Azure Data Factory is a widely used cloud-based ETL and data integration service contributing its part to the advancement of data engineering. Our Azure Data Factory interview questions blog offers you the knowledge required to give your best in your next job interview. The content furnished in this blog is suitable for freshers, as well as experienced candidates.
|To make things clear, we have designed this ADF interview questions into three categories|
There are multiple concepts in Azure Data factory, to make things clear, let's discuss concept-wise ADF interview questions and their simplified answers.
We all know the importance of data in today's technology-driven world. Data is growing at an alarming rate, and this data can come from different sources and in a variety of types. Getting the insights hidden in big data, becomes complex. The data needs to undergo operations like combining data from various sources, data transformation, data orchestration, building workflows, and more.
It is a challenging task to perform all these operations using traditional tech tools. Azure data factors offer next-generation capabilities and simplify end-to-end data movement processes.
Azure Data Factory is a cloud-based platform that specializes in data integration and ETL operations. It simplifies the process for the data engineers to transform data at scale and build workflows for data movement.
Azure data factory streamlines the process to build data pipelines to ingest data from diversified sources. It allows building basic to advanced ETL processes to transform data using modern computing services such as Azure data bricks, Azure SQL database, etc. Using ADF you can publish data to data warehouse & BI tools.
ADF is a PaaS ( Platform as a Service) offering that specializes in data movement between on-premises & cloud and performs data transformation.
ETL is a core component of the Data engineering process and stands for Extract, Transform and Load. In simple terms, it gathers data from different sources ( Extraction), turns data into the required format (transformation), and stores data in a defined target (loading).
Data Orchestration is an automated process where the data can be sourced from multiple sources, combined, and data readily available for advanced data analysis tools.
Incremental data loading is a process where a comparison can be made between source data and target data. In a nutshell, when data is loaded to a target, at first it makes a comparison with the source system to the target system and then loads only changed data, not all data.
Related Article: Azure Data Factory vs Databricks
Following are some of the key components of the ADF and these components work together to move data from source to target and perform data transformation tasks.
The following are the key components of ADF:
In Data engineering a pipeline is a series of steps required for processing data. In a Data factory, a pipeline can contain a series of activities that accomplish a portion of the work. All these activities construct a pipeline and accomplish a required task. A typical data Pipeline consists of three elements that are source, processing, and destination. A data pipeline logically combines a group of activities and automates them for data processing.
Datasets help users to understand the data structures in a data store. Using this component you can easily point to the data you wish to use.
In simple terms, an Activity is a task or step performed in a Pipeline.
Following are the three types of Activities:
Want to become a Azure Data Factory professional and get into a high-paying profession? Check out our Expert designed industry-oriented "Azure Data Factory Training" course. This course will help you to achieve excellence in this domain.
The data Flow component is majorly used by data engineers to build data transformation logic using a graphical approach. ADF offers two types of data flows:
Mapping Data Flows in ADF allows data engineers to visually design transformation logic without writing code.
Data Wrangling allows developers to build Power Query mesh-ups that are usable in ADF data pipelines.
These are connection strings, which contain connection information that helps us ADF to connect to external Sources.
The Azure integration runtime (IR) is powerful and default compute infrastructure used by Azure Data Factory Pipelines to support integration. It acts as a bridge between ADF Linked services and Activity.
Following are the three different integration runtimes offered by ADF:
Below mentioned are the three triggers supported by ADF:
Tumbling Window Trigger: Executes Pipelines on Cycle intervals.
Schedule Trigger: Records response related to blob storage.
Event-based Trigger: Executes ADF Piplens based on timetable.
A data lake is a data repository that stores, and processes structured and semi-structured data in a secure way. It stores large volumes of data and process data irrespective of the data type & size.
It is an open-source analytics platform that simplifies the process of running big data frameworks. It enables data engineers to build optimized clusters for Spark, Kafka, LLAP, Hadoop, and Hbase on Azure.
Absolutely no. ADF comes with 90 + built-in connectors that can transform any sort of data using mapping data flow activities.
In ADF it is possible to create a trigger to run a pipeline periodically. You can create a scheduler and specify the time (start, recurring, end, etc.). The next step is to attach a targeted pipeline. We can create single or multiple triggers on a single pipeline.
Yes, It can be possible to pass parameters from the Parent pipeline to the Child pipeline. Following are the simple steps required to pass parameters:
Yes, It's possible to add default values to parameters in the pipeline.
We can create data flows using ADF V2 version.
Following are the two types of environments supported by ADF for Transformation tasks:
In ADF you can use the copy activity component to perform data copying activities between the cloud and on-premises. This component can also be used to publish data transformation and analysis for BI & application use.
Following are the file formats supported by copy activity:
Following the three major steps involved in copying data:
Yes, ADF & Synapse pipelines allow the users to incrementally load data.
Azure table storage is an essential service that is specially designed to store large volumes of structured data.
So far we have discussed some of the frequently asked azure data factory interview questions and still, there are many questions that will be added to this blog soon. Practice these questions before you attend any ADF interview & all the very best.
By Tech Solidity
Last updated on April 3, 2023