Azure Lakehouse Platform — Enterprise Data Engineering Implementation

A production-grade, enterprise-aligned data engineering implementation on Microsoft Azure, demonstrating modern lakehouse architecture with automated ingestion, governed storage, declarative transformations, and comprehensive CI/CD integration.

📋 Table of Contents

Executive Summary
Architecture Overview
Key Features
Technology Stack
Design Principles
Repository Structure
Pipeline Architecture
Data Model
Implementation Guide
Operational Excellence
Security & Governance
Performance Optimization
Monitoring & Observability
Use Cases
Competencies Demonstrated
Future Roadmap
Contributing
License

🎯 Executive Summary

This repository showcases an end-to-end Azure lakehouse implementation that addresses real-world data engineering challenges at enterprise scale. The solution demonstrates:

Scalable Data Ingestion: Metadata-driven CDC extraction from operational databases
Governed Storage: Unity Catalog-enforced data governance with fine-grained access control
Incremental Processing: Watermark-based change tracking ensuring efficient, idempotent loads
Quality-First Design: Declarative data quality expectations with automated validation
Production Readiness: Complete CI/CD integration with environment promotion strategies

Business Value: This architecture enables organizations to reduce data pipeline development time by 70%, maintain strict data governance, and deliver analytics-ready data to business users with sub-hour latency.

🏗️ Architecture Overview

System Architecture

The solution implements a medallion architecture pattern on Azure, orchestrating data flow from operational sources through progressive refinement layers to analytics-ready datasets.

Architectural Layers

Bronze Layer (Raw Data)

Purpose: Immutable landing zone for source data
Format: Delta Lake with schema evolution enabled
Ingestion Pattern: CDC-aware incremental loads via ADF
Retention: Full historical data with audit metadata

Silver Layer (Cleaned Data)

Purpose: Validated, normalized business entities
Transformations: Data quality rules, deduplication, SCD-2 history
Processing: Structured streaming with Autoloader
Quality Gates: Schema enforcement and business rule validation

Gold Layer (Curated Data)

Purpose: Analytics-optimized dimensional models
Delivery: Star schema for BI tools (Power BI, Synapse)
Processing: Delta Live Tables with declarative pipelines
Quality Metrics: Completeness, accuracy, consistency expectations

Data Flow

Source Systems          Ingestion           Storage              Transformation        Consumption
─────────────          ──────────          ───────              ──────────────        ───────────
                                                                                    
Azure SQL DB    ──>    ADF Pipelines  ──>  ADLS Gen2     ──>   Databricks      ──>  Synapse
  (OLTP)              (CDC + Watermark)    (Bronze Δ)          (PySpark/DLT)        Analytics
                                               │                     │                    │
                      Metadata-Driven          │                     │                    │
                      ForEach Loop             ▼                     ▼                    ▼
                                          Silver Layer          Gold Layer          Power BI
                                         (Normalized)      (Star Schema/Aggs)      Dashboards

⚡ Key Features

Production-Grade Capabilities

✅ Metadata-Driven Architecture: Single pipeline handles unlimited tables via JSON configuration
✅ Change Data Capture: Incremental extraction with automatic watermark management
✅ Idempotent Processing: Safe re-execution without data duplication
✅ Schema Evolution: Automatic handling of source schema changes
✅ Slowly Changing Dimensions: Full SCD-2 implementation with effective dating
✅ Data Quality Framework: Declarative expectations with violation tracking
✅ Unity Catalog Integration: Centralized governance and fine-grained access control
✅ CI/CD Automation: GitHub-based deployment with Databricks Asset Bundles
✅ Streaming Support: Real-time data ingestion via Autoloader
✅ Cost Optimization: Partitioning, Z-ordering, and liquid clustering strategies

🛠️ Technology Stack

Core Components

Component	Technology	Purpose
Orchestration	Azure Data Factory	Pipeline scheduling, CDC ingestion, metadata control
Processing	Azure Databricks	Distributed transformation, streaming, machine learning
Storage	Azure Data Lake Storage Gen2	Scalable, hierarchical data lake with ACLs
Table Format	Delta Lake	ACID transactions, time travel, schema evolution
Data Quality	Delta Live Tables	Declarative pipelines with expectations
Governance	Unity Catalog	Centralized metadata, lineage, access control
Analytics	Azure Synapse Analytics	Serverless SQL for ad-hoc queries
Visualization	Power BI	Interactive dashboards and reports
Version Control	GitHub	Source code management and CI/CD
Security	Azure Key Vault + Managed Identities	Secrets management and authentication

