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 logging into your My Hours account. Navigate to the section where you can view the data you wish to export (such as timesheets, reports, or project data). Look for an "Export" option, which is typically available as a CSV or Excel file. Select the appropriate format and download the file to your computer.
Go to Google Sheets by visiting [sheets.google.com](https://sheets.google.com) and log in with your Google account. If you don't have a Google account, you'll need to create one to use Google Sheets.
Once in Google Sheets, click on the "+ Blank" option to create a new spreadsheet. This will serve as the destination for the data you are transferring from My Hours.
Before importing, consider setting up your Google Sheet with headers that match the data fields from My Hours. This will help in organizing the data once it is imported. You can adjust these headers later if necessary.
In your new Google Sheet, click on "File" in the top menu, then select "Import." In the Import window, choose "Upload" and drag your downloaded My Hours file into the uploader. Select "Replace spreadsheet" if it's a new sheet or "Append to current sheet" if adding to existing data. Ensure that the import settings match your data format (e.g., CSV or Excel) and click "Import data."
After the data is imported, you may need to format it for readability and usability. Adjust column widths, apply formatting to headers, and sort or filter data as necessary. This step ensures the data is presented clearly and is easy to work with.
Finally, review the imported data to ensure it transferred correctly. Check for any discrepancies such as missing data or misaligned columns. Compare a few entries with the original data from My Hours to confirm accuracy. Make any necessary adjustments directly within Google Sheets.
By following these steps, you can effectively move your data from My Hours to Google Sheets manually, without the need for third-party tools 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.
My Hours was launched back in 2002 and it is a cloud-based time-tracking solution best suited for small teams and freelancers. Since then My Hours has been rewritten twice to meet the growing demands and it is a product of Spica, a company headquartered in Ljubljana with 100+ employees. The users of My Hours can start time tracking on unlimited projects and tasks in seconds which easily generates insightful reports and create invoices.
My Hours' API provides access to a variety of data related to time tracking and project management. The following are the categories of data that can be accessed through the API:
1. Time tracking data: This includes information about the time spent on tasks, projects, and clients. It includes start and end times, duration, and any notes or comments associated with the time entry.
2. Project data: This includes information about the projects being worked on, such as project name, description, status, and associated tasks.
3. Task data: This includes information about the individual tasks within a project, such as task name, description, status, and associated time entries.
4. Client data: This includes information about the clients being worked with, such as client name, contact information, and associated projects.
5. User data: This includes information about the users of the My Hours platform, such as user name, email address, and associated time entries, projects, and tasks.
Overall, the My Hours API provides a comprehensive set of data that can be used to analyze and optimize time tracking and project management 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:
