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Begin by logging into your Kyriba account. Navigate to the section where your desired data is located. Use the built-in export functionality to download your data. This is typically done by exporting the data in a CSV or Excel format. Ensure that the export contains all necessary fields needed for your analysis in BigQuery.
Once you've exported the data, open the file to clean and prepare it. Check for any inconsistencies, missing values, or errors that may have occurred during the export process. Ensure that the data types are consistent and that the file is formatted correctly to prevent issues when uploading to BigQuery.
Log into your Google Cloud Platform account and navigate to Google Cloud Storage. Create a new bucket or use an existing one to store the data files. The bucket acts as a staging area for your data before it is moved to BigQuery. Ensure that the bucket is in the same region as your BigQuery dataset for optimal performance.
Upload the cleaned data file from your local machine to the Google Cloud Storage bucket. You can do this through the GCS web interface by clicking "Upload files" and selecting your prepared file. Alternatively, use the `gsutil` command-line tool if you prefer scripting the upload process.
In your Google Cloud Platform console, navigate to BigQuery. Create a new dataset to store the imported data. Datasets in BigQuery are similar to databases and help organize your tables. When creating the dataset, specify the dataset ID and select the appropriate data location.
Use the BigQuery web UI to load the data from Google Cloud Storage into a new table. In BigQuery, navigate to your dataset, click "Create table," and select "Google Cloud Storage" as the source. Specify the GCS file path, configure the schema manually or use auto-detect, and adjust any additional settings like data format and partitioning.
Once the data has been loaded into BigQuery, run a few preliminary queries to ensure data integrity and confirm that the upload process was successful. Check for any discrepancies or errors in the data types and values. Correct any issues by re-uploading the data if necessary. Once verified, you can proceed with your data analysis using BigQuery's powerful SQL capabilities.
By following these steps, you can effectively transfer data from Kyriba to BigQuery 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:
