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
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
"The intake layer of Datadog’s self-serve analytics platform is largely built on Airbyte.Airbyte’s ease of use and extensibility allowed any team in the company to push their data into the platform - without assistance from the data team!"
“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.”
“We chose Airbyte for its ease of use, its pricing scalability and its absence of vendor lock-in. Having a lean team makes them our top criteria. The value of being able to scale and execute at a high level by maximizing resources is immense”
- Go to the Wikimedia Downloads page for pageviews: https://dumps.wikimedia.org/other/pageviews/
- Choose the year and month you’re interested in.
- Select the day and hour, and download the desired pageviews file. These files are typically compressed using gzip, so make sure you have a tool to decompress them.
- Ensure you have Python installed on your system. If not, download and install Python from the official website: https://www.python.org/downloads/
- Install DuckDB via pip, which is Python’s package installer. Open your command line and run:
pip install duckdb - Optionally, you might want to use pandas for easier data manipulation:
pip install pandas
- Decompress the downloaded pageviews file using a tool like gzip or an integrated decompression tool in your operating system.
- Write a Python script to read the decompressed data. Wikipedia pageviews files are usually in a plain text format with space-separated values.
- Parse the data into a structured format, such as a pandas DataFrame.
- In your Python script, import DuckDB and connect to a database:
import duckdb
# This will create a new database file if it doesn't exist
con = duckdb.connect('my_wikipedia_pageviews.duckdb') - Create a table within DuckDB to store the Wikipedia pageviews data. Ensure the table schema matches the data you’re importing:
con.execute("""
CREATE TABLE pageviews (
project_code VARCHAR,
page_title VARCHAR,
view_count BIGINT,
data_size BIGINT
)
""")
Insert the data from the pandas DataFrame into the DuckDB table:import pandas as pd
# Assuming you've parsed your data into a pandas DataFrame called df
con.execute("INSERT INTO pageviews (project_code, page_title, view_count, data_size) VALUES (?, ?, ?, ?)", df.values.tolist())
Run a query to ensure that the data has been inserted correctly:
result = con.execute("SELECT * FROM pageviews LIMIT 10").fetchall()
print(result)
Once you’re done with inserting data and running any queries, close the DuckDB connection:
con.close()
Notes:
- Be aware of the data size when working with Wikipedia pageviews data, as it can be quite large. You might need to process and insert the data in chunks.
- Make sure you handle any special cases or anomalies in the Wikipedia pageviews data, such as encoding issues or unexpected data formats.
- If you’re dealing with a very large dataset, consider using DuckDB’s bulk insert features for better performance.
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:
