| Course | BU 455 Big Data Management |
|---|---|
| Unit | Unit 5 |
| Paper type | Distributed processing exercise |
| Length | About 870 words, 6 pages |
| Format | APA 7 student paper |
| School | Herzing University |
| Program | BS in Business Analytics |
| Updated | October 2026 |
Free sample paper for BU 455 Unit 5
Sixteen Partitions, One Straggler and a Laptop That Won: A Distributed Processing Exercise on a Composite Bike-Share Operator's Year of Telemetry
Student Name
Business, Herzing University
BU 455: Big Data Management
Instructor Name
March 11, 2026
Sixteen Partitions, One Straggler and a Laptop That Won: A Distributed Processing Exercise on a Composite Bike-Share Operator's Year of Telemetry
The Job and the Two Ways to Run It
The analytics lead at Pedalworks Mobility, the fictional city bike service at the center of this course, wants to know how each e-bike's battery capacity has declined, week by week, across 2025. The data are one year of telemetry, about 184 million messages, stored since the storage unit as monthly columnar files of about 4 gigabytes in total. This exercise runs the job two ways: split across a small distributed cluster, using the Apache Spark engine, and on a single workstation with an in-process analytical database. It reports what splitting the work cost, stage by stage, gives the one partition that finished long after the rest its own account, and compares the whole run with the single machine.
Distributed processing splits data into partitions, runs the same operation on each partition in parallel and then combines the results, the pattern made widely known by MapReduce (Dean & Ghemawat, 2008) and generalized by Spark (Zaharia et al., 2016). Its benefit is parallel work; its costs are starting the cluster, moving data between machines and waiting for the slowest partition.
The Distributed Run, Stage by Stage
The cluster had four worker machines with four processor cores each, and the job used 16 partitions, with telemetry divided among them by bike identifier. The job ran in four stages.
| Stage | What it did | Data moved between machines | Time |
|---|---|---|---|
| 0. Start | Request and start the cluster's workers | None | 55 seconds |
| 1. Read and filter | Read battery, bike and time columns; drop messages from bikes not in service | None; each partition read its own files | 48 seconds |
| 2. Group by bike and week | Average battery per bike per week; partial averages computed within each partition first, then combined | About 110 MB of partial results | 35 seconds for most partitions |
| 3. Attach bike details | Add model and purchase date from a small table sent to every worker | Under 1 MB | 4 seconds |
| 4. Write results | About 172,000 rows of weekly averages | None | 6 seconds |
| Waiting for the slowest partition | Fifteen partitions idle while one finished stage 2 | None | 2 minutes 13 seconds |
| Total | 4 minutes 41 seconds |
Stage 2 is where distribution costs the most in principle, because grouping by bike requires all of a bike's messages to reach the same machine. The job reduced that cost by computing partial averages within each partition before moving anything, so only about 110 megabytes crossed the network instead of several gigabytes of raw messages.
The Partition That Finished Late
Fifteen partitions finished stage 2 within 35 seconds. The sixteenth took nearly three minutes, and the whole job waited for it. Investigation showed why. One bike, number 2231, had a firmware fault from May to July that made it send a message every second instead of every 30 seconds, producing about 7.8 million messages, more than 4 percent of the year's telemetry from one of 3,300 bikes. Because messages were divided by bike, all of them landed in the same partition, which therefore held several times the work of any other.
This is data skew, and it is common: distributed jobs are only as fast as their largest partition. Two fixes were tried. Removing the duplicate messages, which carried no new information since the bike was parked, brought the partition into line and cut the total to 2 minutes 58 seconds. Spreading a single heavy bike across several partitions would also work but adds a second combining step. The duplicate removal was kept, and the faulty firmware was reported to the bike maker.
What Each Run Cost Beyond Time
Time is not the only cost. The cluster was rented by the minute from the company's cloud provider, and each run, including startup, cost about 90 cents; the workstation already sits on the analyst's desk and adds nothing. More significant is the cost in attention. Running the job on the cluster required writing it in Spark's programming interface, choosing a partitioning key, finding the straggler through the cluster's monitoring pages and understanding why one partition held so much work. The single-machine version was a single query of about fifteen lines that any analyst on the team could read. When the job has to change, for example to compare battery decline across bike models, the analyst can change the query in minutes, while the cluster version needs the data engineer. For a three-person analytics team, that difference in who can maintain the work matters as much as the run time. The exercise records both kinds of cost so the architecture decision later in the course can weigh them together.
The Single Machine
The same job on one workstation with 8 processor cores, reading the same columnar files with an in-process analytical database, took 2 minutes 12 seconds before the duplicate fix and 1 minute 39 seconds after it. It needed no startup, moved no data between machines and had no partitions to wait for. McSherry et al. (2015) proposed judging a distributed system by the configuration that outperforms a competent single-threaded implementation, and found that many published systems needed large clusters just to match one laptop. Pedalworks' result is the same lesson at small scale: with 4 gigabytes of columnar data, a single machine does the job faster and more cheaply than a cluster.
