Description:

Learn how to implement a real-time data streaming project using Kafka, Spark, Databricks, and Azure. This course will provide you with the necessary skills to build scalable and efficient data pipelines for processing streaming data.

Key Highlights:

Hands-on experience with Kafka, Spark, Databricks, and Azure
Building real-time data pipelines for processing streaming data
Scalable and efficient project implementation

What you will learn:

Establishing the necessary infrastructure for project development
Setting up Confluent Kafka on a cloud platform.
Performing data cleaning, modeling, and transformations.
Utilizing Azure DevOps for version control.
Managing sprints and tracking development progress in the ADO board.
Handling bug tracking and resolution in the ADO Board throughout the SDLC.
Developing ADF pipelines for batch or full data loads.
Managing near real-time streaming data using Databricks Autoloader and Delta Live Tables (DLT).
Implementing metadata-driven ETL pipelines.
Configuring logging, email alerting, and error log monitoring.
Deploying the project to a production environment.
Possessing functional knowledge in the banking domain.