Summarize this article with:
Trusted by data-driven companies
Building your pipeline or Using Airbyte
Airbyte is the only open source solution empowering data teams to meet all their growing custom business demands in the new AI era.
- Inconsistent and inaccurate data
- Laborious and expensive
- Brittle and inflexible
- Reliable and accurate
- Extensible and scalable for all your needs
- Deployed and governed your way
Start syncing with Airbyte in 3 easy steps within 10 minutes
Take a virtual tour
Demo video of Airbyte Cloud
Demo video of AI Connector Builder
Setup Complexities simplified!
Simple & Easy to use Interface
Airbyte is built to get out of your way. Our clean, modern interface walks you through setup, so you can go from zero to sync in minutes—without deep technical expertise.
Guided Tour: Assisting you in building connections
Whether you’re setting up your first connection or managing complex syncs, Airbyte’s UI and documentation help you move with confidence. No guesswork. Just clarity.
Airbyte AI Assistant that will act as your sidekick in building your data pipelines in Minutes
Airbyte’s built-in assistant helps you choose sources, set destinations, and configure syncs quickly. It’s like having a data engineer on call—without the overhead.
What sets Airbyte Apart
Modern GenAI Workflows
Move Large Volumes, Fast
An Extensible Open-Source Standard
Full Control & Security
Fully Featured & Integrated
Enterprise Support with SLAs
What our users say
Andre Exner
"For TUI Musement, Airbyte cut development time in half and enabled dynamic customer experiences."
Chase Zieman
“Airbyte helped us accelerate our progress by years, compared to our competitors. We don’t need to worry about connectors and focus on creating value for our users instead of building infrastructure. That’s priceless. The time and energy saved allows us to disrupt and grow faster.”
Rupak Patel
"With Airbyte, we could just push a few buttons, allow API access, and bring all the data into Google BigQuery. By blending all the different marketing data sources, we can gain valuable insights."
Start by accessing the public Wikipedia Pageviews API. This API provides data on the number of views for Wikipedia articles. Use an HTTP client like `requests` in Python to make GET requests to the API endpoint, specifying the article title and date range for which you want to retrieve the data.
Once you receive the response from the API, parse the JSON data to extract the relevant information. Typically, you'll want to extract fields such as the article title, the number of views, and the date of the views. Use Python’s built-in JSON parsing utilities to handle this.
Before you can use Firestore, you need to set up a Google Cloud Project. Go to the Google Cloud Console, create a new project, and enable the Firestore API. Note down the project ID, as it will be used to authenticate and connect to Firestore.
Install the Firebase Admin SDK in your environment using a package manager like `pip`. Run `pip install firebase-admin`. Then, generate a private key file for your service account in the Google Cloud Console. Use this key to initialize the Firebase Admin SDK in your Python script, allowing you to authenticate and access Firestore.
Plan and create a Firestore database structure that will store the Wikipedia pageviews data. Typically, you might create a collection with documents named after article titles, and each document containing fields for date and view count. Use the Firestore console or the SDK to create this structure.
Using the initialized Firebase Admin SDK, write the parsed Wikipedia pageviews data into Firestore. For each entry in your data set, create a document in the appropriate Firestore collection. Use the SDK’s methods to add or update documents with the extracted view count and date information.
Set up a cron job or equivalent scheduling mechanism on your local machine or server to automate the data fetching and updating process. This will ensure that your Firestore database remains up-to-date with the latest Wikipedia pageviews without manual intervention. Use tools like `cron` for Unix-based systems or `Task Scheduler` for Windows to run your script at regular intervals.
By following these steps, you can efficiently transfer Wikipedia pageviews data into Google Firestore 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:
