Smart Big Data Analytics Solution for Next-Gen Digital Car Wash Operations


For an automated digital car wash company, we built a robust Big Data analytics solution by integrating two REST APIs—QuickBooks and SuperOperator—into a fully automated data pipeline. Using Python on Databricks, we extracted 25+ tables daily and implemented a Delta Lakehouse architecture with three layers: raw, curated, and semantic. Data was ingested into Azure ADLS (raw layer), transformed using PySpark and Spark SQL within Databricks, and automated via Azure Data Factory (ADF) with metadata-driven parameters. The curated layer housed refined data, while the semantic layer—built on a dedicated SQL pool in Azure Synapse—served as the analytics-ready environment. We connected Synapse to Power BI, delivering not only the requested tables but also highly visual, interactive dashboards that enhanced decision-making. Additionally, we managed complete Power BI administration, including workspace ownership, scheduled refreshes, gateway configuration, and row-level security for sensitive financial reports, ensuring secure, efficient, and insightful data delivery. Read More... 

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