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AKTU · B.Tech · 6th semester

AKTU Stream Processing and Analytics (BCAM062) Question Paper 2025-26

UniversityDr. A.P.J. Abdul Kalam Technical University (AKTU), Lucknow
CourseB.Tech, Semester VI
SubjectStream Processing and Analytics (BCAM062)
ExamTheory Examination 2025-26 (even semester)
Time · Marks3 hours · 70 marks
SourceAKTU question paper, from the ABESIT question paper bank
View original PDF Other years

Note: Attempt all Sections. In case of any missing data; choose suitably.

Section A

Q1. Attempt all questions in brief · 2 × 7 = 14 marks

  1. Define Stream Processing and provide two real-world examples.
  2. Differentiate between Stateless and Stateful processing.
  3. What does latency represent in a stream processing engine?
  4. Briefly explain the concept of micro-batching in Apache Spark.
  5. Define event-time and how it differs from processing-time.
  6. List any two default sinks available in structured streaming.
  7. What is the purpose of a Spark Streaming Context (SSC) ?

Section B

Q2. Attempt any three of the following · 7 × 3 = 21 marks

  1. Discuss the concept of scaling up vs. scaling out in the context of distributed stream processing.
  2. Describe Spark’s memory usage and its impact on latency and throughput.
  3. Explain the Resilience model in Spark. How do RDDs guarantee fault tolerance?
  4. Discuss the structured streaming programming model and the "Unbounded Table" concept.
  5. Explain the Spark streaming execution model and the role of DStreams in materializing results.

Section C

Q3

Attempt any one part of the following · 7 × 1 = 7 marks

  1. Describe the steps to install and configure Apache Spark with JDK and Scala IDE.
  2. Explain the use of window aggregations and the effect of time on transformations

Q4

Attempt any one part of the following · 7 × 1 = 7 marks

  1. Explain the architectural models of a data platform. How does a batch- processing component complement a streaming application?
  2. Design a Spark streaming program to count words in text data received from a TCP socket.

Q5

Attempt any one part of the following · 7 × 1 = 7 marks

  1. What are Resilient Distributed Datasets (RDDs)? Explain how the lineage graph helps in distributed processing.
  2. Analyze the micro-batching processing model. How does it simplify fault tolerance compared to continuous processing?

Q6

Attempt any one part of the following · 7 × 1 = 7 marks

  1. Explain the structured streaming programming model. How does it treat a live data stream as a continuously appended table?
  2. Compare micro-batch processing with continuous processing modes in structured streaming. When would you prefer one over the other?

Q7

Attempt any one part of the following · 7 × 1 = 7 marks

  1. Explain the concept of time-based stream processing in DStreams. How do slide intervals and window intervals interact?
  2. Discuss performance tuning strategies for Spark streaming. How does adjusting the batch interval affect latency and throughput?

Other years

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