How to load data from Wikipedia Pageviews to Convex

Learn how to use Airbyte to synchronize your Wikipedia Pageviews data into Convex within minutes.

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.

Building in-house pipelines

Bespoke pipelines are:
  • Inconsistent and inaccurate data
  • Laborious and expensive
  • Brittle and inflexible
Furthermore, you will need to build and maintain Y x Z pipelines with Y sources and Z destinations to cover all your needs.

After Airbyte

Airbyte connections are:
  • Reliable and accurate
  • Extensible and scalable for all your needs
  • Deployed and governed your way
All your pipelines in minutes, however custom they are, thanks to Airbyte’s connector marketplace and AI Connector Builder.

Start syncing with Airbyte in 3 easy steps within 10 minutes

Set up a Wikipedia Pageviews connector in Airbyte

Connect to or one of 400+ pre-built or 10,000+ custom connectors through simple account authentication.

Set up Convex for your extracted Wikipedia Pageviews data

Select where you want to import data from your source to. You can also choose other cloud data warehouses, databases, data lakes, vector databases, or any other supported Airbyte destinations.

Configure the Wikipedia Pageviews to Convex in Airbyte

This includes selecting the data you want to extract - streams and columns -, the sync frequency, where in the destination you want that data to be loaded.

Take a virtual tour

Check out our interactive demo and our how-to videos to learn how you can sync data from any source to any destination.

Demo video of Airbyte Cloud

Demo video of AI Connector Builder

Setup Complexities simplified!

You don’t need to put hours into figuring out how to use Airbyte to achieve your Data Engineering goals.

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

Streamline AI workflows with Airbyte: load unstructured data into vector stores like Pinecone, Weaviate, and Milvus. Supports RAG transformations with LangChain chunking and embeddings from OpenAI, Cohere, etc., all in one operation.

Move Large Volumes, Fast

Quickly get up and running with a 5-minute setup that enables both incremental and full refreshes for databases of any size, seamlessly scaling to handle large data volumes. Our optimized architecture overcomes performance bottlenecks, ensuring efficient data synchronization even as your datasets grow from gigabytes to petabytes.

An Extensible Open-Source Standard

More than 1,000 developers contribute to Airbyte’s connectors, different interfaces (UI, API, Terraform Provider, Python Library), and integrations with the rest of the stack. Airbyte’s AI Connector Builder lets you edit or add new connectors in minutes.

Full Control & Security

Airbyte secures your data with cloud-hosted, self-hosted or hybrid deployment options. Single Sign-On (SSO) and Role-Based Access Control (RBAC) ensure only authorized users have access with the right permissions. Airbyte acts as a HIPAA conduit and supports compliance with CCPA, GDPR, and SOC2.

Fully Featured & Integrated

Airbyte automates schema evolution for seamless data flow, and utilizes efficient Change Data Capture (CDC) for real-time updates. Select only the columns you need, and leverage our dbt integration for powerful data transformations.

Enterprise Support with SLAs

Airbyte Self-Managed Enterprise comes with dedicated support and guaranteed service level agreements (SLAs), ensuring that your data movement infrastructure remains reliable and performant, and expert assistance is available when needed.

What our users say

Raman Singh

Tech Lead at Symend

Predictable, straightforward pricing model that simplified budgeting and significantly reduced overall spend

Learn more
Chase Zieman headshot

Chase Zieman

Chief Data Officer

“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.”

Learn more

Rupak Patel

Operational Intelligence Manager

"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."

Learn more

How to Sync to Manually

Step 1: Understand Wikipedia Pageviews API

Begin by familiarizing yourself with the Wikipedia Pageviews API. This API provides data on the number of views a Wikipedia page receives. Review the API documentation to learn about endpoints, parameters, and data formats, usually JSON.

Step 2: Setup a Programming Environment

Establish a programming environment with a language of your choice, such as Python, Node.js, or Ruby. Ensure you have the necessary tools and libraries for HTTP requests and data handling. For Python, libraries like `requests` and `json` are useful.

Step 3: Fetch Wikipedia Pageviews Data

Write a script to request data from the Wikipedia Pageviews API. Construct the API request URL with desired parameters such as the page title, date range, and granularity. Use your environment's HTTP client to perform GET requests and capture the response.

Step 4: Parse and Clean the Data

Process the JSON response received from the API. Extract relevant fields such as the timestamp and view count. Clean the data as needed, handling any missing or malformed entries to prepare it for insertion into your Convex database.

Step 5: Setup Convex Database

Create a Convex database if you haven't already. Use their CLI tools or web interface to initialize your database and define the schema that will store the Wikipedia pageviews data. Ensure the schema matches the structure of the data you plan to insert.

Step 6: Write Data Insertion Script

Develop a script to insert the cleaned data into your Convex database. Establish a connection to your database using Convex's APIs or SDK, authenticate if necessary, and use database commands to insert each record. Handle any errors or exceptions during this process.

Step 7: Schedule Regular Updates

To keep your data current, automate the process of fetching and inserting Wikipedia pageviews. Use a scheduling tool like `cron` on Unix-based systems or Task Scheduler on Windows, or write a script to periodically run the data fetching and insertion scripts at desired intervals.

By following these steps, you can effectively move data from Wikipedia pageviews to a Convex database manually, without relying on third-party connectors or integrations.