Summarize


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 ensuring that AWS CloudTrail is enabled in your AWS account. Navigate to the CloudTrail console and create a trail if one doesn't exist. Configure the trail to log events to an S3 bucket. This bucket will serve as the storage location for CloudTrail logs.
Use AWS Identity and Access Management (IAM) to create a user or role with the necessary permissions to access the S3 bucket. Attach policies like `AmazonS3ReadOnlyAccess` to this user or role. Generate access keys if creating a user, and note these credentials as they'll be needed later.
Download and install SnowSQL, Snowflake's command-line interface, on your local machine or a server. Configure it with the necessary connection details for your Snowflake account, including your account name, user, warehouse, database, and schema. Use the `snowsql -a -u ` command to set up the connection.
In Snowflake, create an external stage that points to the S3 bucket containing CloudTrail logs. Use the following SQL command in SnowSQL:
```sql
CREATE STAGE my_s3_stage
STORAGE_INTEGRATION = (your_storage_integration)
URL = 's3://your-cloudtrail-bucket-name/'
FILE_FORMAT = (TYPE = 'JSON');
```
Ensure your Snowflake account has the necessary access to the S3 bucket through a storage integration.
Create a target table in Snowflake where you want to load the CloudTrail data. Use the `COPY INTO` command to load data from the S3 stage into this table:
```sql
CREATE OR REPLACE TABLE cloudtrail_logs (
event_id STRING,
event_time TIMESTAMP,
event_name STRING,
user_identity STRING,
aws_region STRING,
source_ip_address STRING,
user_agent STRING,
-- Add other fields as needed
);
COPY INTO cloudtrail_logs
FROM @my_s3_stage
FILE_FORMAT = (TYPE = 'JSON')
PATTERN = '.*your-log-file-pattern.*';
```
To keep your CloudTrail logs in Snowflake updated, automate the data loading process. Use AWS Lambda or a cron job on an EC2 instance to periodically trigger the `COPY INTO` operation. Use the Snowflake REST API or a script that utilizes SnowSQL for this task.
Regularly check the Snowflake table to ensure data is being loaded correctly. Use Snowflake's query capabilities to validate the integrity and completeness of the data. Set up alerts or dashboards in Snowflake for monitoring purposes, ensuring any discrepancies or issues are quickly identified and addressed.
By following these steps, you can efficiently transfer your AWS CloudTrail data to the Snowflake Data Cloud without relying on third-party tools.
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.
AWS CloudTrail is a web service developed to simplify and provide assistance with AWS accounts. Enabling compliance, governance, and operational and risk auditing, it allows users to monitor, log, and document AWS account-related activity in an easily searchable format. With its comprehensive account event history function, CloudTrail helps users analyze and troubleshoot security and operational issues, detect unusual account activity, and much more by increasing visibility into customers’ user and resource activity.
AWS CloudTrail provides access to a wide range of data related to AWS account activity and resource usage. The following are the categories of data that can be accessed through the API:
1. Event history: This includes information about all the events that have occurred in an AWS account, such as API calls, console sign-ins, and resource changes.
2. Resource activity: This category includes data related to the usage of AWS resources, such as EC2 instances, S3 buckets, and RDS databases.
3. User activity: This category includes data related to user activity in an AWS account, such as user sign-ins, password changes, and access key usage.
4. Security analysis: This category includes data related to security events in an AWS account, such as failed login attempts, unauthorized access attempts, and changes to security groups.
5. Compliance auditing: This category includes data related to compliance auditing in an AWS account, such as changes to IAM policies, CloudTrail configuration changes, and VPC network changes.
Overall, the AWS CloudTrail API provides a comprehensive view of AWS account activity and resource usage, making it a valuable tool for monitoring and managing AWS environments.
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: