
Explain how a full stack compute platform translates notebook logic into backend tasks and workflows, then renders results on the UI while incorporating DevOps, big data, and pipelines.
Configure per-repo local virtual environments and Windows environment variables for python, spark, hadoop, and java while executing full spectrum tests, including unit and integration tests, Jenkins builds, and UI runs.
Reading notes from wikipedia, emails, shared drive, videos carefully is the most imp job other runs at alpha beta gamma farmworks, older un on our own framework, this 90% of the job?
Input csv for the bath to get diff port and market projections? Yes
Running multiple runs using csv? Yes
Batch runs for input to start with? Yes
Holding batch run and also Unemployment batch runs
Views vs table creation - temp vs permanent table creation has to be checked as the order of how we run the batch and any manual run might be custom? Yes
At the end do the runs
Code for batch running
Make tests pass locally by setting up the correct virtual environment with the prescribed make/build/setup.py steps, clearing cache, and validating non-pyspark tests before spark tests.
Rename task workflows on the front end, adjust unit test and axis names, then run workflow registration integrity tests, the Jenkins build, and front-end checks of input, execution, and output.
Handle date time columns across Pandas and Spark by normalizing strings, dash and slash formats, and schemas, then reliably join data frames with Spark and use notebooks for schema checks.
Master the master simulation run by selecting default options to ensure minimal input and configuration of market and unemployment files. Keep time steps minimized for a quick, working simulation.
Run the UI frontend to verify access, ensure upload and execution steps align on version and file name, and anticipate failures from schemas, typos, and data references.
After any logic change, update the unit test by regenerating the expected output from CSV data. Verify the virtual environment and run non-spark tests before schema changes.
This video reviews all 18 tickets, showing renaming workflows and tasks, updating unit tests and integrity checks, and validating the UI with PySpark, Hive, and Oracle workflows.
Review checkpoints for pip install and virtual environment setup in the Python full stack framework. Identify two workflow types (Python local and Spark large) and timeout and data-related UI issues.
All Slides - Master deck
Interview Prep Quant Finance Python Full Stack Computational Framework for Begineers 101
This courses might help you get a job and is very specficly tuned to job requiremnets in Computational Framework.
Solving Tickets for Real life Job Like scenarios - Get ready right now. Very practical course required to get a job in this market. This course help you develop a minset to deliver the good and not to just code.
Learn how to deal with tickets, code changes, python computational framework, get job done.
This course will help you solve tickets and perform in job related to full stack python computational simulation engine. Also will learn how to do testing and software build.
Includes the tests and quiz of real life cases which will help you cater to jobs.
Learn how to use testing and also deal with note makign and reporting to clients in a practical way.
This is not an purely academic course and is very simiular to internal training courses.
Each section comes with a test and helps you gauge and also impress the clients.
Terms and areas touched are Jenkins build, Unit testing, Pytesting, Venv, Spark dataframes, hadoop, schemas, data manipulation, end to end code release framework.
Learn how to change the code from scracth to release in production enviroments.