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."
Before moving data, familiarize yourself with the export capabilities of Aha!. Aha! allows you to export data into CSV files. Navigate to the relevant Aha! report or list view, and use the export feature to download the data in CSV format. Ensure that the data you need is correctly filtered and presented in the report before exporting.
Once you have identified the necessary data, export it from Aha! into a CSV file. Double-check that the export includes all necessary fields and data types. Open the CSV in a spreadsheet application like Excel or Google Sheets to verify the contents and format. Make any necessary adjustments to ensure the data is clean and consistent.
Ensure that your Oracle Database is up and running, and that you have the necessary permissions to access and load data into it. Create a target table in your Oracle Database that matches the structure of the data in your CSV file. Use SQL commands to define the table schema, including columns, data types, and any necessary constraints.
SQL*Loader is a utility that comes with Oracle Database installations and can be used to load data from external files into Oracle tables. Create a control file that specifies how to parse and load the CSV data. The control file should define the input file location, table name, and mapping of CSV fields to table columns.
Execute SQL*Loader from the command line using the control file you created. The command should specify the data file (CSV), the control file, and any additional parameters necessary for your environment. Monitor the loading process for errors and ensure all records are successfully imported into the Oracle Database.
After the data load is complete, verify that the data has been accurately imported into the Oracle Database. Use SQL queries to inspect the records in the target table, checking for data consistency and completeness. Make sure that all fields are correctly populated and that no records are missing.
If you need to regularly move data from Aha! to Oracle, consider automating the process. Write a script to automate the export from Aha!, the preparation of the CSV file, the execution of SQL*Loader, and any verification steps. Schedule this script to run at regular intervals using cron jobs (Linux) or Task Scheduler (Windows), ensuring timely updates to your Oracle Database.
By following these steps, you can directly move data from Aha! to an Oracle Database without using 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.
Aha stands for America Heart Association. This Advised Fund Program provides an easy, flexible, and tax-wise way to support all your favorite charities through one account, which is a very different kind of high-growth SaaS company. We are self-funded, completely remote, and have no sales team. We aspire to a loving software world built by happy teams. Today more than 600,000+ product builders from many of the world's most renowned companies trust our software to form a better future. So, Aha helps teams to be happy.
Aha's API provides access to a wide range of data related to product management and development. The following are the categories of data that can be accessed through Aha's API:
1. Product data: This includes information about products, features, releases, and ideas.
2. Roadmap data: This includes data related to the product roadmap, such as goals, initiatives, and timelines.
3. User data: This includes data related to users, such as their roles, permissions, and activity.
4. Integration data: This includes data related to integrations with other tools, such as Jira, Trello, and Slack.
5. Analytics data: This includes data related to product analytics, such as usage metrics, customer feedback, and market trends.
6. Custom data: This includes data that can be customized based on the specific needs of the user, such as custom fields and workflows.
Overall, Aha's API provides a comprehensive set of data that can be used to manage and develop products more effectively.
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:
