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 Harvest account. Navigate to the Reports section and select the type of data you wish to export, such as timesheets, expenses, or invoices. Harvest allows you to export this data in CSV format. Download the CSV file to your local machine, as this will be the data you transfer to Amazon S3.
If you haven’t already, install the AWS Command Line Interface (CLI) on your local machine. The AWS CLI is a unified tool to manage your AWS services. It can be installed via package managers like `pip` for Python. Configure the AWS CLI using the command `aws configure`, and input your AWS Access Key, Secret Key, region, and output format as prompted.
Log in to your AWS Management Console and navigate to the S3 service. Select "Create bucket" and follow the instructions to create a new S3 bucket. Make sure to choose a unique bucket name and select the appropriate region. Set the bucket's permissions and configurations according to your requirements.
Organize the exported CSV files from Harvest on your local machine. Ensure that the file names and structure meet your organizational needs and will be easy to manage once uploaded to S3. This preparation will help in maintaining a clear and organized data structure within your S3 bucket.
Use the AWS CLI to upload the CSV files to your S3 bucket. Navigate to the directory containing your CSV files in your terminal. Use the command `aws s3 cp yourfile.csv s3://your-bucket-name/` to upload individual files. For multiple files, use the command `aws s3 cp . s3://your-bucket-name/ --recursive` to upload all files in the directory.
After uploading, verify that the data has been successfully transferred to S3. You can do this by accessing your S3 bucket through the AWS Management Console and checking the presence and integrity of the uploaded files. Make sure the file sizes and counts match your expectations.
If you wish to automate this process for future data transfers, consider writing a script that uses the AWS CLI commands to automate the data export from Harvest (if possible through their API), download, and upload process. Schedule this script using a task scheduler like cron (Linux/Mac) or Task Scheduler (Windows) to run at your desired intervals.
By following this guide, you'll be able to move data from Harvest to Amazon S3 without relying on third-party connectors or integrations, ensuring a more controlled and direct transfer process.
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.
Harvest is a provider of time tracking and online invoicing services for freelancers and small businesses. Harvest focuses on providing simple to use web-based software for professional services. Customers range from freelancers to creative services businesses, to team within Fortune 500 organizations and non-profits.
Harvest's API provides access to a wide range of data related to time tracking, invoicing, and project management. The following are the categories of data that can be accessed through Harvest's API:
1. Time tracking data: This includes information about the time spent on tasks, projects, and clients.
2. Invoicing data: This includes information about invoices, payments, and expenses.
3. Project management data: This includes information about projects, tasks, and team members.
4. Client data: This includes information about clients, contacts, and projects associated with them.
5. User data: This includes information about users, their roles, and permissions.
6. Reports data: This includes information about various reports generated by Harvest, such as time reports, expense reports, and project reports.
7. Account data: This includes information about the Harvest account, such as account settings, plan details, and billing information.
Overall, Harvest's API provides a comprehensive set of data that can be used to automate various business processes and gain insights into the performance of projects and teams.
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:
