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PROJECT WALKTHROUGH / ETL / ELT

Enterprise Data Pipeline

Ingest multiple operational sources, validate and transform records, and publish curated Snowflake data for analytics.

AdvancedETL / ELTEnterpriseMulti-stage learning walkthrough
AzureADFSnowflakeSQLPython
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ARCHITECTURE / LEARNING VIEW
01Source systems

Operational tables, scheduled files, and an illustrative API feed.

SQL · CSV · REST
02Orchestration

Coordinate dependencies, parameters, and bounded retries.

Azure Data Factory
03Landing / raw

Keep source-aligned extracts with arrival metadata.

Cloud storage
04Transform

Standardize, deduplicate, and apply documented rules.

Python · SQL
05Curated warehouse

Publish validated facts and dimensions for analytics.

Snowflake
06Analytics

Expose governed views and business-ready datasets.

SQL · BI
DESIGN WALKTHROUGH6 LAYERS
PROJECT OVERVIEW

Understand the system before building it.

A learning walkthrough for a configurable batch pipeline that brings operational extracts into a cloud warehouse. It focuses on reliability and clear ownership at each layer rather than a particular vendor deployment.

WHAT YOU WILL EXPLORESeparate ingestion, transformation, and serving concernsDesign incremental loads with repeatable checkpointsExplain quality gates, failure handling, and observability
PROJECT LIFECYCLE / 15 STEPS01Business Problem02Requirements03Architecture04Source Systems05Data Ingestion06Transformation07Storage / Warehouse08Data Quality09Error Handling10Performance11Security12Monitoring13Deployment14Challenges & Solutions15Interview Explanation
01
START WITH THE WHY

Business problem

Make the data need understandable before choosing tools or designing pipelines.

THE CHALLENGE

Customer, account, and transaction information arrives from separate operational systems. Analysts need a consistent view, but extracts differ in structure, arrive on different schedules, and can contain duplicate or incomplete records.

WHY DATA ENGINEERING

Data engineering provides a repeatable way to move, validate, transform, and make data available with clear ownership and operational expectations.

EXPECTED OUTCOME

The design produces traceable, validated datasets that analysts can use for consistent reporting. The expected outcome is a more dependable data workflow, not a guaranteed business metric or employment result.

02
DEFINE THE BOUNDARIES

Requirements

Separate what the workflow must do from the reliability and operational qualities it needs.

FUNCTIONAL / WHAT IT DOES
  • Ingest the agreed source data and preserve traceability to its origin.
  • Apply documented business transformations and publish consumable outputs.
  • Support a repeatable load schedule and a safe way to reprocess a bounded period.
  • Make rejected or incomplete records available for investigation.
TECHNICAL / HOW IT OPERATES
  • 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.

01Source systems

Operational tables, scheduled files, and an illustrative API feed.

SQL · CSV · REST
02Orchestration

Coordinate dependencies, parameters, and bounded retries.

Azure Data Factory
03Landing / raw

Keep source-aligned extracts with arrival metadata.

Cloud storage
04Transform

Standardize, deduplicate, and apply documented rules.

Python · SQL
05Curated warehouse

Publish validated facts and dimensions for analytics.

Snowflake
06Analytics

Expose governed views and business-ready datasets.

SQL · BI
Conceptual 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 / 01Relational tables

Operational SQL database

Example data

Customers, accounts, transactions

Frequency

Daily extract or incremental watermark

Ingestion

Parameterized query or database connector into a landing area

Potential issues
Schema changesLong-running extractsConcurrent updates during extraction
SOURCE / 02CSV / JSON files

Partner file drop

Example data

Reference data and periodic adjustments

Frequency

Scheduled daily delivery

Ingestion

Check file presence, validate naming, then load from a staged location

Potential issues
Late or missing filesMalformed rowsDuplicate delivery
SOURCE / 03REST API

Service endpoint

Example data

Status and enrichment attributes

Frequency

Bounded scheduled requests

Ingestion

Paginated client with rate-limit-aware retries

Potential issues
Rate limitsPartial pagesExpired credentials
05–07
MOVE, SHAPE, STORE

Implementation flow

Build the pipeline in observable stages so each boundary can be tested and recovered independently.

