These artifacts are part of a blog that describes a sample use case for predicting POS sales across stores, coordinated with inventory and supply chain data. PySpark is used for data and ML pipelines on Databricks, orchestrated with Control-M from BMC, to predict POS and forecast inventory items in Production.
• Databricks Intelligent Data Platform on Azure • PySpark • Python Pandas library • Python Seaborn library for data visualization • Jupyter Notebooks on Databricks • Parquet and Delta file format
• Code for data ingestion, processing, ML training and serving, saving forecasted results to Databricks Lakehouse in delta format. • Code for workflow and orchestration with Control-M to coordinate all the activities and tasks and handle failure scenarios