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Begin by thoroughly understanding the data structure and schema within Visma e-conomic. Identify the key tables and data points you need to export, such as invoices, customers, products, etc. This understanding will aid in effective data extraction.
Utilize Visma e-conomic's built-in export functionalities. Typically, this can be done through their API or by exporting data manually in CSV or Excel formats. Ensure that you export your data in a consistent and organized manner, maintaining relational integrity where necessary.
Once exported, prepare your data for transfer. Cleanse the data by removing any unnecessary fields, correcting errors, and formatting it to match the schema required by Firebolt. This might involve converting data types or restructuring data into a flat file format suitable for SQL ingestion.
Ensure that your Firebolt environment is set up and ready to receive data. This involves creating the necessary tables and schemas that mirror the structure of the data being imported. Use Firebolt's SQL Data Definition Language (DDL) to define tables, data types, and constraints.
Use Firebolt’s native data ingestion tools to upload your cleaned and prepared data. This can typically be done using SQL COPY commands to load data from your local filesystem or cloud storage into Firebolt tables. Ensure you follow Firebolt’s guidelines for data ingestion to optimize performance and accuracy.
After loading the data, run validation checks to ensure data integrity and accuracy. Use SQL queries to compare row counts, data types, and key metrics between Visma e-conomic and Firebolt. This ensures that no data has been lost or altered during the transfer process.
Finally, optimize your data within Firebolt for efficient querying and analysis. Create necessary indexes and partitions to improve query performance. Firebolt’s indexing and data optimization techniques can significantly enhance data retrieval times, making your analytical workflows more efficient.
By following these steps, you can manually transfer data from Visma e-conomic to Firebolt without relying on third-party connectors, ensuring complete control and customization throughout the process.
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.
Visma e-conomic having other systems like e-commerce, payment service providers, point of sale, marketplaces, logistic and accounting systems. It generally offers businesses with a range of software solutions, including an online accounting program. After all, Visma e-conomic is the market leader in cloud-based financial systems in Denmark and has over 160,000 customers. Visma e-conomic is one kinds of e-commerce market place that is aimed at both small and medium-sized businesses and accountants and bookkeepers.
Visma E-conomic's API provides access to a wide range of data related to accounting and financial management. The following are the categories of data that can be accessed through the API:
1. Customers and Suppliers: Information about customers and suppliers, including contact details, payment terms, and credit limits.
2. Invoices: Details of invoices issued and received, including invoice numbers, dates, amounts, and payment status.
3. Products and Services: Information about products and services offered by the business, including prices, descriptions, and stock levels.
4. Bank Transactions: Details of bank transactions, including deposits, withdrawals, and transfers.
5. Accounting Journals: Information about accounting journals, including general ledger entries, accounts payable, and accounts receivable.
6. VAT: Details of VAT transactions, including VAT rates, amounts, and tax codes.
7. Reports: Access to a range of financial reports, including balance sheets, income statements, and cash flow statements.
Overall, the Visma E-conomic API provides comprehensive access to financial data, enabling businesses to streamline their accounting processes and gain valuable insights into their financial performance.
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:
