The Big Data Show
A technical podcast offering mock data engineering interviews and tutorials for aspiring professionals in India.
This is a practice ground for data engineering job interviews. The show simulates real technical screenings, complete with live coding in shared documents, system design challenges on a virtual notepad, and deep dives into core concepts like Spark, AWS, and data warehousing. It's less of a talk show and more of a hands-on, public study session for technical candidates.
“Its strict focus on the mock interview format is unique. Instead of discussing careers, the show *is* the career hurdle, providing a public, watchable version of the technical screening process that candidates will actually face, right down to the screen-sharing and live problem-solving.”
Who hosts this show
The Big Data Show is a YouTube channel run by Indian data engineers, primarily featuring hosts Manoj Kumar and Nisha. The show's main purpose is to empower the aspiring data engineering community by conducting and publishing detailed mock technical interviews. These sessions simulate real-world job screenings, covering everything from fundamental concepts and SQL challenges to complex system design problems, providing a public practice ground for viewers preparing for their own interviews.
Credentials & credits
- Senior Data Engineer
- Staff Data Engineer
What kind of podcast
- Country
- India
- Region
- india
When new episodes drop
- 01First round of Data Engineering Interview at product based companySep 22, 2024 · 59 min
- 02Data Engineering InterviewSep 20, 2024 · 35 min
- 03Data Engineering InterviewSep 17, 2024 · 30 min
- 04Big Data Mock InterviewSep 7, 2024 · 40 min
- 05Big Data Interview - Round 1Aug 14, 2024 · 46 min
- 06Fundamental Of Orchestration - Episode 01Aug 10, 2024 · 38 min
- 07Interview Question on Cache v/s Persist - Part 1Jul 11, 2024 · 14 min
- 08IPL Final 2024 Data Analysis: Building the Ultimate Scorecard with PysparkJul 6, 2024 · 44 min
Notable episodes
- 01Data Engineering Interview (with Xian Dong)
A representative example of the show's format, progressing from experience questions to a live system design problem on a shared notepad.
- 02Data Engineering Interview (with Sanyam)
This episode showcases the hands-on coding aspect, starting immediately with complex SQL and PySpark challenges before moving to concepts.
- 03IPL Final 2024 Data Analysis: Building the Ultimate Scorecard with Pyspark
An example of the non-interview format, this episode is a project-based tutorial that walks through a practical data analysis task from scratch.
- 04Fundamental Of Orchestration - Episode 01
This episode is a tutorial that breaks down a core data engineering concept (orchestration), showing the channel's educational breadth beyond just interviews.
What you'll be asked on this show
The host emulates a real technical interviewer, often starting with a candidate's introduction before moving to hands-on coding problems (SQL/PySpark) or conceptual questions about data architecture. They use a shared document for live problem-solving and frequently probe with follow-ups like "Why did you choose this approach?" or "What's the difference between X and Y?". The interview often concludes with a larger system design question, asking the candidate to architect a data pipeline from scratch on a shared screen.
The show primarily features one-on-one mock technical interviews where hosts like Manoj or Nisha act as the interviewer. These sessions often use a shared screen for live coding and system design. Some episodes deviate from this format to become solo-host tutorials on specific data engineering topics (like Apache Spark's `cache` vs. `persist`) or project walkthroughs.
Questions the host keeps coming back to
12 cataloguedIf you're going on this show as a guest, expect some version of each of these. Each note explains when the host reaches for it.
origin
1- Q.01
“Can you please introduce yourself and explain your experience?”
This is the standard opening to understand the candidate's background and set the stage for more technical questions.
process
5- Q.01
“Can you explain your high-level data architecture?”
This question assesses the candidate's ability to articulate end-to-end data flows and system components.
- Q.02
“What are some Spark optimizations you've implemented?”
The host asks this to move beyond theory and into practical, performance-oriented experience.
- Q.03
“How would you implement a re-triggerable pipeline that avoids duplicates?”
A scenario-based question to evaluate the candidate's grasp of idempotency and error handling.
- Q.04
“Design a pipeline to ingest data from [source] to [target].”
This is often the final, capstone question, testing system design and architectural skills in a live setting.
- Q.05
“How would you optimize a slow-running SQL query?”
A practical problem-solving question to understand the candidate's debugging and performance tuning methodology.
problem-solving
1- Q.01
“Let's solve a SQL/PySpark problem on a shared screen.”
The host presents a hands-on coding challenge to test practical, real-world skills early in the interview.
craft
2- Q.01
“What's the difference between a data lake and a data warehouse?”
A fundamental conceptual question to gauge the candidate's understanding of core data architecture principles.
- Q.02
“What are the pros and cons of using a tool like Airflow?”
This assesses the candidate's awareness of the trade-offs involved in choosing specific orchestration tools.
technical fundamentals
1- Q.01
“What is the CAP Theorem?”
This question tests theoretical knowledge of distributed systems, a key area for big data roles.
technique
2- Q.01
“Why did you choose to use 'persist' instead of 'cache'?”
A follow-up question designed to probe for a deeper, more nuanced understanding of a specific technology (Spark).
- Q.02
“What's the difference between external and internal stages in Snowflake?”
This question tests for detailed, platform-specific knowledge, in this case for the Snowflake data warehouse.
Signature segments
- · Mock Technical Interviews
- · Live SQL/PySpark Coding
- · System Design Problems
- · Conceptual Deep Dives
- · Shared Screen Problem-Solving
Topics covered repeatedly
Who gets booked here
Guests are data engineering candidates, typically with a few years of experience, who volunteer to go through a public mock technical interview to test their skills and receive feedback.
- Rachanaon First round of Data Engineering Interview at product based company
- Sanyamon Data Engineering Interview
- Xian Dongon Data Engineering Interview
- Pragyaon Big Data Mock Interview
Where to find this show
Audience & reach
The show has featured sponsors like Astronomer, a company in the data orchestration space. The highly specific audience of practicing and aspiring data engineers makes it a targeted channel for companies with business-to-developer (B2D) products and services.
Subscriber and view counts are pulled live from YouTube and re-verified on a 30-day cycle. Listener estimates for the RSS feed aren't published here unless they're host-verified.
Pitch this show
People also ask
- Who are the hosts of The Big Data Show?
- The primary hosts who conduct the mock interviews are experienced data engineers, including Manoj Kumar and Nisha.
- What is the main format of the show?
- The main format is a mock technical interview for a data engineering role. A host interviews a guest candidate, asking technical questions, posing coding challenges, and presenting system design problems.
- Is the show still active?
- Based on recent episode dates from late 2024, the show appears to be dormant or has ceased regular production.
- Who is this podcast for?
- It's for aspiring or current data engineers who are preparing for technical job interviews and want to see real-world examples of questions and solutions.
- How can I participate as a guest (interviewee)?
- Past episode descriptions included a Topmate link for booking mock interviews, though the channel's current activity status is unclear.
- Where can I watch The Big Data Show?
- The show is available on its YouTube channel of the same name.
Built from the show's public RSS feed, YouTube, the host's own websites, and the cited sources below. Computed and AI-extracted fields are labelled. Facts only — no private info, no fabrication, no transcripts republished.
Sources & how this page was built
This page is AI-assisted, grounded in the public sources cited below, and host-verifiable. We publish facts only; we do not republish transcripts. If anything here is wrong, the host can claim and correct the page above.Model: gemini-2.5-pro · high confidence
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