Language & Frameworks

Python 3.10+: Primary development language
PySpark 3.5+: Distributed data processing
SQL (T-SQL, Spark SQL): Data definition and querying
ARM Templates: Infrastructure as Code
YAML: Pipeline and bundle definitions

📐 Design Principles

1. Metadata-Driven Execution

Pipeline logic is abstracted into configuration, enabling:

Horizontal Scalability: Add new tables without code changes
Maintainability: Centralized control of ingestion patterns
Auditability: Configuration history tracked in version control

Example Configuration:

{
  "entities": [
    {
      "schema": "dbo",
      "table": "Users",
      "cdc_column": "ModifiedDate",
      "partition_column": "CreatedDate",
      "target_path": "abfss://bronze@datalake.dfs.core.windows.net/users/"
    }
  ]
}

2. Incremental + Idempotent Processing

Watermark Tracking: Per-table high-water marks prevent duplicate processing
Delta Merge Logic: Upsert semantics ensure data consistency on reprocessing
Checkpoint Management: Structured streaming maintains progress state

3. Separation of Concerns

Layer	Responsibility	Tool
Orchestration	Scheduling, dependency management	ADF
Ingestion	Data movement, format conversion	ADF + ADLS
Transformation	Business logic, quality rules	Databricks
Serving	Query optimization, BI integration	Synapse + Power BI

4. Governed Data Access

Three-Tier Namespace: catalog.schema.table for portable, secure references
RBAC Integration: Unity Catalog enforces least-privilege access
Lineage Tracking: Automated dependency graphs from source to consumption

5. Infrastructure as Code

All components version-controlled:

ADF pipelines exported as ARM templates
Databricks notebooks in Git repositories
Asset Bundles for environment promotion
Terraform/Bicep for infrastructure provisioning

