Learn ETL & Big Data Processing - Azure Data Factory(ADF), SQL, Blob, Datalake, REST API, PySpark, Databricks, DeltaLake
InstructorAtchyut Kumar
Editorial course preview
These 4 complementary views highlight concrete, legible examples from the course presentation.
This slide outlines the Azure Data Factory curriculum, listing key topics like Activities, Pipelines, Triggers, Datasets, Integration Runtime, and Data Flows alongside a visual diagram.
This slide illustrates the Extract, Transform, Load (ETL) workflow, showing how data moves from various sources through a staging area into a data warehouse before being transmitted to business intelligence tools.
This slide introduces instructor Atchyut Kumar A, highlighting his M.Tech qualification from NIT Calicut, GATE achievements, and over 12 years of experience as a Senior Consultant.
This slide outlines the intended audience for an Azure Data Factory course, targeting IT developers, university students, database professionals, BI developers, and aspiring data engineers.
Instructional signals highlight that the instructor explains complex topics from the ground up in a logical and accessible manner.
The curriculum spans essential data engineering tools including ADF, SQL (including window functions and CTEs), Databricks-PySpark, and Delta Lake.
One signal suggests that specific modules, such as log maintenance and certain technical topics like triggers, may be incomplete or missing. Note that this signal predates the displayed update date, which does not prove a correction has been made.
Learner signals indicate a lack of dedicated exercise resources or hands-on implementation materials to accompany the video content. Note that this signal predates the displayed update date, which does not prove a correction has been made.
The curriculum aligns well with the target audience of beginners and professionals seeking to learn Azure data engineering fundamentals.