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Begin by exploring the data export capabilities within Kyriba. Review the Kyriba documentation or contact your Kyriba account representative to determine the formats (e.g., CSV, XML) and methods (e.g., scheduled reports, manual exports) available for extracting data from the platform.
Configure Kyriba to automatically generate and export the required data at regular intervals. Use Kyriba's built-in reporting or export functionality to schedule these exports. Ensure the data is saved in a secure location on your local environment where it can be accessed for further processing.
Write a script in a language of your choice (e.g., Python, Bash) that will handle the downloaded data files. This script should be capable of performing any necessary transformations or validations on the data before it is uploaded to S3. Ensure the script is configured to run automatically after the data export completes.
Download and install the AWS Command Line Interface (CLI) on your local machine or the server where the data export script will run. Configure the AWS CLI with your credentials and default region by running `aws configure`. This will allow you to interact with your S3 buckets directly from the command line.
Log into your AWS Management Console and create a new S3 bucket, specifying the appropriate region and setting up necessary permissions. Ensure the bucket's policy allows for data uploads from the IP range of your local environment, but restricts public access for security.
Integrate AWS CLI commands into your local script to automate the process of uploading files to the S3 bucket. Use the `aws s3 cp` command to copy files from your local system to the S3 bucket. The script should handle any errors or retries to ensure data integrity and successful uploads.
Implement logging in your script to track each step of the data handling and upload process. Regularly review these logs to ensure that data exports and uploads occur as expected. Additionally, periodically check the S3 bucket to verify that files are being transferred correctly and maintain data integrity.
By following these steps, you can efficiently move data from Kyriba to Amazon S3 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.
Kyriba is a global leader in cloud treasury and finance solutions, providing mission-critical capabilities for cash and risk management, payments, and working capital solutions. More than 2,500 clients worldwide rely on Kyriba to view, protect and grow their liquidity. Kyriba has connectivity in its DNA and is driven by research and innovation to uncover new ways to use APIs, artificial intelligence, and predictive analytics to support our customers. It unifies cloud offerings with a truly global community of customers, partners, and talented employees reaching over 100 countries worldwide.
Kyriba's API provides access to a wide range of financial data, including:
1. Cash Management Data: This includes information on cash balances, bank accounts, and transactions.
2. Payment Data: This includes details on payments made and received, including payment method, amount, and date.
3. FX Data: This includes exchange rates and currency conversion information.
4. Risk Management Data: This includes data on financial risks such as market risk, credit risk, and liquidity risk.
5. Treasury Management Data: This includes information on treasury operations such as cash forecasting, cash positioning, and cash pooling.
6. Compliance Data: This includes data on regulatory compliance, such as anti-money laundering (AML) and know your customer (KYC) requirements.
7. Reporting Data: This includes data on financial reporting, such as balance sheets, income statements, and cash flow statements.
Overall, Kyriba's API provides a comprehensive set of financial data that can be used to manage cash, payments, risk, compliance, and reporting.
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:
