Emit order and fulfillment events.
JSON events All projects
PROJECT WALKTHROUGH / REAL-TIME PROCESSING
Real-Time Data Processing
Explore a streaming architecture for events, validation, state, and near-real-time analytical outputs.
ARCHITECTURE / LEARNING VIEW01Applications 02Event transport 03Checkpointed stream 04Validate and enrich 05Serving tables 06Monitoring
Buffer and partition events by stable key.
Streaming broker conceptConsume offsets and track state safely.
Structured Streaming conceptCheck schema, event time, and reference keys.
Python · SparkPublish recent event facts and aggregates.
Delta conceptObserve lag, throughput, and invalid events.
Metrics design DESIGN WALKTHROUGH6 LAYERS
PROJECT OVERVIEW
Understand the system before building it.
A conceptual streaming walkthrough that explains event ingestion, checkpointing, event-time handling, and downstream serving. It is an architecture exercise only; ARTECH does not deploy or run streaming infrastructure.
WHAT YOU WILL EXPLOREDistinguish event time and processing timeExplain checkpoints, late events, and replayDesign monitoring for lag and data quality
01
START WITH THE WHY
Business problem
Make the data need understandable before choosing tools or designing pipelines.
An online storefront wants a fresher view of order and fulfillment activity than a nightly batch can provide, while retaining a path to reconcile late or duplicated events.
Data engineering provides a repeatable way to move, validate, transform, and make data available with clear ownership and operational expectations.
The learning design outlines how events could be validated and made available with bounded freshness expectations. No real-time service, broker, or alert integration is implemented.
02
DEFINE THE BOUNDARIES
Requirements
Separate what the workflow must do from the reliability and operational qualities it needs.
- Accept order lifecycle events
- Publish aggregates with an explicit freshness target
- Support recovery and historical reconciliation
- 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.
Emit order and fulfillment events.
JSON eventsBuffer and partition events by stable key.
Streaming broker conceptConsume offsets and track state safely.
Structured Streaming conceptCheck schema, event time, and reference keys.
Python · SparkPublish recent event facts and aggregates.
Delta conceptObserve lag, throughput, and invalid events.
Metrics designConceptual 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 / 01Event streams
Storefront and fulfillment services
Example data
Order created, paid, packed, shipped, and delivered events
Frequency
Continuous event emission
Ingestion
Consume partitioned event records with checkpointed offsets
Potential issues
Duplicate deliveryOut-of-order eventsLate eventsSchema evolution
05–07
MOVE, SHAPE, STORE
Implementation flow
Build the pipeline in observable stages so each boundary can be tested and recovered independently.
Data ingestion
- Define event envelope, key, schema, and event-time field.
- Use checkpointing and bounded trigger intervals appropriate to the freshness need.
- Plan replay from retained source offsets or a durable landing log.
- Handle poison events in a quarantine path without stalling unrelated partitions.
SourcePipelineLandingRaw
Transformation
- Validate event schema and required identifiers.
- Deduplicate using stable event ID and an appropriate retention window.
- Order state transitions by event time and source sequence where available.
- Enrich events with reference data using explicit freshness expectations.
# 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
- Retain raw events with arrival metadata and source offset.
- Store curated event facts with event and processing timestamps.
- Use aggregations with well-defined window and late-event policy.
- Keep the serving layer's freshness contract visible to consumers.
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 event type, schema version, and required keys.
- Track duplicate, late, malformed, and unknown-reference counts.
- Reconcile streaming aggregates against a bounded batch recomputation.
- Monitor watermark behavior and dropped late data.
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
- Use checkpoint-aware restart behavior and test recovery paths.
- Separate transient dependency errors from invalid event content.
- Quarantine malformed events with enough context for replay.
- Define what happens when source retention is shorter than outage duration.
10 / Performance
- Balance partition count with key distribution and consumer parallelism.
- Avoid unnecessary state and wide shuffles.
- Measure processing rate against incoming rate and backlog growth.
- Tune micro-batch intervals against latency and efficiency goals.
11 / Security
- Authenticate producers and consumers with scoped identities.
- Encrypt transport and storage in a real deployment.
- Minimize sensitive payload fields and apply access controls to raw events.
- Keep credentials in a secret manager, never in code or notebooks.
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 input rate, processing rate, backlog, and end-to-end freshness.
- Surface failed batches, restart events, and invalid-event rate.
- Monitor state-store size and checkpoint health.
- All infrastructure and alerting here are design concepts only.
OBSERVABILITY DESIGNCONCEPT
Pipeline statusRun state
Job durationElapsed time
Record countsRead · written · rejected
FreshnessLast successful data time
Data qualityRule outcomes
Access auditWho · what · when
Real-Time Data Processing 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.
Events arrive out of order or after a window closes.
Possible approach
Choose an event-time and lateness policy, document late-data handling, and reconcile when needed.
A consumer restart replays events.
Possible approach
Use checkpointing and idempotent downstream writes keyed by stable event identity.
Incoming rate exceeds processing rate for a sustained period.
Possible approach
Inspect bottlenecks and partition skew, then scale or simplify only after measuring capacity and cost.
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 is a conceptual event-processing design for order lifecycle updates. Events carry a stable identifier, key, schema version, and event timestamp. A checkpointed consumer validates and deduplicates events, handles late arrivals under an explicit policy, and publishes curated event facts and windowed aggregates. I would monitor processing rate, backlog, freshness, invalid events, and checkpoint health. The design also needs a replay and batch reconciliation strategy. This walkthrough does not represent a deployed real-time system.”
PROJECT-SPECIFIC QUESTIONS
Practice the follow-up.
01
How do event time and processing time differ?
02
What is a checkpoint responsible for?
03
How would you handle duplicate and late events?
04
What metrics reveal a growing stream backlog?
05
How would you reconcile streaming results?
PROJECT CHECKLIST
Work through the build.
Tick items as you explore. This checklist is session-only and is not saved.
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