Event Driven Data Ingestion Using Azure Databricks Autoloader

When:

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Track:

Meetup April 2023

Databricks Auto Loader is designed to solve the problem of getting data into the Databricks platform for processing and analysis. The process of loading data into a data platform like Databricks can be time-consuming and complex, especially when working with large volumes of data or data from multiple sources. The Auto Loader feature simplifies this process by allowing users to specify a set of external data sources and a schedule for data loading, and then automatically loading the data from those sources at the specified intervals.

By automating the data loading process, the Auto Loader helps users to save time and effort, and enables them to focus on other tasks such as data processing and analysis. It also helps to ensure that data is loaded consistently and reliably, reducing the risk of errors or data loss.

Databricks Auto Loader helps to make it easier for users to work with large volumes of data and to get that data into the platform quickly and efficiently, enabling them to focus on using the data to drive insights and business value.