05

Data ingestion

  • Land immutable source extracts with run ID and arrival timestamp.
  • Use a watermark or source change field for incremental reads where reliable.
  • Parameterize environment, source, and processing date.
  • Retry transient failures with limits; preserve failed-run context for replay.
SourcePipelineLandingRaw
06

Transformation

  • Normalize types, names, and timestamp conventions.
  • Deduplicate on an agreed business key and deterministic update order.
  • Join reference data and derive documented business attributes.
  • Separate technical metadata from business columns.
# 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)
07

Storage / warehouse

  • Raw layer retains source-aligned records and ingestion metadata.
  • Staging tables provide typed, validated working structures.
  • Curated facts and dimensions support common analytical questions.
  • Views or data marts present stable, documented consumption contracts.
RawStagingCuratedMarts / Views
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
  • Schema and required-column validation before processing.
  • Null, type, duplicate-key, and referential-integrity checks.
  • Row-count and source-to-target reconciliation by run or date.
  • Freshness threshold and invalid-record quarantine with reason codes.
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 source and network errors with bounded backoff.
  • Record run IDs, step, source, row counts, and error details in an audit log.
  • Quarantine invalid records instead of silently discarding them.
  • Make a run reprocessable by date or source without duplicate side effects.

10 / Performance

  • Read only needed columns and bounded date ranges.
  • Use incremental processing to avoid unnecessary full reloads.
  • Size warehouse compute to measured concurrency and workload needs.
  • Review query profiles and file layout before tuning.

11 / Security

  • Use managed identities or a secret manager in a real deployment.
  • Separate development, staging, and production roles.
  • Grant least privilege to ingestion and transformation identities.
  • Mask or limit access to sensitive customer attributes.
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 pipeline status, duration, freshness, and row counts per source.
  • Surface quality failures, retries, and rejected-record counts.
  • Define alert thresholds and an owner for each critical dependency.
  • This is a monitoring design concept; ARTECH does not run this pipeline.
OBSERVABILITY DESIGNCONCEPT
Pipeline statusRun state
Job durationElapsed time
Record countsRead · written · rejected
FreshnessLast successful data time
Data qualityRule outcomes
Access auditWho · what · when

Enterprise Data Pipeline 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.

CHALLENGE / 01

A partner file is delivered twice with the same business records.

Possible approach

Use a stable business key and deterministic deduplication rule; retain delivery metadata for audit.

CHALLENGE / 02

One source changes a field type without notice.

Possible approach

Validate schema at landing, quarantine incompatible input, and communicate the change before promotion.

CHALLENGE / 03

A source is temporarily unavailable during its scheduled window.

Possible approach

Apply bounded retries, preserve the failed watermark, and support a controlled later replay.

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 EXPLANATION

Example interview explanation: “I would describe a batch data platform that brings operational tables, scheduled files, and API data into a raw landing layer. An orchestrator coordinates parameterized ingestion, then SQL and Python transformations standardize records, apply deterministic deduplication, and validate business rules. Validated data is published to warehouse facts and dimensions, while rejected records and run metadata remain available for investigation. I would call out how incremental checkpoints, bounded retries, role-based access, and freshness monitoring address the main reliability risks. In a real project, I would tailor the design to the source SLAs, data classification, and workload measurements.”

Learning example · adapt to your own experience
PROJECT-SPECIFIC QUESTIONS

Practice the follow-up.

01

Explain the project architecture and why it is layered.

02

How did you choose full versus incremental ingestion?

03

How would you handle duplicate or late-arriving records?

04

What would you monitor for freshness and quality?

05

How would you secure source credentials?

06

How would you recover one failed source without rerunning everything?

Practice questions Explore Interview Support
PROJECT CHECKLIST

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