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First, you need to extract the Wikipedia pageviews data. Wikipedia provides pageviews data via their RESTful API. You can use Python with the `requests` library to send HTTP GET requests to this API. The URL typically is in the format: `https://wikimedia.org/api/rest_v1/metrics/pageviews/per-article/{project}/{access}/{agent}/{article}/{granularity}/{start}/{end}`. Customize the placeholders with appropriate values for project, access, etc., to get the data you need.
Once you have the data extracted, you'll need to process and clean it. Use Python to parse the JSON response from the API. You might want to filter the data, handle any missing values, or convert it into a structured format like CSV or Parquet. Libraries such as `pandas` can be very useful for this task, allowing you to manipulate and clean the data efficiently.
AWS S3 will serve as the storage component of your data lake. Log into your AWS Management Console and navigate to S3. Create a new S3 bucket where you will store the processed Wikipedia pageviews data. Make sure to configure the bucket settings, including permissions and access policies, to ensure security and compliance with your organization's standards.
With the data cleaned and structured, the next step is to upload it to your S3 bucket. Use the AWS CLI or Boto3 (AWS SDK for Python) to programmatically upload your files. For example, using Boto3, you would use the `upload_file` method to transfer your data file into the designated S3 bucket. Ensure the right IAM permissions are in place for the operation.
AWS Glue can be used to catalog the data stored in S3. Set up an AWS Glue Crawler to automatically scan your S3 bucket and populate the AWS Glue Data Catalog with the metadata of your datasets. This will help in organizing and preparing your data for analysis. Define appropriate IAM roles and permissions for the Glue service to access your S3 data.
If further transformation is needed, create an AWS Glue job. AWS Glue allows you to write ETL (Extract, Transform, Load) scripts in Python or Scala to transform your data into the desired format. You can write scripts to filter, aggregate, or join data as necessary, and then write the transformed data back to another S3 bucket or overwrite the existing data.
Finally, use Amazon Athena to query your data directly in S3. Athena is a serverless interactive query service that makes it easy to analyze data in S3 using standard SQL. Point Athena to the AWS Glue Data Catalog you set up earlier, and start querying your pageviews data. This allows for quick analysis and insights without the need for complex data pipelines.
By following these steps, you can effectively move Wikipedia pageviews data into an AWS Data Lake 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?
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