Knowing an answer and explaining why it works are two different skills. I created this package to help you practise both.
Hey, I’m Ajay Kadiyala. This bundle brings together my Data Engineering Interview Kit, hands-on SQL + PySpark labs, and one focused written response from me.
Here’s what you get:
📘 Complete Interview Kit — 25 Documents
Use my existing interview-preparation material to organise your revision, work through questions, and identify topics you need to revisit.
💻 SQL + PySpark Practice Lab
Work through five business scenarios in both Spark SQL and PySpark:
• Deduplicate order updates.
• Calculate customer revenue after refunds.
• Find consecutive login streaks.
• Process late-arriving sales and check safe reruns.
• Reconcile source and target tables.
The lab pack includes five practice notebooks, five solution notebooks, synthetic datasets, expected outputs, automated checks, explanations, follow-up questions, scoring rubrics, and a START_HERE guide.
Write your answer, run the checks, inspect the results, and compare your reasoning with the solution.
💬 One Personalised Written Answer from Ajay
Use the included Ask Ajay service for one focused question about your data engineering preparation, learning priorities, or interview approach.
Share your background, goal, and one clear question so I can give you a useful response.
This includes one written answer. Live calls, full resume reviews, complete code reviews, and ongoing mentoring are not included.
Who is this for?
Learners with basic SQL and Python knowledge who want structured interview preparation and practical coding exercises.
What do you need?
A laptop or desktop and either a compatible Databricks Python notebook environment or local Spark. The guide explains setup. The SQL exercises use Spark SQL.
How I suggest you use it:
Learn at your own pace. Focus on understanding one problem properly before moving to the next.
This is a self-study package with one written Q&A. It does not include certification, job placement, or guaranteed interview results.
— Ajay Kadiyala | DataGeeks