Crack Azure Data Engineering by Interview Preparation Course

Explore Azure storage fundamentals, blob storage concepts, and data storage services, highlighting durability, security, scalability, and the hierarchical file system of gen two.

Explore level two Azure storage interview questions to understand why storage account names must be unique and how endpoints and urls like blob.core.windows.net are formed.

Explore factors affecting storage account costs in Azure, including region, account type, access tier, redundancy, transactions, and data egress. Learn how to optimize by choosing appropriate options based on requirements.

Learn to generate a comprehensive inventory report of blob metadata in an Azure storage account by configuring blob inventory rules, selecting fields, and scheduling daily or weekly reports.

Enable the static website feature on an Azure storage account to host a cheat sheet in static html in a blob container, accessible via internet url.

Learn to create and deploy a static web page on Azure storage by enabling the static website feature, uploading ABC.html to the dollar web container, and using the endpoint url.

Change the redundancy level of an Azure storage account after creation via the portal. Open configurations, select application level, and save to update redundancy; you may also adjust account keys.

Explore how Azure Data Factory orchestrates data migration and ETL, including copy activities moving data from on-prem to the cloud, data flow transformations, and pipelines as sequences of activities.

Explore four levels of parameterization in Azure Data Factory: linked service, dataset, pipeline, and global account parameters, with practical steps to create and apply each parameter.

Azure Data Factory user properties attach to activities and may be dynamic for execution metrics. Annotations are static tags at pipeline, dataset, linked service, or trigger levels for grouping.

Explore the integration runtime types in Data Factory—Azure integration runtime (auto resolve), self hosted, and Azure exercise integration runtime—and their use cases for on-prem data transfer and lift-and-shift migrations.

Not mandatory to create your own integration runtime; Azure data factory provides default integration runtime for cloud data movement, while self-hosted runtime enables on-premises to cloud migrations and linked services.

Learn to migrate on-prem data to Azure with Azure Data Factory by creating a self-hosted integration runtime, configuring linked services and datasets, and executing a successful copy pipeline.

Compare Apache Spark, Databricks, and PySpark to clarify differences, including open‑source Spark, Databricks runtime improvements, and the Python library required to run Spark code.

Explore Spark SQL, a Spark module for structured data processing that provides data frames and a distributed SQL engine to transform data using SQL, making Spark accessible to SQL developers.

Learn how to create a Databricks cluster using the UI, selecting cluster type (single node or high concurrency standard), runtime version, auto scaling, inactivity termination, and worker and driver configurations.

Automate the execution of a Databricks notebook by scheduling a job in the Databricks workflow, or via an Azure Data Factory pipeline using a notebook activity.