Succession of Azure Databricks and Data Factory initiatives were executed, establishing holistic enterprise-grade data platform.
The extant data warehouse employing Azure Data Factory (ADF) for ETL processes.
Self-Hosted Integration Runtimes interfaced with legacy systems, while Copy Activity with PolyBase proficiently
ingested data into Azure Synapse Analytics. Azure Databricks managed intricate transformations leveraging PySpark DataFrames and SQL,
orchestrated by ADF’s Databricks Notebook Activity.
A subsequent initiative introduced real-time processing capabilities,
integrating Azure Event Hubs for IoT data ingestion and utilizing Databricks Structured Streaming with watermarking.
Delta Lake architecture featuring bronze, silver, and gold tables, facilitating ACID transactions,
temporal data travel, and Slowly Changing Dimension (SCD) Type 2 historical data tracking.
Sophisticated analytics and machine learning. MLflow in Databricks managed experiment tracking model deployment using MLflow Registry.
ADF pipelines with ForEach activities orchestrated regular model retraining Databricks’ distributed ML libraries.
As data volumes burgeoned, performance optimizations were indispensable. Dynamic partition pruning, Z-ORDER indexing, Auto Loader
Databricks enhanced query performance data ingestion efficiency on petabyte-scale datasets.
A data governance initiative Azure Purview for data cataloging and lineage.
Custom classifiers Spark UDFs identified industry-specific sensitive data, Data Factory’s Mapping Data Flows captured lineage information.
Security enhancements encompassed fine-grained access controls in Databricks using Table ACLs and centralized secrets management with Azure Key Vault referenced by ADF linked services.
ETL modernization transitioned from batch to micro-batch processing, using ADF’s tumbling window triggers and Databricks’ checkpoint mechanisms for exactly-once processing with Structured Streaming foreachBatch.
A comprehensive observability solution was implemented using Azure Monitor, Log Analytics workspaces, and Power BI dashboards with DirectQuery for real-time platform health monitoring and custom metric tracking.
Cost optimization strategies included Databricks instance pools, refined autoscaling policies with min/max workers, and dynamic pipeline generation in ADF using the REST API and Azure Functions.
Throughout these initiatives, I leveraged the latest features of both platforms, including Databricks’ Delta Engine, Unity Catalog for granular permissions, and Photon for vectorized query execution, as well as ADF’s data flows with Power Query and auto-update functionalities for integration runtimes.
The resultant platform accommodates a broad spectrum of use cases from BI reporting to advanced machine learning, processing petabytes of data using distributed computing paradigms and delivering substantial business value across the organization.