I worked on Azure-based Data Engineering projects involving ADF, Databricks, ADLS, and PySpark. My role included building ingestion pipelines, developing transformation logic, optimizing Spark jobs, and handling deployments through Azure DevOps. In my recent project, we processed large-scale transactional data and built reporting-ready Gold layer datasets for business teams.
ADF mainly supports Schedule Trigger, Tumbling Window Trigger, and Event Trigger. Schedule triggers run pipelines at fixed times, tumbling windows are used for dependency-based incremental processing, and event triggers start pipelines when files arrive in storage. I mostly used event triggers for near real-time ingestion workflows.
SCD Type 1 is implemented by overwriting old data, while Type 2 maintains historical records using active flags and effective dates. In PySpark, I usually implement SCD logic using Delta Lake MERGE statements for efficient updates and inserts. This helps maintain scalable historical tracking for dimension tables.
First, I analyze the failed activity from the ADF Monitor section and identify the root cause. After fixing the issue, I rerun the failed activity or restart the pipeline from a checkpoint stage. We also use control tables and parameterized pipelines to avoid rerunning completed steps.
We integrate Databricks notebooks with Azure DevOps or GitHub repositories for version control. CI/CD pipelines automate notebook deployment across DEV, UAT, and PROD environments using YAML or release pipelines. Environment-specific configurations are parameterized to reduce manual changes during deployments.
ADF provides activities like Copy Activity for data movement, Lookup for metadata reading, and ForEach for iterative processing. Execute Pipeline activity helps trigger child pipelines, while Databricks Notebook activity runs transformation notebooks. We also use If Condition and Stored Procedure activities for workflow control.
Delta Live Tables help automate reliable ETL pipelines with built-in monitoring and dependency management. Batch processing is used for scheduled historical data loads, while streaming is preferred for near real-time ingestion scenarios. I use streaming mainly for continuously arriving transactional or event data.
I implement validations such as null checks, duplicate detection, schema validation, and row count reconciliation. Failed records are redirected to error tables for further analysis instead of stopping the entire pipeline. Audit logging and monitoring frameworks help maintain data reliability and traceability.
Our architecture involved source systems feeding data into ADLS through ADF pipelines. Databricks processed Bronze, Silver, and Gold layers using PySpark transformations and Delta Lake. The curated data was then consumed by Power BI and downstream analytics applications.
I handle schema evolution using Delta Lake schema merge and Databricks Auto Loader features. Before applying schema changes, validations are performed to avoid breaking downstream processes. We also maintain version-controlled schema definitions and monitoring alerts for unexpected changes.
I optimize Spark jobs using partitioning, broadcast joins, caching, and predicate pushdown techniques. I also avoid unnecessary shuffle operations and replace expensive UDFs with native Spark functions whenever possible. Proper cluster sizing and Adaptive Query Execution also improve overall performance.
Partitioning divides large datasets into smaller logical chunks based on columns like date or region. This improves query performance because Spark scans only relevant partitions instead of the entire dataset. It also increases parallelism and reduces execution time for large-scale processing.
In one production issue, the pipeline failed because of an unexpected schema change from the source system. I quickly analyzed logs, updated the transformation logic, validated the impacted data, and reran the failed jobs. Clear communication with stakeholders helped minimize business impact and downtime.
I avoid deep technical jargon and explain solutions using business-oriented language and simple examples. Instead of discussing Spark transformations, I explain how the solution improves reporting accuracy, processing speed, or operational efficiency. This helps clients understand business value more clearly.
I would first understand the priority and explain the technical effort and risks involved transparently. Then I would propose a phased delivery approach focusing on critical features first. Clear communication and realistic milestone planning usually help align expectations effectively.
In one project, data, QA, and reporting teams had overlapping deadlines and dependency conflicts. I coordinated regular sync meetings, tracked blockers actively, and prioritized tasks based on business impact. Proper communication and dependency management helped us deliver the project on time.
๐ ๐ต๐ฎ๐๐ฒ ๐ฐ๐ฟ๐ฒ๐ฎ๐๐ฒ๐ฑ ๐ฎ ๐๐ผ๐บ๐ฝ๐น๐ฒ๐๐ฒ ๐ฃ๐ฟ๐ฒ๐ฝ๐ฎ๐ฟ๐ฎ๐๐ถ๐ผ๐ป ๐๐๐ถ๐ฑ๐ฒ ๐ณ๐ผ๐ฟ ๐๐ฎ๐๐ฎ ๐๐ป๐ด๐ถ๐ป๐ฒ๐ฒ๐ฟ๐.
๐๐ฒ๐ ๐๐ต๐ฒ ๐๐๐ถ๐ฑ๐ฒ ๐ต๐ฒ๐ฟ๐ฒ – ๐https://topmate.io/kasi_v/1823412?utm_source=public_profile&utm_campaign=kasi_v
If you’ve read this far, LIKE ๐ and RESHARE ๐ to help more engineers prepare confidently.