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."
To begin, you need to fetch data from Clockify. Log in to your Clockify account and navigate to the API section. Here, you will find your API key, which you will use to authenticate your requests to the Clockify API.
Use a programming language such as Python to make HTTP GET requests to Clockify's API endpoints. You can use the `requests` library in Python. For example, to retrieve time entries, send a request to `https://api.clockify.me/api/v1/workspaces/{workspaceId}/time-entries`. Ensure you include your API key in the request headers for authentication.
Once you have fetched the data, process it as needed. You may want to convert the JSON response into a more usable format, such as CSV or JSONL (JSON Lines). This can be done using Python's `pandas` library to manipulate and structure the data into the desired format.
Install and configure the AWS Command Line Interface (CLI) on your local machine if you haven't already. You can do this by following the instructions on the AWS CLI installation page. Authenticate using your AWS credentials to ensure you have access to your S3 buckets.
Log in to the AWS Management Console, navigate to the S3 service, and create a new bucket if you don't have one already. Note the bucket name and region, as you'll need these details to upload your files.
Use the AWS CLI to upload the processed data file to your S3 bucket. If your data is in a file named `clockify_data.csv`, you can use the command:
```
aws s3 cp clockify_data.csv s3://your-bucket-name/path/to/destination/
```
Ensure you replace `your-bucket-name` and `path/to/destination/` with your actual bucket name and desired path.
After uploading, verify that the data is correctly stored in your S3 bucket. You can do this by checking the S3 bucket in the AWS Management Console or by using the AWS CLI to list the contents of the bucket:
```
aws s3 ls s3://your-bucket-name/path/to/destination/
```
Confirm that your file is present and is of the expected size.
This guide provides a direct method to move data from Clockify to S3 using API requests and AWS CLI, avoiding third-party 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.
Clockify is the most popular free time tracker and timesheet app for teams of all sizes. Unlike all the other time trackers, Clockify lets you have an unlimited number of users for free. Clockify is an online app that works in a browser, but you can also install it on your computer or phone. Clockify is largely used by everyone from freelancers, small businesses, and agencies, to government institutions, NGOs, universities, and Fortune 500 companies.
Clockify's API provides access to a wide range of data related to time tracking and project management. The following are the categories of data that can be accessed through Clockify's API:
1. Time entries: This includes data related to the time spent on tasks, projects, and clients.
2. Projects: This includes data related to the projects being worked on, such as project name, description, and status.
3. Clients: This includes data related to the clients associated with the projects, such as client name, contact information, and billing details.
4. Users: This includes data related to the users who are using Clockify, such as user name, email address, and role.
5. Workspaces: This includes data related to the workspaces created in Clockify, such as workspace name, description, and settings.
6. Reports: This includes data related to the reports generated in Clockify, such as time spent on projects, tasks, and clients.
Overall, Clockify's API provides access to a comprehensive set of data that can be used to track time, manage projects, and generate reports.
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:
