Palantir Prep Plan (1-2 Days)

🚀 DAY 1 – Core Concepts + Palantir Focus

⏰ 3–4 Hours Total

🔹 1. Palantir Foundry (🔥 MOST IMPORTANT)

What to Study:

What is Foundry?

Pipeline flow

Ontology basics

✅ Ready Answer:

Q: What is Palantir Foundry?

👉

Palantir Foundry is a data platform used to integrate, transform, and analyze data from multiple sources. It enables building data pipelines, creating datasets, and developing applications using ontology for business insights.

Q: Explain a data pipeline in Palantir

👉

A data pipeline in Palantir involves ingesting raw data from sources, transforming it using code repositories or pipelines, storing it as curated datasets, and then using ontology to build business-facing applications.

Q: What is Ontology?

👉

Ontology in Palantir defines relationships between datasets and business entities, enabling users to interact with data in a business-friendly way rather than raw tables.

🔹 2. Data Engineering Basics

Learn:

ETL vs ELT

Batch vs Real-time

✅ Answers:

Q: ETL vs ELT

👉

ETL transforms data before loading into the system, while ELT loads raw data first and then transforms it inside the system. ELT is more scalable in modern data platforms.

Q: Batch vs Real-time

👉

Batch processing handles data at scheduled intervals, while real-time processes data instantly as it arrives.

🔹 3. SQL Basics (VERY IMPORTANT)

Revise:

Joins

Aggregation

Group By

✅ Answers:

Q: Difference between INNER JOIN and LEFT JOIN

👉

INNER JOIN returns only matching records, while LEFT JOIN returns all records from the left table and matching ones from the right.

Q: Find duplicate records

👉

SQL

Copy code

SELECT column_name, COUNT(*)

FROM table_name

GROUP BY column_name

HAVING COUNT(*) > 1;

🔹 4. Your Experience Mapping (🔥 Game Changer)

Prepare THIS STORY:

👉

“In my current role, I work extensively with monitoring tools like Dynatrace, where I analyze application performance data, identify anomalies, and improve system reliability. I also created dashboards in ServiceNow to track SLA metrics and incident trends, which helped in proactive issue resolution and better decision-making.”

🚀 DAY 2 – Scenario + Behavioral + Storytelling

⏰ 3–4 Hours Total

🔹 1. Scenario-Based Questions

✅ Answer 1:

Q: Pipeline failure — what will you do?

👉

First, I will check logs to identify the failure point. Then I will validate input data and dependencies. If it's a transformation issue, I will debug the logic. After fixing, I will rerun the pipeline and implement monitoring or alerts to prevent recurrence.

✅ Answer 2:

Q: Data inconsistency issue

👉

I will validate source data, check transformation logic, and compare outputs. Then I will implement data quality checks such as validation rules and alerts to ensure consistency.

🔹 2. Behavioral Questions (VERY IMPORTANT)

✅ Tell me about yourself

👉

I have 5+ years of experience in production support, specializing in monitoring, incident management, and performance analysis using Dynatrace and ServiceNow. I’ve worked on building dashboards, analyzing data trends, and improving system reliability. Recently, I’ve started working with Palantir Foundry to understand data pipelines and ontology. I’m now looking to transition into a data engineering role where I can leverage my analytical skills and contribute to data-driven solutions.

✅ Why Data Engineering?

👉

In my current role, I already work with large volumes of system and application data. I realized that I enjoy analyzing and deriving insights from data, which motivated me to move into data engineering, where I can build scalable data pipelines and contribute more directly to business insights.

✅ Why PwC?

👉

PwC offers an opportunity to work on diverse data-driven projects with a strong focus on innovation and problem-solving. I’m particularly interested in working with Palantir and contributing to impactful data solutions in a consulting environment.

🔹 3. Strong Incident Story (🔥 MUST PREPARE)

👉 Use this:

“We encountered an HTTP 503 error in one of our applications. I started by checking server health and logs, but no issues were found. I then performed an IIS reset, which restored the service. Post-resolution, I analyzed patterns and ensured monitoring alerts were in place to detect similar issues earlier.”

🔹 4. Bonus (This Will Impress Interviewer)

Prepare 1 idea:

👉

“I’m working on automating batch job monitoring, which is currently manual. My goal is to build a data-driven pipeline to track job status and alert failures proactively.”

⚡ Final Cheat Sheet (Revise Before Interview)

✔ Palantir basics

✔ SQL queries

✔ 2 scenarios

✔ 1 strong incident story

✔ Tell me about yourself

✔ Why transition

🧠 Pro Tip (VERY IMPORTANT)

You don’t need to be perfect in Palantir.

You need to show: 👉 Learning mindset

👉 Real-world problem solving

👉 Data thinking