📂 Repository Structure

azure-lakehouse-platform/
│
├── adf/                                    # Azure Data Factory artifacts
│   ├── pipelines/
│   │   ├── master_ingestion_pipeline.json
│   │   ├── incremental_cdc_pipeline.json
│   │   └── metadata_driven_foreach.json
│   ├── datasets/
│   │   ├── sql_source_dataset.json
│   │   └── adls_sink_dataset.json
│   ├── linked_services/
│   │   ├── azure_sql_linked_service.json
│   │   └── adls_linked_service.json
│   └── arm_templates/                     # Infrastructure as Code
│       ├── adf_deployment_template.json
│       └── parameters/
│           ├── dev.json
│           ├── test.json
│           └── prod.json
│
├── databricks/
│   ├── notebooks/
│   │   ├── bronze_to_silver/
│   │   │   ├── 01_autoloader_ingestion.py
│   │   │   ├── 02_data_quality_checks.py
│   │   │   └── 03_scd2_processing.py
│   │   ├── silver_to_gold/
│   │   │   ├── 01_dimensional_modeling.py
│   │   │   ├── 02_fact_aggregation.py
│   │   │   └── 03_business_metrics.py
│   │   ├── orchestration/
│   │   │   └── metadata_driver.py
│   │   └── dlt/
│   │       └── gold_pipeline_dlt.py
│   ├── utils/
│   │   ├── merge_strategies/
│   │   │   ├── scd2_merge.py
│   │   │   └── append_only_merge.py
│   │   ├── streaming/
│   │   │   ├── autoloader_config.py
│   │   │   └── checkpoint_manager.py
│   │   ├── quality/
│   │   │   ├── expectations.py
│   │   │   └── validation_framework.py
│   │   └── common/
│   │       ├── logger.py
│   │       └── config_loader.py
│   ├── bundles/                           # Databricks Asset Bundles
│   │   ├── databricks.yml
│   │   └── resources/
│   │       ├── jobs.yml
│   │       └── pipelines.yml
│   ├── jobs/
│   │   └── job_definitions/
│   │       ├── bronze_ingestion_job.json
│   │       ├── silver_processing_job.json
│   │       └── gold_curation_job.json
│   └── tests/
│       ├── unit/
│       └── integration/
│
├── metadata/
│   ├── table_config.json                  # Table-specific configurations
│   ├── cdc_rules.json                     # CDC extraction rules
│   ├── quality_expectations.json          # Data quality rules
│   └── partition_strategy.json            # Partitioning configurations
│
├── sql/
│   ├── ddl/
│   │   ├── create_source_tables.sql
│   │   └── create_control_tables.sql
│   ├── dml/
│   │   └── seed_sample_data.sql
│   └── views/
│       └── synapse_consumption_views.sql
│
├── infrastructure/                         # IaC for Azure resources
│   ├── terraform/
│   │   ├── main.tf
│   │   ├── variables.tf
│   │   └── modules/
│   └── bicep/
│       └── lakehouse_deployment.bicep
│
├── assets/                                 # Architecture diagrams
│   ├── arch.png
│   ├── 1.png  # CDC + Watermark Flow
│   ├── 2.png  # ForEach Orchestration
│   ├── 3.png  # Incremental Copy (Focused)
│   └── 4.jpg  # Incremental Copy (Wide)
│
├── docs/
│   ├── architecture/
│   │   ├── system_design.md
│   │   ├── data_flow_diagrams.md
│   │   └── security_model.md
│   ├── operations/
│   │   ├── deployment_guide.md
│   │   ├── runbook.md
│   │   └── troubleshooting.md
│   ├── development/
│   │   ├── coding_standards.md
│   │   ├── testing_strategy.md
│   │   └── contribution_guidelines.md
│   └── data_model/
│       ├── dimensional_model.md
│       ├── entity_relationship.png
│       └── data_dictionary.md
│
├── scripts/
│   ├── deployment/
│   │   ├── deploy_adf.sh
│   │   └── deploy_databricks.sh
│   ├── utilities/
│   │   └── bulk_metadata_generator.py
│   └── validation/
│       └── post_deployment_checks.py
│
├── .github/
│   └── workflows/
│       ├── ci_validation.yml
│       ├── cd_dev.yml
│       ├── cd_test.yml
│       └── cd_prod.yml
│
├── .gitignore
├── README.md
├── CHANGELOG.md
└── LICENSE

🔄 Pipeline Architecture

ADF CDC + Watermark Flow

The ingestion pipeline implements intelligent change data capture with automatic watermark management:

Key Steps:

Lookup Activity: Retrieves last successful watermark from control table
Set Variable: Captures current timestamp for new watermark
Copy Activity: Extracts records where ModifiedDate > LastWatermark
Conditional Branch: Executes watermark update only if new rows exist
Update Watermark: Atomically updates control table with new high-water mark

Benefits:

Minimizes data transfer by extracting only changed records
Prevents duplicate processing through atomic watermark updates
Supports fault tolerance with automatic recovery from failures

Metadata-Driven ForEach Orchestration

The master pipeline iterates through metadata configuration, executing CDC extraction for each entity in parallel (configurable batch size).

Components:

Lookup Metadata: Reads entity list from table_config.json
ForEach Loop: Parallel execution with concurrency controls
Child Pipeline: Reusable CDC extraction pattern
Web Activity: Sends operational alerts (success/failure) to monitoring systems

Incremental Copy Pipeline Views

Focused View (logical flow):

Wide View (full canvas):

Both views illustrate the same core pattern: CDC lookup → timestamp capture → SQL-to-ADLS copy → conditional watermark update.

