Top 10 Azure Data Factory Interview Questions

Azure Data Factory is a cloud-based Microsoft tool that collects raw business data and transforms it into usable information. There is a considerable demand for Azure Data Factory Engineers in the industry. Hence, cracking its interview needs a bit of homework. This Azure Data Factory Interview Questions blog contains the most probable questions asked during Data Engineers' job interviews.

Azure Data Factory Interview Questions 1) Why do we need Azure Data Factory? Why ADF

Azure Data factory does not store any data itself; it lets you create workflows that orchestrate the movement of data between supported data stores and data processing. You can monitor and manage your workflows using both programmatic and UI mechanisms. Apart from that, it is the best tool available today for ETL processes with an easy-to-use interface. This shows the need for Azure Data Factory.

2) What is Azure Data Factory? Azure Data Factory

Azure Data Factory is a cloud-based integration service offered by Microsoft that lets you create data-driven workflows for orchestrating and automating data movement and data transformation overcloud. Data Factory services also offer to create and running data pipelines that move and transform data and then run the pipeline on a specified schedule.

Check out: Azure Data Factory

3) What is Integration Runtime? Integration Runtimes

Integration runtime is nothing but a compute infrastructure used by Azure Data Factory to provide integration capabilities across different network environments.

Types of Integration Runtimes:

Azure Integration Runtime – It can copy data between cloud data stores and dispatch the activity to a variety of computing services such as SQL Server, Azure HDInsight Self Hosted Integration Runtime – It is software with essentially the same code as Azure Integration runtime, but it is installed on on-premises systems or virtual machines over virtual networks. Azure SSIS Integration Runtime – It helps to execute SSIS packages in a managed environment. So when we lift and shift the SSIS packages to the data factory, we use Azure SSIS Integration Runtime.

Azure Data Factory Interview Questions

4) How much is the limit on the number of integration runtimes? Limit

There is no specific limit on the number of integration runtime instances. But there is a limit on the number of VM cores used by Integration runtime based on per subscription for SSIS package execution.

5) What is the difference between Azure Data Lake and Azure Data Warehouse? DataLake_Vs_Warehouse

Azure Data Lake Data Warehouse Data Lake is a capable way of storing any type, size, and shape of data. Data Warehouse acts as a repository for already filtered data from a specific resource. It is mainly used by Data Scientists. It is more frequently used by Business Professionals. It is highly accessible with quicker updates. It becomes a pretty rigid and costly task to make changes in Data Warehouse. It defines the schema after when the data is stored successfully. Datawarehouse defines the schema before storing the data. It uses ELT (Extract, Load and Transform) process. It uses ETL (Extract, Transform and Load) process. It is an ideal platform for doing in-depth analysis. It is the best platform for operational users. Check out: Azure Data Lake

6) What is Blob Storage in Azure? Azure Blob S

It helps to store a large amount of unstructured data such as text, images or binary data. It can be used to expose data publicly to the world. Blob storage is most commonly used for streaming audios or videos, storing data for backup, and disaster recovery, storing data for analysis etc. You can also create Data Lakes using blob storage to perform analytics.

7) Difference between Data Lake Storage and Blob Storage. DataLake_Vs_Blob

Data Lake Storage Blob Storage It is an optimized storage solution for big data analytics workloads. Blob Storage is general-purpose storage for a wide variety of scenarios. It can also do Big Data Analytics. It follows a hierarchical file system. It follows an object store with a flat namespace. In Data Lake Storage, data is stored as files inside folders. Blob storage lets you create a storage account. Storage account has containers that store the data. It can be used to store Batch, interactive, stream analytics, and machine learning data. We can use it to store text files, binary data, media storage for streaming and general purpose data. Check out: Azure Blob Storage

8) What are the steps to create an ETL process in Azure Data Factory? ETL Process

There are straightforward steps to create an ETL process. We need to create a service for a linked data store which is an SQL Server Database. Let’s assume that we have a car dataset. For this car’s dataset, we can create a linked service for the destination data store that is Azure Data Lake. Now create a data set for Data Saving. Create a Pipeline and Copy Activity. Finally, schedule a pipeline by adding a trigger.

9) What is the difference between Azure HDInsight and Azure Data Lake Analytics? LakeAnalytics_Vs_HDInsight

Azure HDInsight Azure Data Lake Analytics It is a Platform as a Service. It is a Software as a Service. Processing data in it requires configuring the cluster with predefined nodes. Further, by using languages like pig or hive, we can process the data. It is all about passing the queries written for data processing. Data Lake Analytics further creates compute nodes to process the data set. Users can easily configure HDInsight Clusters at their convenience. Users can also use Spark, Kafka, without restrictions. It does not give that much flexibility in terms of configuration and customization. But, Azure manages it automatically for its users.

10) What are the top-level concepts of Azure Data Factory? There are four basic top-level concepts of Azure Data Factory:

Pipeline – It acts as a carrier where lots of processes take place. Activities – It represents the steps of processes in the pipeline. Data Sets – It is a data structure that holds our data. Linked Services– These services store information that is essential while connecting the resources or other services. Let’s say we have an SQL server, so we need a connecting string connected to an external device, and we will mention the source and the destination for it.