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One of Microsoft’s most powerful cloud-based tools today is Azure Data Factory.(also known as ADF) If you want to grow your career in Microsoft Azure, you should know about Azure Data Factory as well.It collects business data and processes it to generate usable insights and information. Data Factory is an extract, transform and load (ETL) service designed to automate data transformation.
Take a look at the top Azure Data Factory interview questions, you should prepare for before a job interview. The questions and answers are given here cover the fundamentals, intermediate and advance topic which would be helpfull to ace the Interview of freshers, experienced and professionals as well.
These Azure Data Factory interview questions and their answers are prepared by industry experts who have more than 7–15 years of experience in an Azure Data Factory.
Let’s start !!!
Azure Data Factory is a fully managed, cloud-based Microsoft tool that automates the transformation and movement of data. This data integration ETL service gathers raw data and transforms it into useful information. You can create pipelines, which are data and schedule-driven workflows through ADF. ADF can also transform and process using Azure Data Lake Analytics, Spark, HDInsight Hadoop, Azure Machine learning, and more.
When you go through any Microsoft Azure tutorial, you will find Data Factory mentioned in all of them. In today’s data-driven world, data flows in through multiple sources in various forms. Each source transfers or channelises data using different methods in multiple formats. When this information needs to be conveyed over a cloud or other storage platforms, it needs to be managed efficiently before being shared. Therefore, this raw data from multiple sources should be cleaned, filtered and transformed before sharing, removing unwanted components.
Since this revolves around transferring data, enterprises should determine that the data is gathered from multiple sources and stored in a common location. You can achieve data storage and transformation through conventional warehouses as well. However, they have certain limitations. Conventional warehouses have customised applications for managing their processes. But this is a time-consuming process, and integrating every process can be a hectic task. Therefore, what you need is a way to automate the process or ensure workflows are designed appropriately. With Azure Data Factory, all these processes can be coordinated more conveniently.
You can perform any number of integration runtime incidents in the Azure Data Factory. There is no limit here. However, there is a limit on how many VM cores can be utilised by the integration runtime for each SSIS package implementation subscription. Anyone pursuing a Microsoft Azure certification at any level should know and understand each of these terms.
Data Factory uses a safe and secure infrastructure for computing known as Integration Runtime. It offers data integration abilities across multiple environments. Additionally, it ensures that these activities are implemented in areas as close to your data stores as possible. This term is a fundamental part of any Microsoft Azure certification you train for, so make sure you’re aware of such terminologies.
One of the most fundamental components while learning Microsoft Azure is Blob Storage. Microsoft Azure Blob Storage is instrumental for enterprises looking to store large volumes of unstructured data such as text or binary data. Enterprises can also use blob storage for rendering data to outsiders or to confidentially save app data. Some of the primary applications of blob storage are as follows:
Consider an instance where you attempt to retrieve any data from an SQL database. If any data has to be processed, it goes through processing before going to Azure Data Lake Store, where it is saved. The steps to create ETL are given here.
There are three trigger types supported in ADF. These are:
Tumbling Window Trigger: This trigger helps execute ADF pipelines over cyclic intervals. A tumbling window trigger maintains the state of the pipeline.
Schedule Trigger: This trigger helps execute ADF pipelines that follow the wall clock timetable.
Event-based Trigger: This trigger enables a response to any event related to blob storage. Examples of events could be when you add or delete blob storage.
An Azure Function is a solution to implement small function lines or code within a cloud environment. Using Azure Functions, you can choose programming languages of your choice. Users only pay for the first time they run the code, meaning a pay-per-use model is implemented. Functions support a diverse range of languages such as C#, F#, Java, PHP, Python and Node.JS. Additionally, Azure Functions also support consistent integration and deployment. Businesses can develop applications that don’t need servers by using the applications of Azure Functions.
In its current version, the MDF functionality (Mapping Data Flow) permits SQL Data Warehouse, SQL Database, and Parquet and text files stored in Azure Blob Storage and Data Lake Storage Gen2 natively for source and sink. You can use the Copy Activity functionality to access data from any supplementary connector. After this, you must also run an Azure Data Flow activity to transform the data efficiently after the staging is complete.
To execute an SSIS package in Azure Data Factory, you must create an SSISDB catalogue and SSIS IR, hosted in Microsoft Azure’s SQL Database or Managed Instance.
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A dataset refers to data you can use in potential pipeline activities as outputs or inputs. Typically, a dataset is the structure of the data within linked stores of data such as files, documents and folders. For example, a Microsoft Azure Blob Storage dataset can describe a folder and the container within Blob Storage from which particular pipeline activities should read data as processing input.
The primary objective of Data Factory is organising data copying among multiple non-relational and relational sources of data locally hosted within enterprise data centres or cloud platforms. Additionally, Data Factory Service is also useful for transforming ingested data and fulfilling business objectives. In a typical Big Data solution, Data Factory Service plays the role of an ETL or ETL tool that allows data ingestion.
Mapping data flow activities refer to visually designed data transformation. Mapping data flows allow you to graphically design data transformation logic effectively when an expert developer is not present. Additionally, mapping data flows are operated as activities within the Data Factory pipeline on fully managed Data Factory Spark clusters.
Wrangling data flow activities denote data preparation activities that don’t require code. A wrangling data flow can integrate with Power Query Online to enable access to Power Query M functions to wrangle data via spark implementation.
Databricks refer to a quick, mutual and easy platform based on Apache Spark and optimised for Microsoft Azure. Databricks has been designed in collaboration with the Apache Spark founders. Additionally, Databricks combines the best features of Azure and Databricks to help users accelerate innovation through a faster setup. Business analysts, data scientists and data engineers can work together more easily through these smooth workflows, making for an interactive workspace.
SQL Data Warehouse is an enormous data store of information collected through a wide range of sources in an organisation and facilitates data-driven decision making. These data warehouses allow you to gather data from various databases that exist as distributed or remote systems.
Azure SQL Data Warehouses can be built through the integration of analytical reporting, multiple source data etc. To put it simply, SQL Data Warehouse is an enterprise-level cloud app that allows businesses to process complex queries rapidly from large volumes of raw data. It also functions as a Big Data concept solution.
Data comes from many different sources; thus, the amount of data gathered can be overwhelming. In such cases, we need Azure Data Factory to carry out the process of storing data and transforming it in an efficient and organised way.
Traditional data warehouses can carry out this process. However, there can be many disadvantages to this process.
Data comes in many different formats from different sources, and processing and transforming this data needs to be structured.
Yes, it is possible to define default values for pipeline parameters.
You should use the @coalesce construct to handle null values in an activity output efficiently.
The time window or the schedule trigger is used to schedule a pipeline. Pipelines can be scheduled periodically or in calendar-based recurring patterns.
Also Read: A Comprehensive Guide To Microsoft Azure Data Scientist Associate Certification
Using the @activity constrict, an activity can be adequately consumed in a subsequent activity.
Yes, it is possible to pass parameters to a pipeline run. You can define pipelines and pass arguments by executing the pipeline run using a trigger or on-demand.
Data Flows are created in the Azure Data Factory V2 version.
For Azure Data Factory, coding knowledge is not required as ADF provides more than 90 built-in connectors to transform data.
Azure table storage is rapid and efficient storage that allows users to store structured data in the cloud. This service offers a Keystore with designed schemas.
Azure Data Factory supports two types of computer environments, namely:
These are some of the most frequently asked ADF interview questions. To get a holistic understanding of ADF and give your career the boost it deserves, enrols in a training course on Koenig.
Archer Charles has top education industry knowledge with 4 years of experience. Being a passionate blogger also does blogging on the technology niche.
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