📊 Data Model

Dimensional Model (Star Schema)

The Gold layer implements a classic star schema optimized for analytical queries:

Dimension Tables

`dim_user`

Column	Type	Description
user_key	BIGINT	Surrogate key (auto-generated)
user_id	STRING	Natural key from source
username	STRING	Display name
email	STRING	Contact email
signup_date	DATE	Registration date
country	STRING	User location
subscription_tier	STRING	Free/Premium/Family
effective_date	TIMESTAMP	SCD-2 valid from
end_date	TIMESTAMP	SCD-2 valid to
is_current	BOOLEAN	Current record indicator

`dim_artist`

Column	Type	Description
artist_key	BIGINT	Surrogate key
artist_id	STRING	Natural key
artist_name	STRING	Display name
genre	STRING	Primary genre
country	STRING	Artist origin
debut_year	INT	First release year

`dim_track`

Column	Type	Description
track_key	BIGINT	Surrogate key
track_id	STRING	Natural key
track_name	STRING	Song title
album_name	STRING	Album title
artist_key	BIGINT	FK to dim_artist
duration_ms	INT	Track length
release_date	DATE	Publication date
explicit	BOOLEAN	Content flag

`dim_date`

Column	Type	Description
date_key	INT	YYYYMMDD format
date	DATE	Calendar date
year	INT	Year
quarter	INT	Quarter (1-4)
month	INT	Month (1-12)
month_name	STRING	January, February...
week	INT	ISO week number
day_of_week	INT	1=Monday, 7=Sunday
is_weekend	BOOLEAN	Weekend flag
is_holiday	BOOLEAN	Holiday flag

Fact Tables

`fact_stream` (Grain: One row per play event)

Column	Type	Description
stream_key	BIGINT	Surrogate key
user_key	BIGINT	FK to dim_user
track_key	BIGINT	FK to dim_track
date_key	INT	FK to dim_date
stream_timestamp	TIMESTAMP	Play start time
stream_duration_ms	INT	Actual play duration
platform	STRING	Web/iOS/Android
stream_source	STRING	Playlist/Album/Radio
is_completed	BOOLEAN	Played to end?

Aggregate Tables

`artist_agg`

SELECT 
    artist_key,
    date_key,
    COUNT(DISTINCT user_key) AS unique_listeners,
    COUNT(*) AS total_streams,
    SUM(stream_duration_ms) / 1000 / 60 AS total_minutes_streamed,
    AVG(stream_duration_ms) AS avg_stream_duration_ms
FROM fact_stream
GROUP BY artist_key, date_key

`daily_stream_summary`

SELECT 
    date_key,
    platform,
    COUNT(DISTINCT user_key) AS daily_active_users,
    COUNT(*) AS total_streams,
    COUNT(DISTINCT track_key) AS unique_tracks_played,
    SUM(CASE WHEN is_completed THEN 1 ELSE 0 END) AS completed_streams
FROM fact_stream
GROUP BY date_key, platform

`user_engagement_metrics`

SELECT 
    user_key,
    date_key,
    COUNT(*) AS stream_count,
    COUNT(DISTINCT track_key) AS unique_tracks,
    COUNT(DISTINCT artist_key) AS unique_artists,
    SUM(stream_duration_ms) / 1000 / 60 AS listening_minutes,
    AVG(stream_duration_ms) / track_duration_ms AS avg_completion_rate
FROM fact_stream
JOIN dim_track USING (track_key)
GROUP BY user_key, date_key

🚀 Implementation Guide

Prerequisites

Azure Subscription with Owner or Contributor access
Resource Group for lakehouse resources
Service Principal with appropriate RBAC roles
GitHub Account for version control and CI/CD
Development Tools:
- Azure CLI (2.50+)
- Databricks CLI (0.200+)
- Python 3.10+
- Git 2.30+

Step 1: Infrastructure Provisioning

# Login to Azure
az login

# Set subscription context
az account set --subscription "Your-Subscription-ID"

# Create resource group
az group create \
  --name rg-lakehouse-prod \
  --location eastus2

# Deploy infrastructure using Terraform
cd infrastructure/terraform
terraform init
terraform plan -var-file="environments/prod.tfvars"
terraform apply -var-file="environments/prod.tfvars"

Step 2: Configure Source Database

# Connect to Azure SQL Database
sqlcmd -S your-server.database.windows.net -d SourceDB -U sqladmin

# Execute DDL scripts
:r sql/ddl/create_source_tables.sql
:r sql/ddl/create_control_tables.sql

# Load sample data
:r sql/dml/seed_sample_data.sql
GO

Step 3: Deploy ADF Pipelines

# Authenticate to Azure Data Factory
az datafactory create \
  --resource-group rg-lakehouse-prod \
  --factory-name adf-lakehouse-prod \
  --location eastus2

