Auto Loader — Handling Incremental ETL with Databricks

Data Handling is one of the crucial segment of any Data related job as proper data planning drives into results which led to efficient and economical storage, retrieval, and disposal of data. When it comes to Data Engineering profile, Data Loading (ETL) plays an equivalent role too. Data Loading can be done in 2 ways — Full Load or Incremental Load. <a href="https://www.databricks.com/" rel="noopener ugc nofollow" target="_blank">Databricks</a> provide a great feature with <a href="https://docs.databricks.com/ingestion/auto-loader/index.html" rel="noopener ugc nofollow" target="_blank">Auto Loader</a> to handle the incremental ETL and taking care of any data that might be malformed and would have been ignored or lost. In this article we will mainly focus on Incremental ETL process, for full load we will discuss some other day ;) <a href="https://medium.com/@riyukhandelwal/auto-loader-handling-incremental-etl-with-databricks-ae71687a281b">Read More</a>