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Begin by thoroughly understanding the data structure in Visma e-conomic. Identify the specific data entities you need to export, such as invoices, customers, or transactions. Determine the format and schema of this data to prepare for the export process.
Use the Visma e-conomic API to extract data directly. You will need to write a custom script, preferably in a language like Python, to interact with the API. Authenticate using your Visma e-conomic credentials, and make API calls to fetch the required data. Ensure to follow API rate limits and best practices for data retrieval.
Once the data is extracted, transform it into CSV format, which is compatible with ClickHouse. During this transformation, ensure that data types and formats are consistent with ClickHouse requirements. Pay attention to date formats, numerical precision, and any necessary data cleaning.
Before importing the data, set up a ClickHouse database and create the necessary tables that match the schema of your CSV files. Use SQL commands to define the table structure, paying careful attention to data types and any indexing strategies for optimal performance.
Transfer your CSV files to the ClickHouse server. This can be done using secure file transfer protocols like SCP or SFTP. Ensure that the files are placed in a directory accessible by the ClickHouse instance for the import process.
Use ClickHouse's native command-line client to load the CSV files into the database. Execute SQL commands to perform the data import, specifying the appropriate table and handling any potential errors or data conversion issues. Use the `INSERT INTO ... FROM INFILE` syntax or the `clickhouse-client` tool for efficient bulk loading.
After the data import is complete, validate the data in ClickHouse to ensure it matches the original data in Visma e-conomic. Run queries to check row counts, data consistency, and integrity. Make any necessary adjustments to the imported data to correct discrepancies.
By following these steps, you can effectively move data from Visma e-conomic to ClickHouse 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.
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?
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