Spring 2027

Big Data Systems

Explores design and implementation of big data processing systems, covering cluster architecture, execution frameworks, and applications like batch analytics and stream processing.

offering recorded3 credits
Recorded instructors · Spring 2027 Meena Syamkumar

Summary

1 / 5

Past student reviews · Shivaram Venkataraman

Students read two 15-20 page papers each week, one for each lecture. The final project related to class areas and was considered not too bad.

Grade history

↗
average GPA
letter grades
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AB
B
BC
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F

All recorded terms · compare terms & instructors

Prerequisites

Course map

Graduate/professional standing

  • Graduate/professional standing
COMPSCI 744 used by

“Used by” includes alternatives; linked courses may have other requirements. This is a best-effort interpretation; check the catalog requirements above.

Prerequisite text tree
  • Graduate/professional standing

Professors

Spring 2027
Historical instructors & teaching patterns

SHIVARAM VENKATARAMAN is recorded teaching in Fall 2018, Fall 2019, Fall 2020, Fall 2021, Fall 2022, Spring 2024, Spring 2025. Recorded history may be incomplete and does not establish a future schedule.

Recorded history may be incomplete and does not establish a future schedule.

Calendar & sections

Spring 2027

Schedule loads here as you scroll.

SectionModeEnrolled / capacityWaitlist
LEC 002Classroom Instruction0 / 780
LEC 001Classroom Instruction0 / 780

Times are Central. Select a meeting for details; export includes recorded dates for the selected sections. Enrollment reflects scan time.

Meeting source records
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Student experience

the class

Past student reviews · Shivaram Venkataraman

The course featured well-rounded paper selections and amazing lectures. Exams consisted of open-ended design questions that students found really fun.

Recent recorded grades — Fall 2022: 3.96 GPA, 98.8% A/AB (n=85 letter grades); Spring 2024: 3.85 GPA, 90.0% A/AB (n=90 letter grades); Spring 2025: 3.80 GPA, 91.5% A/AB (n=71 letter grades).

difficulty & workload

Past student reviews · Shivaram Venkataraman

Students read two 15-20 page papers each week, one for each lecture. The final project related to class areas and was considered not too bad.

Past student reviews · Shivaram Venkataraman

Past students described Shivaram Venkataraman’s classes as interactive and engaging. They noted the course was heavy due to weekly paper reading, projects, assignments, and exams.

Topics

  • Cluster architecture and design goals such as flexibility, performance, and fault tolerance.
  • Popular execution frameworks and basic abstractions for data processing.
  • Applications including batch analytics, stream processing, graph processing, and machine learning.

Skills

  • Understanding design and implementation issues in big data processing systems, including cluster architecture, execution frameworks, and abstractions for batch, stream, and graph processing.

Grades

Latest available · Spring 2025— not enough history to project Spring 2027.

average GPA
A / AB grades
letter grades
Instructor

Grade distribution · % of letter grades

A
AB
B
BC
C
D
F

Grades over time

Through Spring 2025

More grade details Grade mix, volume & source data

Where this course fits relative to

Latest available grades · Spring 2025 · all course levels

GPA

Higher than % of other courses in this group.

Course GPAs · red marks this course’s range

letter grades

More recorded grades than % of other courses in this group.

Typical course in this group: letter grades.

About this comparison

1286 courses over the same term, each with at least 30 recorded letter grades. Cross-listed courses count once. GPA is not a measure of difficulty or teaching quality. The typical course is the median by recorded grade count; tied values are not counted as lower. Grade counts describe course scale, not unique students or typical section size.

Descriptions compare GPA with this group’s average: at least 0.20 higher or lower; otherwise close to average. Section size uses median recorded enrollment: small up to 30, mid-sized 31–99, large 100+. Lectures and discussion/lab sections are described separately.

Sources & history

UW–Madison

Catalog & offerings

Descriptions, prerequisites, and recorded course offerings.

Catalog observation history

Observations at scan time; dates do not imply when a catalog change took effect.

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Selected offering source records
COMPSCI 744 · Spring 2027

Big Data Systems

Recorded 2026-10-14
Raw records 
[
  {
    "run_id": "20261014T203208-0851c9a1",
    "semester": "1274",
    "observed_at": "2026-10-14 17:50:09.728220+00:00",
    "offering_id": "1274:266:024668",
    "course_id": "COMPSCI 744",
    "course_uid": "course_8c0399ef1c8361bc30a76c02",
    "term_id": "1274",
    "source_course_id": "024668",
    "source_subject_id": "266",
    "title": "Big Data Systems",
    "credits_min": 3,
    "credits_max": 3,
    "typically_offered": "Fall, Spring"
  }
]
Rate My Professors

Student reviews

Original comments behind the course and instructor summaries.

Read original reviews
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Madgrades

Grade history

Recorded grade distributions by term, section, and instructor.

Explore recorded grades
Dataset & model

Recorded 2026-10-14

View the dataset on Hugging Face
Model outputs & technical records
Qwen/Qwen3.8-27B
LLM outputs across runs
Download part 1
Full model traces

Recorded model configuration, reasoning, and tool conversations.

Download part 1
Model & dataset provenance 
{
  "model": "Qwen/Qwen3.8-27B",
  "model_revision": "1d4bf0f2ff6012fd82039f2fa52739d0dd7c60c0",
  "task_version": "17",
  "output_id": "a282ac02d8c633acabf3180332f3f69fca6905095c7877a96bb45dec58a43ebc",
  "requirements_status": "valid",
  "dataset_revision": "fc1811fa52e581ea57a35c0cf3343f8740a8ffbf",
  "observed_at": "2026-10-14 17:50:09.728220+00:00"
}