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PROJECT WALKTHROUGH / ETL / ELT

Azure ETL Pipeline

Orchestrate operational extraction, validation, and cloud warehouse loading with a parameterized pipeline design.

IntermediateETL / ELTBanking5 learning stages
Azure Data FactoryAzure SQLSnowflake
Start project Practice questions Interview support
ARCHITECTURE / LEARNING VIEW
01Azure SQL

Operational tables with a reliable update timestamp.

Azure SQL
02ADF pipeline

Parameterized copy and validation workflow.

Azure Data Factory
03Landing

Date-partitioned files with pipeline run metadata.

Azure Storage
04Transform

Type casting, standardization, and reconciliation.

SQL · Python
05Warehouse

Validated tables for downstream reporting.

Snowflake
DESIGN WALKTHROUGH5 LAYERS
PROJECT OVERVIEW

Understand the system before building it.

This project walks through an orchestration-first design using Azure Data Factory concepts and a cloud SQL source. It covers linked services, datasets, triggers, monitoring, and a Snowflake target as a learning scenario.

WHAT YOU WILL EXPLOREExplain linked services, datasets, activities, and triggersDesign parameterized scheduled ingestionPlan run monitoring and data reconciliation
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

A generic financial operations team needs a consistent daily dataset from operational records for downstream reconciliation and reporting.

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

A repeatable design makes the data flow, run outcome, and failure location easier to reason about. No actual banking data or production integration is provided.

02
DEFINE THE BOUNDARIES

Requirements

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

FUNCTIONAL / WHAT IT DOES
  • Extract account and transaction data on a defined schedule
  • Load a typed, queryable warehouse target
  • Support date-range replay and source reconciliation
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.

01Azure SQL

Operational tables with a reliable update timestamp.

Azure SQL
02ADF pipeline

Parameterized copy and validation workflow.

Azure Data Factory
03Landing

Date-partitioned files with pipeline run metadata.

Azure Storage
04Transform

Type casting, standardization, and reconciliation.

SQL · Python
05Warehouse

Validated tables for downstream reporting.

Snowflake
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 database

Operational Azure SQL

Example data

Account and transaction records

Frequency

Daily schedule with incremental watermark

Ingestion

ADF copy activity via linked service and parameterized dataset

Potential issues
Source throttlingSchema driftNetwork connectivityLate changes
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

  • Define linked services for source, landing, and target connections.
  • Parameterize processing date and source table.
  • Schedule through a trigger and pass explicit run parameters.
  • Check file or load completion before transformation begins.
SourcePipelineLandingRaw
06

Transformation

  • Standardize transaction timestamp and amount types.
  • Apply business rules for valid account and transaction state.
  • Deduplicate using transaction key and update sequence.
  • Record rejected values with a reason rather than hiding them.
-- Illustrative quality check
SELECT business_key, COUNT(*) AS row_count
FROM staged_records
GROUP BY business_key
HAVING COUNT(*) > 1;
07

Storage / warehouse

  • Use a landing container for source-aligned snapshots or deltas.
  • Keep staging structures separate from curated warehouse tables.
  • Publish a reconciliation-ready transaction fact and account dimension.
  • Retain run identifiers for traceability.
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
  • Validate required account and transaction identifiers.
  • Compare source, landed, and target row counts.
  • Check amount range, currency code, and accepted statuses.
  • Track data freshness against the planned schedule.
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 activity-level retry only for transient failures.
  • Capture the failed activity and pipeline run ID.
  • Keep the last successful watermark unchanged until target commit.
  • Make replay parameters explicit for safe recovery.

10 / Performance

  • Use incremental extraction for large operational tables.
  • Tune copy parallelism based on source capacity and measured load.
  • Push suitable filtering to the source query.
  • Avoid over-parallelizing a constrained SQL source.

11 / Security

  • Use managed identity where supported for Azure resources.
  • Store external credentials in a secret manager in a real deployment.
  • Limit access by pipeline role and environment.
  • Mask sensitive account fields in consumer-facing datasets.
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.

  • Inspect pipeline and activity run status, duration, and retries.
  • Compare rows read, written, and rejected.
  • Track freshness and late-file events.
  • Alerts and integrations are design concepts only in this walkthrough.
OBSERVABILITY DESIGNCONCEPT
Pipeline statusRun state
Job durationElapsed time
Record countsRead · written · rejected
FreshnessLast successful data time
Data qualityRule outcomes
Access auditWho · what · when

Azure ETL 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

The source system has a limited query window.

Possible approach

Use bounded incremental extraction and coordinate parallelism with the source owner.

CHALLENGE / 02

A scheduled run starts before the source export is complete.

Possible approach

Add a file/data readiness gate and a bounded wait/retry design.

CHALLENGE / 03

A pipeline retry would reload already-written rows.

Possible approach

Use idempotent target keys or partition replacement and only advance the checkpoint after a successful commit.

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: “The learning design extracts account and transaction changes from Azure SQL through a parameterized ADF pipeline. Linked services define connections, datasets describe locations and structure, and a scheduled trigger supplies a processing window. Data lands with a run ID, then validation and transformations prepare warehouse tables. I would compare source and target counts, preserve the previous watermark on failure, and monitor activity duration, retries, and freshness. The credentials, alerting, and deployment details would be environment-specific in a real implementation.”

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

Practice the follow-up.

01

What is the difference between a linked service and a dataset?

02

How do you parameterize a reusable pipeline?

03

How would you prevent a watermark advancing after a failed load?

04

Which monitoring signals would you check after a run?

Practice questions Explore Interview Support
PROJECT CHECKLIST

Work through the build.

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