Databricks | Pyspark| AutoLoader: Incremental Data Load |with Demo

AutoLoader in Databricks is a crucial feature that streamlines the process of ingesting and processing large volumes of data efficiently. By automatically detecting and loading new or modified files from cloud storage, AutoLoader enhances data engineers' productivity, reduces latency in data availability, and ensures data accuracy. It plays a pivotal role in enabling timely insights and analytics, making it an indispensable component in modern data architectures.
The Autoloader feature of Databricks looks to simplify incremental loading, taking away the pain of file watching and queue management. However, there can also be a lot of nuance and complexity in setting up Autoloader and managing the process of ingesting data using it.After this session you will be better equipped to use Autoloader in a data ingestion platform, simplifying your production workloads and accelerating the time to realise value in your data!
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[@ajaykiranchundi9979]

Thanks for the kickstart. But I have a question once the python script is executed..it is continuously running and not stopping at all. Is it a general behavior of autoloader ?
Also, if I have to run it as a part of ingestion pipeline..what are your suggestions ?

[@gudiatoka]

Not sure about autoloader as it used stream and in structured streaming we can use trigger available now (it is same as event based trigger in adf)

[@SHARATH2596]

For a beginner would you suggest to learn AWS or azure?

[@ShilpaDataInsights]

You can start with any cloud service AWS or Azure. The basics remains the same.