Streaming Data Using DLT with Auto Loader and Hugging Face Sentiment Analysis

Modern data engineering requires more advanced data lifecycle for data ingestion, transformation, and processing. In this session, learn how the Databricks Lakehouse Platform provides an end-to-end data engineering solution — ingestion, processing and scheduling — that automates the complexity of building and maintaining pipelines and running ETL workloads directly on a data lake, so your team can focus on quality and reliability to drive valuable insights.

Try Databricks: https://databricks.com/try-databricks?itm_data=NavBar-TryDatabricks-Trial
Note: Databricks Lakeflow unifies Data Engineering with Lakeflow Connect, Lakeflow Spark Declarative Pipelines (previously known as DLT), and Lakeflow Jobs (previously known as Workflows).

Видео Streaming Data Using DLT with Auto Loader and Hugging Face Sentiment Analysis канала Databricks