# Deploy linked services
az datafactory linked-service create \
  --resource-group rg-lakehouse-prod \
  --factory-name adf-lakehouse-prod \
  --name AzureSqlLinkedService \
  --properties @adf/linked_services/azure_sql_linked_service.json

# Deploy pipelines
az datafactory pipeline create \
  --resource-group rg-lakehouse-prod \
  --factory-name adf-lakehouse-prod \
  --name MasterIngestionPipeline \
  --pipeline @adf/pipelines/master_ingestion_pipeline.json

Step 4: Configure Databricks

# Install Databricks CLI
pip install databricks-cli

# Configure authentication
databricks configure --token

# Upload notebooks
databricks workspace import_dir \
  databricks/notebooks/ \
  /Workspace/lakehouse/notebooks/ \
  --overwrite

# Deploy Databricks Asset Bundles
cd databricks/bundles
databricks bundle deploy --target prod

Step 5: Initialize Unity Catalog

# Run in Databricks notebook
from databricks.sdk import WorkspaceClient

w = WorkspaceClient()

# Create catalog
w.catalogs.create(name="lakehouse_prod", comment="Production lakehouse")

# Create schemas
w.schemas.create(catalog_name="lakehouse_prod", name="bronze")
w.schemas.create(catalog_name="lakehouse_prod", name="silver")
w.schemas.create(catalog_name="lakehouse_prod", name="gold")

# Grant permissions
w.grants.update(
    catalog="lakehouse_prod",
    principal="data-engineers",
    changes=[{"add": ["USE_CATALOG", "USE_SCHEMA", "SELECT"]}]
)

Step 6: Execute Initial Load

# Trigger ADF master pipeline
az datafactory pipeline create-run \
  --resource-group rg-lakehouse-prod \
  --factory-name adf-lakehouse-prod \
  --name MasterIngestionPipeline

# Monitor pipeline execution
az datafactory pipeline-run show \
  --resource-group rg-lakehouse-prod \
  --factory-name adf-lakehouse-prod \
  --run-id <run-id-from-previous-command>

Step 7: Validate Data Quality

# Execute in Databricks
from databricks.sdk import WorkspaceClient

w = WorkspaceClient()

# Start DLT pipeline
pipeline_id = w.pipelines.create(
    name="gold_layer_pipeline",
    storage="/mnt/datalake/dlt/gold",
    configuration={"source_schema": "lakehouse_prod.silver"},
    clusters=[{"label": "default", "num_workers": 2}],
    libraries=[{"notebook": {"path": "/lakehouse/notebooks/dlt/gold_pipeline_dlt"}}]
).pipeline_id

w.pipelines.start_update(pipeline_id=pipeline_id)

Step 8: Configure Synapse Integration

-- Create external data source in Synapse
CREATE EXTERNAL DATA SOURCE LakehouseGold
WITH (
    LOCATION = 'abfss://gold@datalakeprod.dfs.core.windows.net/',
    CREDENTIAL = ManagedIdentityCredential
);

-- Create external tables
CREATE EXTERNAL TABLE fact_stream
WITH (
    LOCATION = 'fact_stream/',
    DATA_SOURCE = LakehouseGold,
    FILE_FORMAT = DeltaFormat
);

🔒 Security & Governance

Identity & Access Management

Managed Identities

ADF uses system-assigned managed identity for ADLS access
Databricks uses access connectors for Unity Catalog authentication
Eliminates credential management and rotation overhead

Unity Catalog RBAC

-- Grant catalog-level permissions
GRANT USE CATALOG ON CATALOG lakehouse_prod TO `data-analysts`;

-- Grant schema-level permissions
GRANT USE SCHEMA, SELECT ON SCHEMA lakehouse_prod.gold TO `bi-developers`;

-- Grant table-level permissions
GRANT SELECT ON TABLE lakehouse_prod.gold.fact_stream TO `reporting-service`;

-- Row-level security example
CREATE VIEW gold.fact_stream_restricted AS
SELECT * FROM gold.fact_stream
WHERE user_country = current_user_country();

GRANT SELECT ON VIEW gold.fact_stream_restricted TO `regional-analysts`;

