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Begin by ensuring you have Python installed on your machine, as it will be used to fetch and process data. Also, ensure MongoDB is installed and running on your local machine or accessible on your network. You will need the `pymongo` library for interacting with MongoDB and `requests` for handling HTTP requests.
Wikipedia offers an API to access pageview data. Use Python's `requests` library to make HTTP GET requests to the Wikimedia REST API. For example, you might request data from the endpoint `https://wikimedia.org/api/rest_v1/metrics/pageviews/per-article/{project}/{access}/{agent}/{article}/{granularity}/{start}/{end}`. Replace placeholders with appropriate values to specify the project, access method, and time period you are interested in.
Once you receive the data, you will likely get it in JSON format. Parse this JSON data using Python's built-in `json` library. Extract the relevant information such as article title, view count, and timestamp. Properly format this data to make it ready for insertion into MongoDB.
Install the `pymongo` package if you haven't already by running `pip install pymongo`. This library provides the necessary tools to interact with MongoDB from Python. Configure `pymongo` by establishing a connection to your MongoDB server. Typically, this involves creating a `MongoClient` instance with the appropriate URI.
Use `pymongo` to create a new database and a collection within that database to store the Wikipedia pageviews. For example, you might create a database called `wikipedia_data` and a collection called `pageviews`. This can be done using `db = client['wikipedia_data']` and `collection = db['pageviews']`.
Use the `insert_one` or `insert_many` methods provided by `pymongo` to insert the processed data into your MongoDB collection. Ensure that the data is structured as dictionaries (or a list of dictionaries) where each dictionary represents a document in MongoDB.
After inserting the data, verify the operation by querying the MongoDB collection. Use `find` or `find_one` methods to retrieve the data and print it out to ensure that the data has been correctly inserted. This step helps in confirming the integrity and correctness of the data transfer process.
By following these steps, you will be able to manually move Wikipedia pageviews data to a MongoDB destination without the need for 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?
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