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* **References:** Provide references from previous employers or mentors.
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Replace `<path>` with the path where you want to store the partitioned Parquet files. When you read the partitioned data, Spark will automatically filter the data based on the partition columns, which can significantly reduce the amount of data that needs to be processed. Finally, consider leveraging **Databricks Delta Lake**. Delta Lake is an open-source storage layer that brings ACID transactions to Apache Spark and big data workloads. It enables building a lakehouse architecture with features like schema enforcement, data versioning, and unified streaming and batch processing. Using Delta Lake can improve data reliability and query performance.
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* **Analyze the Key Metrics:** Focus on the metrics we've discussed, such as same-store sales growth, revenue, and earnings per share. Compare them to expectations and prior periods.