That's probably the gist indeed. I'm just a chemist from education, so I do see labels flying past like ODBC and I roughly know in which context it is spoken, but I have no visualization what it does or what it tries to solve. And from Power BI's perspective, what it can break :).
I also asked this question on other platforms, like ChatGPT. But from my side, not idea on ODBC or who manges it, Network Infrastructure, Security, etc, no idea who to go to. I just have no expectation on it. All I know, it is not me :).
Like:
Power BI -> ODBC -> API -> Server -> XML -> Network Infrastructure -> Security -> Gateway -> Power Query -> JSON -> Power BI data model -> DAX -> Visualization?
Or am I missing steps? Or did I misplace them?
ChatGPT's answer:
The process of transferring data from an on-premise database to an analytical tool on your computer typically involves several components and technologies. While the specific implementation may vary depending on the systems involved, here are some common elements and considerations:
- Database Connectivity: You need a mechanism to establish a connection between your computer and the on-premise database. This can be achieved through database-specific drivers, protocols, or APIs (Application Programming Interfaces). For example, if you're using a relational database like MySQL or Oracle, you might use ODBC (Open Database Connectivity) or JDBC (Java Database Connectivity) drivers to connect.
- Network Infrastructure: The data transfer relies on the underlying network infrastructure. IP addresses play a crucial role in routing data packets between your computer and the on-premise database. Firewalls, routers, switches, and other networking components ensure secure and reliable communication between the systems.
- Security Measures: To protect the data during transfer, various security measures come into play. This may include encryption techniques like SSL/TLS (Secure Sockets Layer/Transport Layer Security) to establish a secure connection, VPNs (Virtual Private Networks) for secure remote access, or authentication mechanisms such as username/password or API keys.
- Data Extraction: Once the connection is established, you need a method to extract data from the on-premise database. This can involve writing SQL queries or using specific database tools to fetch the required data. Some databases also provide APIs or export functionalities to retrieve data in a structured format like JSON or CSV.
- Data Transformation: Depending on the requirements of the analytical tool, you may need to transform the data into a compatible format. This could involve data cleaning, aggregation, filtering, or joining operations. Extract, Transform, Load (ETL) processes or data integration platforms are often used to perform these transformations.
- Data Transfer: Once the data is extracted and transformed, it needs to be transferred to your computer. This can be achieved using various methods such as HTTP(S) requests, file transfer protocols (FTP), or specialized data transfer protocols like WebSocket or AMQP (Advanced Message Queuing Protocol).
- Data Ingestion: On your computer, you would have an analytical tool or software that can consume the data. This tool could be a standalone application, a web-based platform, or a programming library. The data is ingested into the analytical tool for further analysis, visualization, or processing.
It's important to note that the specific implementation and technologies used may vary depending on the database, analytical tool, and network infrastructure in use. Organizations often have their own customized setups and configurations to ensure data flows securely and efficiently from an on-premise database to an analytical tool.
