Summarize
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."
Begin by setting up access to the Plausible Analytics API. You'll need to generate an API key from your Plausible account settings. This key will allow you to programmatically access data from Plausible. Ensure you have the necessary permissions to access the data you need.
Clearly define what data you need to transfer from Plausible to ClickHouse. This could include metrics like page views, unique visitors, referrers, and more. Knowing exactly what data is required will help streamline the extraction process.
Develop a script using a programming language such as Python. Use HTTP requests to interact with the Plausible API and extract the required data. The script should handle authentication using the API key and include error handling to manage any issues during data retrieval.
Once the data is extracted, transform it into a format suitable for insertion into ClickHouse. This often means converting JSON or other structured data formats into CSV or TSV, which ClickHouse can efficiently ingest. Ensure that the data types align with the schema of your ClickHouse tables.
Set up your ClickHouse environment if you haven't already. Create the necessary database and tables to store the Plausible data. Define the schema based on the transformed data, ensuring that data types and columns match the transformed data structure.
Write a script, again using a language like Python or Bash, that uses ClickHouse HTTP or native protocols to insert the transformed data into the database. This script should read the transformed data files and execute the loading process. Optimize the script for bulk data loading to improve performance.
Finally, automate the entire process to ensure data is periodically updated in ClickHouse. Use cron jobs on a Linux server or Task Scheduler on Windows to run both the extraction and loading scripts at desired intervals. This will ensure your ClickHouse warehouse remains in sync with the latest data from Plausible.
By following these steps, you can successfully move data from Plausible to ClickHouse 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.
Appreciable Analytics is an open-source project dedicated to making web analytics more privacy-friendly. Our goal is to reduce corporate surveillance by providing an alternative web analytics tool that doesn't come from the AdTech world. Trusted by thousands of paying customers. We are completely independent, self-funded, and bootstrapped. The legal entity is incorporated in Estonia, while our team works remotely and flexibly.
Plausible's API provides access to a variety of data related to website traffic and user behavior. The following are the categories of data that can be accessed through Plausible's API:
1. Site Metrics: This category includes data related to the overall performance of a website, such as the number of page views, unique visitors, bounce rate, and average session duration.
2. Traffic Sources: This category includes data related to the sources of traffic to a website, such as search engines, social media, direct traffic, and referral traffic.
3. User Behavior: This category includes data related to user behavior on a website, such as the pages visited, time spent on each page, and the actions taken on the website.
4. Geolocation: This category includes data related to the geographic location of website visitors, such as the country, region, and city.
5. Devices: This category includes data related to the devices used by website visitors, such as desktop, mobile, and tablet.
6. Browsers: This category includes data related to the browsers used by website visitors, such as Chrome, Firefox, Safari, and Internet Explorer.
Overall, Plausible's API provides a comprehensive set of data that can be used to analyze website traffic and user behavior, and to make data-driven decisions to improve website performance.
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:
