We are talking about a Databricks pipeline. Which pipeline did you develop, and which database did you use?
💡 Model Answer
In my recent project I built a Databricks pipeline that ingested streaming logs from Kafka, performed real‑time aggregation, and wrote the results to Delta Lake. The pipeline was orchestrated using Databricks Jobs and structured streaming. For the database layer, I used Delta Lake as the storage format, which provides ACID transactions and schema enforcement. Delta Lake is built on top of Parquet files stored in Azure Data Lake Storage or S3, and it integrates seamlessly with Spark SQL. The pipeline also leveraged a Snowflake data warehouse for downstream analytics, connecting via the Snowflake Connector for Spark. This combination allowed me to maintain a single source of truth in Delta Lake while feeding curated datasets into Snowflake for business intelligence.
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