The distributed run was slower, and that is the finding. It does not mean distributed processing is useless for Pedalworks. The cluster's time grew slowly as data were added in a test with the year copied three times, while the workstation's time grew roughly in proportion. Extrapolating, the cluster would overtake the single machine at around ten times today's telemetry, which the five-second firmware would bring close within a few years. Until then, the job runs on the workstation, and the cluster configuration is saved so it can be used when the data warrant it. Pedalworks and all of its telemetry are invented for the course.
References
Dean, J., & Ghemawat, S. (2008). MapReduce: Simplified data processing on large clusters. Communications of the ACM, 51(1), 107-113. https://doi.org/10.1145/1327452.1327492
McSherry, F., Isard, M., & Murray, D. G. (2015). Scalability! But at what COST? In 15th Workshop on Hot Topics in Operating Systems (HotOS XV). USENIX Association.
Zaharia, M., Xin, R. S., Wendell, P., Das, T., Armbrust, M., Dave, A., Meng, X., Rosen, J., Venkataraman, S., Franklin, M. J., Ghodsi, A., Gonzalez, J., Shenker, S., & Stoica, I. (2016). Apache Spark: A unified engine for big data processing. Communications of the ACM, 59(11), 56-65. https://doi.org/10.1145/2934664
What the BU 455 Unit 5 instructions ask for
The fifth unit of BU 455 asks students to run or simulate a distributed processing job and analyze its costs. Common requirements include describing how data are partitioned, reporting each stage's time and data movement, identifying stragglers or skew, and comparing the distributed run with a single-machine alternative. Many instructors ask whether a real cluster is needed; local or small cloud clusters, or careful simulation, are usually acceptable. Students should report honestly if the distributed run is slower. Running jobs on an employer's cluster is discouraged. The submission typically includes a stage table and APA 7 discussion, and strong answers estimate when distribution would begin to pay off.
How the BU 455 Unit 5 example is put together
The paper starts with the analytical question, the data and the two ways the job was run, citing the origins of MapReduce and Spark. A stage table records startup, reading, grouping, attaching details and writing, with data moved and time for each, plus the wait for the slowest partition. A paragraph explains how partial aggregation reduced data movement. A section traces the straggler to one faulty bike and compares two fixes. A section reports the single-machine run and cites research on single-machine baselines. A section adds the costs beyond time, including who can maintain each version, and the close estimates the volume at which the cluster would win.
BU 455 Unit 5 rubric: what full points look like
Distributed processing rubrics generally reward a clear partitioning scheme, stage-by-stage accounting, data movement measured, straggler analysis and an honest single-machine comparison, together with writing and APA. Partitioning earns credit when the key and number of partitions are stated. Stage accounting scores highest when each stage's time is reported. Data movement rewards measuring what crossed the network. Straggler analysis is judged on whether skew is traced to a cause and fixed. The comparison criterion pays for running a competent single-machine version and reporting the result even when it wins. Instructors also credit students who estimate the scale at which distribution becomes worthwhile.
BU 455 Unit 5 help before you submit
Distributed processing exercises often lose points by reporting only total run time, hiding where the time went. Another common problem is ignoring startup and data movement costs. Students frequently miss skew, blaming a slow job on the cluster rather than on one oversized partition. Some compare a tuned cluster with a poorly written single-machine version. Others avoid reporting a slower distributed run, which is itself a valid finding. Many never consider who can maintain each version. A ninety-line script that one analyst understands and a cluster job that needs a specialist are different commitments even when they print the same answer, and the paper should say which one the team can keep running. Finally, running coursework on your company's cluster can interfere with production work and raise access questions; a small rented or local setup is the safer choice.
Get a BU 455 Unit 5 example written to your instructions
Paste the Unit 5 instructions and your BU 455 rubric into the thread, with whatever data or job description the assignment supplies and the environment your section requires. The custom example names the partition column first, prints the counts, charges movement to a stage, examines the straggler and reports the single-machine comparison. First custom sample free, back in 24 to 48 hours.
More BU 455 papers and business analytics samples
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BU 455 Unit 5 questions, answered
Do I need a real cluster to complete this?
Check the instructions, since sections differ and many run the exercise locally with several parts on one machine. A local run still produces uneven partitions, still moves records between them, and still generates the figures the criterion wants to see. What it cannot support is a claim about behavior at scale, so keep the conclusions to what your run demonstrated and name the environment that produced them.
What do I write if the distributed run is slower?
Report it and explain it. Splitting work carries a fixed price paid in coordination and in shifting records around, and below a certain size that price exceeds the work saved. Naming the point at which the balance would tip is the strongest form of this finding. Dropping the comparison because the answer was inconvenient abandons the argument the whole course is built on.
Could I run the job on the cluster at work?
No, for two separate reasons. The compute belongs to the employer and is usually metered against a budget somebody else answers for, and whatever the job reads is company data that would then sit inside your submitted figures. Use whatever environment the classroom provides. Tuning you do on a job at work is your own professional work, performed under your employer's rules, and never something drafted here.
Where can I find a free BU 455 Unit 5 sample paper?
The whole BU 455 Unit 5 distributed processing exercise is free above, with the stage table, the straggler analysis and margin notes. If your job or data differ, send the details and a custom exercise is written free, typically within two days.
What if the distributed run is slower in BU 455 Unit 5?
Report it and explain why. In the sample, startup, data movement and one skewed partition made the cluster slower than a workstation, and the paper estimates the data volume at which that would reverse.