```
Use the `put_object` method from the S3 client to upload your data. Specify the bucket name and the object key (filename) for the data:
```python
def upload_to_s3(data, bucket_name, object_key):
s3_client.put_object(Body=data, Bucket=bucket_name, Key=object_key)
bucket_name = 'your-bucket-name' # Replace with your bucket name
object_key = 'wikipedia_pageviews.json' # File name in S3
upload_to_s3(prepared_data, bucket_name, object_key)
```
After uploading, verify that the object exists in the bucket and set the necessary permissions if the data needs to be publicly accessible. You can check the upload using the AWS Management Console or AWS CLI:
```bash
aws s3 ls s3://your-bucket-name/
```
Optionally, set the permissions on the object using the console or with a bucket policy in the AWS Management Console.
By following these steps, you can move data from Wikipedia Pageviews to S3 without relying on third-party connectors or integrations.
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?
Hope you enjoyed the reading. Here are the 3 ways we can help you in your data journey: