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Crack Any Azure Data Engineering Interview | Medallion Architecture Part 2 | Project Walkthrough
This is Part 2 of the most complete Medallion Architecture walkthrough on Azure —
your ultimate Data Engineering Interview Project Guide.
✅ Missed Part 1? Watch here: https://youtu.be/0hgnQrD6-AM?si=6wEUcP3b93FuHfhG
In this video I cover:
Azure Databricks & Delta Lake (02:00 - 09:09): The video explains using Delta Lake for reliable ACID transactions and features like time travel and Change Data Capture (CDC). It also highlights Unity Catalog for governance, lineage, and security (10:13).
CI/CD & Git Integration (10:00 - 14:35): Discussion on connecting Azure Data Factory (ADF) to Azure Repos, utilizing the `adf_publish` branch, and employing parameterized ARM templates for environment-specific deployments.
Monitoring & Alerting (18:00 - 17:52): Guidance on monitoring pipelines using Log Analytics with KQL queries, alongside Azure Monitor and built-in Databricks job alerting.
Data Quality (25:00 - 20:30): Best practices for implementing data quality checks (completeness, accuracy, consistency) using the Quarantine pattern and tools like Great Expectations or dbt at the Silver layer.
Star Schema & Dimensional Modeling (33:00 - 24:35): A deep dive into Fact vs. Dimension tables, implementing SCD Type 2 for history, and using bridge tables to resolve many-to-many relationships.
Architecture Overview (41:00 - 31:18): A complete end-to-end architecture visual, detailing the flow from source systems through ADF and Databricks to consumption layers like Power BI and Synapse.
------------------------------------------
🎯 INTERVIEW PROJECT GUIDE — Key Talking Points:
- Medallion: idempotency, Bronze (append), Silver (MERGE), Gold (aggregate)
- Ingestion: metadata-driven, watermark incremental loads, CDC
- ADF: ForEach + Lookup pattern, Integration Runtime types
- Git & ARM: adf_publish branch, Bicep over ARM JSON, env parameter overrides
- Monitoring: KQL + Log Analytics, Action Groups, pipeline vs activity-level failures
- Data Quality: quarantine pattern, Great Expectations / dbt, Delta constraints
- Star Schema: SCD Type 2, surrogate vs business key, bridge tables
------------------------------------------
RESOURCES FROM THIS VIDEO:
- PPT Slides: [link]
- Speaker Script & Checklist Doc: [link]
------------------------------------------
If you found this helpful, LIKE, COMMENT & SUBSCRIBE for more Azure
Data Engineering interview content! 🔔
#DataEngineering #Databricks #DeltaLake #MedallionArchitecture #DataInterviewPrep
#AzureDataEngineering #StarSchema #UnityCatalog #ADF #DataEngineeringInterview
#Azure #PySpark #DataQuality #ETL #DataPipeline #NileshTiwari #dataEngineeringConcepts
#InterviewPrep #AzureDatabricks #DataEngineeringProject
Видео Crack Any Azure Data Engineering Interview | Medallion Architecture Part 2 | Project Walkthrough канала Data Engineering concepts
your ultimate Data Engineering Interview Project Guide.
✅ Missed Part 1? Watch here: https://youtu.be/0hgnQrD6-AM?si=6wEUcP3b93FuHfhG
In this video I cover:
Azure Databricks & Delta Lake (02:00 - 09:09): The video explains using Delta Lake for reliable ACID transactions and features like time travel and Change Data Capture (CDC). It also highlights Unity Catalog for governance, lineage, and security (10:13).
CI/CD & Git Integration (10:00 - 14:35): Discussion on connecting Azure Data Factory (ADF) to Azure Repos, utilizing the `adf_publish` branch, and employing parameterized ARM templates for environment-specific deployments.
Monitoring & Alerting (18:00 - 17:52): Guidance on monitoring pipelines using Log Analytics with KQL queries, alongside Azure Monitor and built-in Databricks job alerting.
Data Quality (25:00 - 20:30): Best practices for implementing data quality checks (completeness, accuracy, consistency) using the Quarantine pattern and tools like Great Expectations or dbt at the Silver layer.
Star Schema & Dimensional Modeling (33:00 - 24:35): A deep dive into Fact vs. Dimension tables, implementing SCD Type 2 for history, and using bridge tables to resolve many-to-many relationships.
Architecture Overview (41:00 - 31:18): A complete end-to-end architecture visual, detailing the flow from source systems through ADF and Databricks to consumption layers like Power BI and Synapse.
------------------------------------------
🎯 INTERVIEW PROJECT GUIDE — Key Talking Points:
- Medallion: idempotency, Bronze (append), Silver (MERGE), Gold (aggregate)
- Ingestion: metadata-driven, watermark incremental loads, CDC
- ADF: ForEach + Lookup pattern, Integration Runtime types
- Git & ARM: adf_publish branch, Bicep over ARM JSON, env parameter overrides
- Monitoring: KQL + Log Analytics, Action Groups, pipeline vs activity-level failures
- Data Quality: quarantine pattern, Great Expectations / dbt, Delta constraints
- Star Schema: SCD Type 2, surrogate vs business key, bridge tables
------------------------------------------
RESOURCES FROM THIS VIDEO:
- PPT Slides: [link]
- Speaker Script & Checklist Doc: [link]
------------------------------------------
If you found this helpful, LIKE, COMMENT & SUBSCRIBE for more Azure
Data Engineering interview content! 🔔
#DataEngineering #Databricks #DeltaLake #MedallionArchitecture #DataInterviewPrep
#AzureDataEngineering #StarSchema #UnityCatalog #ADF #DataEngineeringInterview
#Azure #PySpark #DataQuality #ETL #DataPipeline #NileshTiwari #dataEngineeringConcepts
#InterviewPrep #AzureDatabricks #DataEngineeringProject
Видео Crack Any Azure Data Engineering Interview | Medallion Architecture Part 2 | Project Walkthrough канала Data Engineering concepts
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9 апреля 2026 г. 22:33:18
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