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Begin by exploring Kyriba's documentation to understand its built-in data export capabilities. Identify the formats it supports for data export, such as CSV or XML. Familiarize yourself with the types of data you can export and any relevant settings or permissions required to execute a data export.
Use Kyriba’s interface to export the desired data. Follow the steps necessary within Kyriba to generate an export file in a format that is manageable for your needs, such as CSV or XML. Ensure you include all required fields and data points necessary for your application in DynamoDB.
After exporting the data, manually inspect the file to ensure correctness. Convert or clean the data as necessary to fit the requirements of DynamoDB. This might involve transforming data types, flattening structures, or normalizing the data format for compatibility with DynamoDB’s schema-less architecture.
Install and configure the AWS SDK for your preferred programming language (e.g., Python, JavaScript, Java). This SDK will be used to interact with DynamoDB. Ensure you have the necessary AWS credentials configured to access DynamoDB, either via AWS IAM roles or access keys.
Using the AWS Management Console or AWS CLI, create the required tables in DynamoDB. Define the primary keys and any necessary secondary indexes that align with how you intend to query the data. Ensure table structures are prepared to accommodate the data from your Kyriba export.
Develop a script using your chosen programming language to read the exported Kyriba data and load it into DynamoDB. This script should parse the exported data, transform it if necessary, and use the AWS SDK to batch write items to your DynamoDB tables. Handle any exceptions or errors that might occur during this process to ensure data integrity.
Once the data is loaded into DynamoDB, verify data integrity by running queries to check that the data matches what was exported from Kyriba. Perform testing to ensure that all data points are correctly transferred and accessible as expected. Address any discrepancies or issues discovered during this verification process.
By following these steps, you can manually move data from Kyriba 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.
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?
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