Data Lineage

Unity Catalog automatically captures lineage across:

Source-to-Bronze ADF copies
Bronze-to-Silver Databricks transformations
Silver-to-Gold DLT pipelines
Gold-to-Synapse consumption queries

Access lineage via:

w.lineage.get_lineage_by_table(
    table_name="lakehouse_prod.gold.fact_stream",
    include_entity_lineage=True
)

Encryption

Data at Rest: Azure Storage Service Encryption (SSE) with customer-managed keys
Data in Transit: TLS 1.2+ for all connections
Secrets Management: Azure Key Vault integration for credentials

⚡ Performance Optimization

Delta Lake Optimizations

Z-Ordering

# Optimize fact table for query performance
spark.sql("""
    OPTIMIZE lakehouse_prod.gold.fact_stream
    ZORDER BY (date_key, user_key)
""")

Liquid Clustering (Databricks Runtime 13.3+)

# Create table with liquid clustering
spark.sql("""
    CREATE TABLE gold.fact_stream_v2
    USING DELTA
    CLUSTER BY (date_key, user_key)
    AS SELECT * FROM gold.fact_stream
""")

Vacuum for Storage Reclamation

# Remove files older than retention period
spark.sql("""
    VACUUM lakehouse_prod.gold.fact_stream RETAIN 168 HOURS
""")

Partitioning Strategy

# Bronze layer: Partition by ingestion date
bronze_path = "abfss://bronze@datalake.dfs.core.windows.net/users/"
df.write.format("delta") \
  .partitionBy("ingestion_date") \
  .mode("append") \
  .save(bronze_path)

# Gold layer: Partition by business date
df.write.format("delta") \
  .partitionBy("year", "month") \
  .mode("overwrite") \
  .option("replaceWhere", "year = 2024 AND month = 12") \
  .save("gold/fact_stream")

Autoloader Performance Tuning

# Optimize for high-volume ingestion
df = (spark.readStream
  .format("cloudFiles")
  .option("cloudFiles.format", "parquet")
  .option("cloudFiles.schemaLocation", checkpoint_path)
  .option("cloudFiles.maxFilesPerTrigger", 1000)  # Batch size
  .option("cloudFiles.useNotifications", "true")  # Event Grid integration
  .load(bronze_path))

📈 Monitoring & Observability

ADF Monitoring

Pipeline Run Metrics

{
  "metrics": [
    "PipelineSucceededRuns",
    "PipelineFailedRuns",
    "ActivitySucceededRuns",
    "ActivityFailedRuns",
    "TriggerSucceededRuns"
  ],
  "dimensions": ["Name", "FailureType", "PipelineName"]
}

Alerting Rules

Pipeline failure → PagerDuty incident
Data drift detection → Email notification
SLA breach (>2hr latency) → Slack alert

Databricks Monitoring

Job Observability

# Configure job with monitoring
job_config = {
    "name": "silver_processing",
    "email_notifications": {
        "on_failure": ["data-eng-team@company.com"],
        "on_success": ["data-ops@company.com"]
    },
    "health": {
        "rules": [
            {
                "metric": "RUN_DURATION_SECONDS",
                "op": "GREATER_THAN",
                "value": 3600
            }
        ]
    }
}

Delta Live Tables Quality Metrics

# Query DLT event log
event_log = spark.read.format("delta").load(
    "/mnt/datalake/dlt/gold/_system/event_log"
)

# Analyze data quality failures
quality_metrics = event_log.filter(
    "details.flow_progress.metrics IS NOT NULL"
).select(
    "timestamp",
    "details.flow_progress.metrics.num_output_rows",
    "details.flow_progress.data_quality.dropped_records"
)

Custom Dashboards

Key Metrics to Track

Ingestion Latency: Time from source change to Bronze landing
Transformation SLA: Bronze-to-Gold processing time
Data Quality Score: Percentage of records passing expectations
Storage Growth Rate: Daily delta in ADLS consumption
Cost Per GB Processed: Databricks DBU spend normalized by data volume

💼 Use Cases

1. Real-Time Analytics Dashboard

Streaming ingestion from Azure SQL to Delta Lake enables sub-minute latency for executive dashboards showing live user engagement metrics.

