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First, ensure that AWS CloudTrail is enabled in your AWS account. Configure CloudTrail to deliver logs to an Amazon S3 bucket. In the AWS Management Console, navigate to CloudTrail, create a new trail or modify an existing one, and specify an S3 bucket where the logs will be stored.
Create an AWS Identity and Access Management (IAM) role with the necessary permissions. This role should allow access to the S3 bucket containing the CloudTrail logs. Define a policy that grants `s3:GetObject` permission for the S3 bucket and attach it to the role to ensure seamless access when extracting logs.
Use the AWS CLI to download the CloudTrail logs from your specified S3 bucket. The command `aws s3 cp s3:/// / --recursive` can be used to recursively copy files from the S3 bucket to a local directory on your machine or a secure EC2 instance.
AWS CloudTrail logs are stored in JSON format. Write a script in Python, using libraries like `json` and `csv`, to parse the JSON logs and convert them into a CSV format. Ensure that the CSV headers reflect the necessary data fields you want to import into Firebolt.
Access your Firebolt account and set up a database if you haven’t already. Create a new table with a schema that matches the structure of your CSV file. Use Firebolt’s SQL console to define the table with the appropriate columns and data types.
Upload the transformed CSV files back to an S3 bucket if necessary for access from Firebolt. Create an external table in Firebolt that points to your S3 bucket location. Use Firebolt’s SQL syntax to create an external table that can read your CSV files from S3, then use an `INSERT INTO` statement to load the data from the external table into your Firebolt table.
To automate the data transfer process, create an AWS Lambda function that triggers every time new CloudTrail logs are delivered to your S3 bucket. The Lambda function should execute the data download, transformation, and loading steps automatically. Use AWS CloudWatch Events to trigger the Lambda function based on S3 object creation events.
By following these steps, you can efficiently move data from AWS CloudTrail to Firebolt 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.
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:
