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Start by logging into your Visma e-conomic account. Navigate to the data or reports section, and use the built-in export feature to download your data. Typically, you can export data in formats like CSV or Excel, which are commonly supported.
Once the data is exported, open the CSV or Excel file to ensure it is structured correctly. Remove any unnecessary columns or rows and correct any data inconsistencies. This is crucial for ensuring the data can be imported smoothly into DuckDB.
If you haven't already, download and install DuckDB from its official website (https://duckdb.org/). DuckDB is available for various operating systems, so choose the version that suits your system. Follow the installation instructions provided on the website.
Open a terminal or command prompt, and launch DuckDB by typing `duckdb` followed by the name you wish to give to your database (e.g., `duckdb mydatabase.db`). This command will create a new database file or open an existing one.
In the DuckDB shell, create a table that matches the structure of your exported data. Use the `CREATE TABLE` SQL command. For example:
```sql
CREATE TABLE my_table (
column1_name TYPE,
column2_name TYPE,
...
);
```
Replace `column1_name`, `TYPE`, etc., with the actual column names and data types from your exported data.
Use the `COPY` command to import your CSV or Excel data into the newly created table in DuckDB. For a CSV file, the command looks like this:
```sql
COPY my_table FROM 'path/to/your/exported_file.csv' (FORMAT CSV, HEADER TRUE);
```
This command tells DuckDB to read the CSV file and import it into `my_table`. Ensure the file path is correct and accessible.
After importing, verify the data integrity by running SQL queries in DuckDB to check for any discrepancies or errors. For example, run:
```sql
SELECT COUNT(*) FROM my_table;
```
Compare this count with the original data count to ensure all records were imported successfully. Also, inspect a few records to ensure data accuracy.
By following these steps, you can efficiently move your data from Visma e-conomic to DuckDB without relying on any 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.
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: