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Begin by exporting the necessary data from Kyriba. Log in to your Kyriba account and navigate to the data reports or export section. Choose the appropriate files or datasets you wish to export and select a suitable format, such as CSV or XML, which can be easily processed later. Save the exported files to a secure location on your local system.
Log in to your AWS Management Console and create an S3 bucket that will serve as the data repository in your AWS Data Lake. Navigate to the S3 service, click on 'Create bucket', and follow the prompts to configure the bucket settings, including naming, region selection, and access permissions. Ensure the bucket is secure and configured to allow access only to authorized users.
Install the AWS Command Line Interface (CLI) on your local machine if it is not already installed. The AWS CLI will enable you to interact with AWS services from your command line. After installation, configure the AWS CLI using the command `aws configure`. Enter your AWS Access Key ID, Secret Access Key, default region, and preferred output format to authenticate and set up the CLI.
Use the AWS CLI to upload the exported Kyriba data files to your S3 bucket. Open your command prompt or terminal, navigate to the directory containing the exported files, and execute the command `aws s3 cp s3:///` for each file you wish to upload. This command copies the files from your local system to the specified S3 bucket.
AWS Glue is a fully managed ETL service that can be used to prepare your data for analysis. In the AWS Management Console, navigate to AWS Glue and create a new Glue Crawler. Configure the Crawler to scan your S3 bucket and identify the schema of the uploaded data. Once configured, run the Crawler to populate the Glue Data Catalog with metadata about your data files.
After successfully cataloging the data, create an AWS Glue Job to transform the data as necessary. This may involve converting file formats, cleaning data, or aggregating data sets. Configure the Glue Job using Python or Scala scripts to define the transformations. Execute the Glue Job to process and transform the data in your S3 bucket as required.
Amazon Athena is an interactive query service that lets you analyze data in S3 using standard SQL. Navigate to the Athena service in the AWS Management Console, and ensure it is configured to query the data in your Glue Data Catalog. Use Athena to write and execute SQL queries on your data, enabling you to analyze and gain insights from the data you transferred from Kyriba to your AWS Data Lake.
By following these steps, you can successfully move and process data from Kyriba to an AWS Data Lake 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:
