How to load data from Visma Economic to Databricks Lakehouse
Learn how to use Airbyte to synchronize your Visma Economic data into Databricks Lakehouse within minutes.
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How to Sync to Manually
Step 1: Understand Visma e-conomic API
Begin by familiarizing yourself with the Visma e-conomic API. This step involves reviewing the API documentation to understand the endpoints available for extracting the required data. Pay attention to authentication methods and any rate limits imposed by the API.
Step 2: Set Up API Authentication
Set up authentication to access the Visma e-conomic API. Typically, this involves generating API keys or tokens from the Visma e-conomic developer portal. Ensure you have the necessary permissions to access the required data endpoints.
Step 3: Extract Data Using Python Scripts
Develop a Python script to extract data from Visma e-conomic. Use libraries such as `requests` to make API calls to the Visma e-conomic endpoints. Parse the JSON responses and store them in a structured format, like CSV or JSON files, on your local machine or an intermediary storage.
Step 4: Set Up Databricks Environment
Log into your Databricks account and set up a new Databricks workspace if not already done. Ensure that you have a cluster running that can process the data. Familiarize yourself with the Databricks CLI for command-line interactions if needed.
Step 5: Upload Data to Databricks File System (DBFS)
Use the Databricks CLI or the Databricks web interface to upload the extracted data files to the Databricks File System (DBFS). This can be done by using commands like `dbfs cp` to copy your local CSV or JSON files to a DBFS path.
Step 6: Load Data into Databricks Lakehouse Tables
Within a Databricks notebook, write scripts to load the data from DBFS into Databricks Lakehouse tables. Use Spark DataFrames to read the data from the uploaded files and then use the DataFrame API to write the data into Delta Lake tables, with commands like `write.format("delta").saveAsTable("table_name")`.
Step 7: Validate and Transform Data
After loading the data, perform data validation and transformation as needed. Use Databricks notebooks to run SQL queries or further process the data using PySpark, ensuring that the data conforms to your required schema and data quality standards before it's used in analytics or reporting.
By following these steps, you can effectively move data from Visma e-conomic to Databricks Lakehouse without relying on third-party connectors or integrations.