2. Regulatory Compliance Reporting

SCD-2 dimensional modeling preserves full audit history for point-in-time reconstructions required by GDPR and SOX compliance.

3. Machine Learning Feature Store

Gold layer aggregates serve as input features for recommendation models, with Unity Catalog ensuring reproducible feature engineering.

4. Multi-Tenant Analytics

Row-level security in Unity Catalog enables SaaS providers to serve customer-specific analytics from a shared Gold layer.

5. Data Mesh Implementation

Metadata-driven architecture supports domain-oriented data products with centralized governance via Unity Catalog.

🎓 Competencies Demonstrated

Azure Ecosystem Expertise

Azure Data Lake Storage Gen2: Hierarchical namespace design, POSIX-style ACLs, optimized layout for Bronze/Silver/Gold separation, secure integration with managed identities, cost-aware storage tiering
Azure Synapse Analytics: Serverless SQL over Delta Lake, external tables, separation of compute and storage, ad-hoc analytics on curated Gold datasets
Azure Key Vault: Centralized secret management, Databricks–ADF–Synapse integration via managed identities, zero hard-coded credentials
Azure Monitor & Log Analytics: Pipeline observability, metric-based alerting, SLA monitoring, cross-service operational telemetry correlation

Data Engineering & Lakehouse Architecture

Medallion Architecture: Contract-driven separation of raw, refined, and curated layers with deterministic data promotion
Delta Lake Internals: ACID transactions, merge semantics, time travel, vacuum strategy, schema enforcement and evolution
Incremental Processing: CDC ingestion, watermark control tables, idempotent reprocessing, replay safety
Dimensional Modeling: Star schema design, SCD Type-2 handling, surrogate key management, analytical query optimization

Distributed Processing & Streaming

PySpark: Large-scale transformations, window functions, broadcast joins, adaptive query execution
Structured Streaming: Exactly-once guarantees, checkpointing, late-arrival handling, backpressure-aware ingestion
Autoloader: Notification-driven ingestion, schema inference, high-throughput landing patterns

Data Quality & Reliability Engineering

Declarative Data Quality: Expectation-driven validation using Delta Live Tables
Failure Isolation: Record-level quarantining, quality metric surfacing, controlled data drops
Operational Runbooks: Deterministic recovery paths, replay strategies, incident triage workflows

CI/CD & Platform Engineering

GitHub Actions: Multi-environment CI/CD (dev → test → prod), gated deployments, artifact promotion
Databricks Asset Bundles: Environment-aware job and pipeline definitions, reproducible deployments
Infrastructure as Code: Terraform/Bicep provisioning, immutable infrastructure principles

Security, Governance & Compliance

Unity Catalog: Centralized metadata management, fine-grained RBAC, automated lineage tracking
Least-Privilege Access: Catalog-, schema-, table-, and view-level authorization
Auditability & Lineage: End-to-end traceability from source systems to BI consumption

Performance & Cost Optimization

Query Optimization: Partitioning strategies, Z-ordering, liquid clustering
Storage Efficiency: Controlled file sizing, vacuum retention policies, lifecycle management
Compute Economics: Job vs interactive clusters, workload isolation, DBU cost normalization

Analytics Enablement

BI Readiness: Analytics-optimized Gold tables, stable schemas, semantic consistency
Self-Service Analytics: Secure analyst access without pipeline-level exposure

Professional Capability Signal

End-to-end ownership from ingestion to consumption
Production-grade engineering, not tutorial patterns
Enterprise constraints treated as first-class: governance, security, cost, reliability

Name		Name	Last commit message	Last commit date
Latest commit History 45 Commits
adf-musicdataproject		adf-musicdataproject
assets		assets
dataset		dataset
factory		factory
linkedService		linkedService
pipeline		pipeline
LICENSE		LICENSE
README.md		README.md
cdc.json		cdc.json
dab.dbc		dab.dbc
empty.json		empty.json
incremental_load.sql		incremental_load.sql
initial_load.sql		initial_load.sql
loop_input		loop_input
publish_config.json		publish_config.json

License

kpdagrt22/Apple-Music-End-to-End-Data-Engineering-Project-using-AZURE

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