From the course: Build with AI: Data Pipelines with Cursor, Neon, and Streamlit
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Design table schema and create a table in the database
From the course: Build with AI: Data Pipelines with Cursor, Neon, and Streamlit
Design table schema and create a table in the database
- [Instructor] So let's see what our agent did there. I'm going to open the script. It wrote Database connection. It's using this library, which is a Python driver for PostgreSQL, and it should have been added to the requirements that txt, in fact, I see it here. The other library that was added is python-dotenv, which is used in order to read the secrets from this .env file that we added previously. So back to the script, we are importing load.env and the PostgreSQL driver. And then here we have a function that is loading the password from our .env file. So as you can see, my AI agent did not see the .env file. I just told it it's there. I did not give it the password to the database, but it wrote code that is going to read the password from my .env file. And then the code is going to use that password to connect to the database. And this is how we can magically connect to the database and provide the correct password…
Contents
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OpenAlex quick start: Analyze data for Python data pipelines6m 59s
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(Locked)
Data extraction layer: Get data from OpenAlex22m 40s
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(Locked)
Neon database setup: Cloud PostgreSQL for data pipeline projects9m 50s
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(Locked)
Design table schema and create a table in the database9m 50s
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(Locked)
Process and load your data12m 23s
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(Locked)
Data quality testing11m 21s
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(Locked)
Consolidate pipeline logic8m 38s
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(Locked)
Build a Streamlit dashboard7m 58s
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(Locked)
Deploy the Streamlit dashboard7m 28s
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