Feat ipynb validator

[dartanghan]

Added a feature to scan .ipynb (jupyter notebook) files, able to read SQL and PYTHON cells.
This will be very useful for data engineers that want their data products validated.

• Added .ipynb in the README.doc
• Added ipynb in pyproject.toml
• Created ipynb_plugin.py
• Added rules in plugins.yaml

Sample code:

%sql
SET password := "secret"
SELECT :password

%python

@dlt.table
def filtered_taxis():
    password="SQLSERVER"
    username="SQLUSER"
    print("username: ", username)
    print("password: ", password)

Sample result:

File: notebook.ipynb
 Identified issue 1: Hardcoded SQL password in notebook
    Snippet: %sql
SET password := "secret"
SELECT :password
    Line number: 62
    CWE: CWE-798
    Severity: High
...

File: dlt_pipeline.ipynb
 Identified issue 1: Hardcoded credentials in code
    Snippet: password="SQLSERVER"
    username="SQLUSER"
    Line number: 64
    CWE: CWE-798
    Severity: High
...

We can also detect leaked info in metadata

Snippet: 87:   "language_info": {
88:    "name": "python",
89:    "version": "3.11.4",
90:    "password": "hidden in metadata"
91:   }
    CWE: CWE-532
    Severity: Medium
    Why: A metadata field named `password` is present in the notebook metadata. Even though the value is masked, including a password field in metadata can lead to accidental disclosure if the 
actual secret is stored.
    Mitigation: Remove any metadata fields containing sensitive information. Use external secret stores and avoid embedding secrets or secret labels in notebook metadata.
    Confidence: 0.8

[dartanghan]

Hi @mpekatsoula @aaronjb !
Should I fix/adjust anything on the code or do you think that notebook scan aren't aligned with the tool's purpose?
I will be glad to fix or adapt this PR :)

Tnx

[mpekatsoula]

@dartanghan feel free to resolve conflicts/update the plugin and we're happy to merge

[mpekatsoula]

"+"

[mpekatsoula]

Does it make sense to reuse the python prompts here?