Sales and inventory extracts arrive by business date.
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PROJECT WALKTHROUGH / BATCH PROCESSING
Batch Data Processing Platform
Process daily datasets with distributed transformations, validation, partitioning, and storage-aware design.
ARCHITECTURE / LEARNING VIEW01Daily files 02Batch orchestrator 03Raw files 04Distributed transform 05Curated outputs 06Reporting
Validate readiness and pass processing date.
Job schedulerPreserve source files and batch metadata.
Cloud storageClean, join, aggregate, and validate at scale.
PySparkWrite analytics-ready partitions and quality results.
Delta / ParquetExpose validated data to analytical consumers.
SQL · BI DESIGN WALKTHROUGH6 LAYERS
PROJECT OVERVIEW
Understand the system before building it.
A learning architecture for repeatable batch processing of larger files using Spark concepts. It emphasizes bounded inputs, data quality, partitioning decisions, and safe retries rather than a live cluster deployment.
WHAT YOU WILL EXPLOREStructure a daily batch pipelineReason about partitioning and shufflesMake outputs re-runnable and quality-checked
01
START WITH THE WHY
Business problem
Make the data need understandable before choosing tools or designing pipelines.
A retail analyst receives daily sales and inventory files that are too large for manual spreadsheets and need consistent validation before reporting.
Data engineering provides a repeatable way to move, validate, transform, and make data available with clear ownership and operational expectations.
The design produces partition-aware, validated output for downstream analytics and makes reprocessing dates manageable.
02
DEFINE THE BOUNDARIES
Requirements
Separate what the workflow must do from the reliability and operational qualities it needs.
- Process one or more daily source files
- Publish standardized batch outputs by date
- Support rerunning a bounded date range
- Use configuration rather than hard-coded environment-specific values.
- Add validation, structured run logging, and clear failure boundaries.
- Protect credentials and grant only the access each workload needs.
- Measure freshness, duration, volume, and quality outcomes.
03
FOLLOW THE DATA
Architecture
A layered view of how data moves from source systems to a useful consumer-facing output.
Sales and inventory extracts arrive by business date.
CSV · JSONValidate readiness and pass processing date.
Job schedulerPreserve source files and batch metadata.
Cloud storageClean, join, aggregate, and validate at scale.
PySparkWrite analytics-ready partitions and quality results.
Delta / ParquetExpose validated data to analytical consumers.
SQL · BIConceptual learning architectureSpecific services depend on requirements, constraints, and deployment choices.
04
KNOW WHAT ENTERS THE SYSTEM
Source systems
Map each source to its data shape, arrival pattern, ingestion choice, and likely failure modes.
SOURCE / 01Partitioned CSV / JSON files
Daily sales export
Example data
Store, SKU, units, price, and transaction time
Frequency
Daily batch
Ingestion
Read only the requested business-date partition after readiness validation
Potential issues
Late file arrivalSchema driftSmall filesDuplicate records
05–07
MOVE, SHAPE, STORE
Implementation flow
Build the pipeline in observable stages so each boundary can be tested and recovered independently.
Data ingestion
- Confirm input files exist and match expected naming and date.
- Record file paths, sizes, and arrival timestamps.
- Read using an explicit schema where practical.
- Parameterize the date range to make backfills controlled.
SourcePipelineLandingRaw
Transformation
- Normalize data types and timestamps.
- Remove or quarantine duplicate business events using a defined key.
- Join reference data and derive business measures.
- Aggregate only after confirming the intended grain.
# Illustrative transformation outline
valid = [row for row in records if is_valid(row)]
rejected = [row for row in records if not is_valid(row)]
write_curated(valid)
write_quarantine(rejected, run_id=run_id)Storage / warehouse
- Keep original files in a raw layer for traceability.
- Write curated outputs to an appropriate columnar format.
- Partition by query-relevant date only when measurements support it.
- Keep invalid records in a separate quarantine output.
08
TRUST THE OUTPUT
Data quality
Make quality expectations explicit and decide what happens when a record or batch does not pass.
