

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 setting up the AWS SDK for Python, known as Boto3, which allows Python applications to interact with AWS services like DynamoDB. Install Boto3 using pip with the command `pip install boto3`. This will be necessary for accessing and manipulating DynamoDB tables programmatically.
Sentry provides a REST API that you can use to retrieve event data. Familiarize yourself with the Sentry API documentation and obtain an API token by navigating to your Sentry account settings. Use Python's `requests` library to make HTTP GET requests to the relevant Sentry API endpoints to fetch the data you need. For example, you might use an endpoint like `/api/0/projects/{organization_slug}/{project_slug}/events/` to list events.
Parse the JSON responses from the Sentry API to extract the required information. Use Python's built-in `json` library to handle the JSON response data. Create a function that iterates over the API response and structures the data into a format suitable for storage in DynamoDB. Ensure you handle pagination if the data set is large.
In your AWS account, create a DynamoDB table if you haven't already. Define the primary key structure (partition key and optionally a sort key) based on how you plan to query the data. For example, if you are storing Sentry event data, you might use `event_id` as the partition key.
Develop a Python script to automate the ingestion of data into DynamoDB. Use Boto3 to connect to DynamoDB and write the structured data obtained from Sentry into the table. Use the `put_item` method to insert individual records. If dealing with large data sets, consider using `batch_write_item` for more efficient batch processing.
Ensure your script includes robust error handling to manage potential issues such as network failures, API rate limits, or AWS service errors. Use Python's `logging` module to log any errors or significant events during data transfer. This will help in monitoring the process and troubleshooting if necessary.
To keep the DynamoDB data up-to-date with Sentry, consider automating the data transfer script using a task scheduler like cron jobs on Unix-based systems or Task Scheduler on Windows. Determine an appropriate frequency for the data transfer based on your needs, such as hourly, daily, or weekly.
By following these steps, you'll manually set up a process to move data from Sentry to DynamoDB 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.
Sentry is a cloud-based error monitoring platform that helps developers identify and fix issues in their applications. It provides real-time alerts and detailed error reports, allowing developers to quickly diagnose and resolve issues before they impact users. Sentry supports a wide range of programming languages and frameworks, and integrates with popular development tools like GitHub, Jira, and Slack. With features like release tracking, performance monitoring, and customizable dashboards, Sentry helps teams improve the quality and reliability of their software. Overall, Sentry is a powerful tool for any development team looking to streamline their error monitoring and debugging processes.
Sentry's API provides access to a wide range of data related to application performance monitoring and error tracking. The following are the categories of data that can be accessed through Sentry's API:
1. Events: This includes information about errors, crashes, and other events that occur within an application.
2. Issues: This includes details about specific issues that have been identified within an application, including the number of occurrences, the severity of the issue, and any associated metadata.
3. Projects: This includes information about the projects being monitored by Sentry, including project settings, integrations, and other configuration details.
4. Users: This includes information about the users who are interacting with an application, including their IP addresses, browser information, and other relevant data.
5. Releases: This includes information about the releases of an application, including version numbers, release dates, and associated metadata.
6. Performance: This includes data related to the performance of an application, including response times, error rates, and other metrics.
Overall, Sentry's API provides a comprehensive set of data that can be used to monitor and optimize the performance of an application, as well as to identify and resolve errors and other issues.
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: