Key Big Data Interview Questions and Practical Answers

Today we share interview questions that students from our Big Data Advanced class have encountered, focusing on several challenging topics.

(Long) tail task identification and handling?

The role of Paimon in projects, and the scale and common issues of online tasks?

How to deal with a severely underperforming server in a cluster?

Maximum data volume (including batch and streaming)?

Understanding large models and their underlying principles?

These questions reflect a shift in the data field from rote knowledge to practical experience and problem‑solving.

Questions about large models are open‑ended, but keeping up with AI trends is essential.

Effective use of large‑model tools can solve many problems if you know how to ask smart questions.

For introductory large‑model topics, see:

Data Agent: Typical Data + AI Application Scenarios

Key Technologies for Combining Big Data and Large Models

Key Technologies for Combining Big Data and Large Models

Below we briefly address each question.

(Long) tail task identification and handling?

Tail tasks are those without downstream consumers; they can be identified via lineage metadata in mature platforms.

Long‑tail tasks are those that run for a long time (e.g., ≥3 hours) or consume significant resources (e.g., ≥10 % of cluster resources) and can also be detected via metadata.

Tail tasks should be decommissioned, while long‑tail tasks need optimization.

Paimon’s role in projects and online task scale and common issues

Paimon is a core component of the lake framework, supporting unified batch‑stream processing; you should be familiar with its capabilities and the problems it solves.

Task scale should be realistic and reasonable.

Reference materials:

Paimon Performance Optimization Summary

Apache Paimon Interview Essentials – Basics

Apache Paimon Interview Essentials – Advanced Part 1

Apache Paimon Interview Essentials – Advanced Part 2

How to handle a severely underperforming server in a cluster?

First locate the bottleneck by checking CPU, memory, network, and I/O.

Set thresholds to mark a node as unavailable; many cluster managers use a blacklist mechanism to isolate such nodes.

Platforms like Kubernetes provide similar capabilities; for example, Flink on Kubernetes can be configured with blacklist detection intervals and failure‑rate thresholds.

When a node exceeds the failure‑rate or resource‑usage threshold, it is automatically added to the blacklist, and Flink continuously monitors node status without manual intervention, though administrators can intervene manually if needed.

Maximum data volume (batch and streaming)

As a guideline, aim for offline daily increments of at least terabyte scale and streaming peak rates of no less than 100 k requests per second, while remaining realistic about the challenges of large‑scale data.

For more resources, join our knowledge‑sharing community:

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