Incoming records
ValidationSchema · rules · keys
Valid recordsCurated target
!Invalid recordsReason · run ID · quarantine
- Validate schema and required fields before expensive processing.
- Check duplicate transaction keys and numeric ranges.
- Reconcile input, valid, rejected, and output row counts.
- Track partition freshness and empty-batch behavior.
09–12
RUN IT RESPONSIBLY
Operations, performance, security & monitoring
A realistic project also explains how the system behaves when inputs change, work slows down, or something fails.
09 / Error handling
- Retry transient reads with bounded policy.
- Write to a temporary output then publish only after validation.
- Make output replacement or merge behavior idempotent for a date.
- Keep failed date and source metadata for targeted replay.
10 / Performance
- Avoid unnecessary shuffles by filtering and projecting early.
- Choose partition counts based on data volume and output size.
- Consider broadcast joins only when one side is suitably small.
- Inspect Spark execution plans and avoid collecting large data to the driver.
11 / Security
- Limit access to raw files and curated outputs by role.
- Remove or mask unnecessary personal attributes.
- Use secret management for source access in real deployments.
- Separate workspace and storage permissions by environment.
12
Monitoring and observability
Monitor system health and data health together: pipeline status alone does not tell you whether the delivered data is fresh and complete.
- Track batch duration, input/output size, and rejected rows.
- Monitor failed task counts, retries, and data freshness.
- Observe partition sizes and small-file accumulation.
- The monitoring panel is a learning concept, not a live cluster.
OBSERVABILITY DESIGNCONCEPT
Pipeline statusRun state
Job durationElapsed time
Record countsRead · written · rejected
FreshnessLast successful data time
Data qualityRule outcomes
Access auditWho · what · when
Batch Data Processing Platform monitoring signals shown as a design concept. No live pipeline or alert integration is connected.
13
PROMOTE WITH CONTROL
Deployment flow
Treat infrastructure, SQL, configuration, and validation as reviewed changes that move through separate environments.
01DevelopmentBuild and iterate
02TestingRun checks
03StagingValidate release
04ProductionOperate and observe
Git branches and pull requestsCI checks and data testsEnvironment-specific configurationDeployment validation and rollback plan
Deployment workflow is a learning design. No CI/CD pipeline is implemented by this walkthrough.
14
THINK THROUGH TRADE-OFFS
Challenges and solutions
Strong project explanations show the problem-solving process, not only the happy path.
A daily file arrives later than expected.
Possible approach
Gate processing on a readiness check, then retry within a bounded window and alert on missed freshness.
One join key is highly skewed.
Possible approach
Measure skew and evaluate key distribution, salting, or alternative join strategies where justified.
The output directory contains many tiny files.
Possible approach
Review partition count and output strategy; compact when appropriate based on downstream access.
15
MAKE THE WORK CLEAR
How to explain this project in an interview
Use this outline to structure a truthful explanation. Adapt it to work you personally completed; this sample is not a claim about your experience.
EXPLANATION STRUCTURE
01Business problem
02Your role and scope
03Architecture and data flow
04Technology choices
05Challenges and solutions
06Quality, security, and performance
07Deployment and outcome
EXAMPLE INTERVIEW EXPLANATIONExample interview explanation: “This batch design processes daily sales and inventory extracts by business date. It validates file readiness and schema, applies transformations in PySpark, checks duplicates and row counts, then writes curated date-based output only after validation. I would tune partitions and joins based on observed data distribution and Spark plans rather than guessing. A bounded replay path and temporary publish step help make reruns safe. Monitoring would cover duration, freshness, failed tasks, and rejected records.”
PROJECT-SPECIFIC QUESTIONS
Practice the follow-up.
01
What makes a Spark transformation lazy?
02
How do you diagnose a shuffle or skew issue?
03
How do you make a daily batch safely rerunnable?
04
When might you broadcast a join side?
PROJECT CHECKLIST
Work through the build.
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