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 thoroughly reviewing Timely's documentation to understand how data can be exported. Identify available export formats (e.g., CSV, JSON) and determine if Timely offers any APIs for data extraction. This foundational knowledge will guide you in extracting data in a format suitable for further processing.
Use Timely's export features to extract the required data. This might involve setting up automated exports, if available, or manually exporting data in your preferred format. If Timely provides an API, write a script to fetch data programmatically. Ensure that the data export schedule aligns with your data processing needs.
Develop a script to parse the exported data into a structured format. If the data is exported as CSV, use a library like Python's `csv` module to read and structure it. For JSON, utilize a JSON parser. The goal is to transform the raw exported data into a structured format that can be processed and published to RabbitMQ.
Ensure RabbitMQ is installed and properly configured on your server. Follow the official RabbitMQ installation guide for your operating system. Once installed, configure the necessary exchange and queue settings. Create a new exchange and queue if necessary, and configure them to handle the incoming data.
Write a script to publish the parsed data to RabbitMQ. Use a programming language that supports RabbitMQ libraries, such as Python with `pika`. Establish a connection to your RabbitMQ server, and use the `basic_publish` method to send the data to the previously configured exchange and queue. Ensure that your script handles data serialization appropriately.
Enhance your RabbitMQ publisher script with robust error handling and logging. Capture and log exceptions that may occur during data parsing or message publishing. Implement retry logic for transient errors, such as network issues, to ensure reliable data transfer. Logging is crucial for troubleshooting and monitoring the data flow.
Thoroughly test the entire data transfer process to ensure data integrity and reliability. Verify that data from Timely is correctly exported, parsed, and published to RabbitMQ. Conduct tests with different data volumes and scenarios to identify potential bottlenecks or issues. Once testing is complete, deploy the solution to a production environment and continuously monitor its performance.
By following these steps, you can effectively transfer data from Timely to RabbitMQ 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.
Timely's time tracking software , which helps teams stay connected and report accurately across client, project and employee hours. Using Timely's software one can manage their business, connect with their peers and access education from global industry. Timely is used to narrate something that happens at the right time or the scheduled time, as in a timely payment or a timely delivery. Timely Event Software, the top event technology and tools to automate and simplify the management of events, venues and learning.
Timely'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 Timely's API:
1. Time tracking data: This includes data related to the time spent on tasks, projects, and clients.
2. Project management data: This includes data related to project timelines, milestones, and budgets.
3. User data: This includes data related to user profiles, roles, and permissions.
4. Billing data: This includes data related to invoices, payments, and expenses.
5. Reporting data: This includes data related to reports on time tracking, project management, and billing.
6. Integration data: This includes data related to integrations with other tools and platforms. 7. Custom data: This includes data that can be customized based on the specific needs of the user.
Overall, Timely's API provides a comprehensive set of data that can be used to improve time tracking, project management, and billing processes.
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:
