Have you been trying to bag a job as Azure Data Factory professional? If your answer is yes, this is the right blog for you. Here, with the help of professionals we have curated some interview questions that will help you with the interview process.
The serverless fully managed Azure Data Factory (ADF) is a remedy for ingesting, preparing, and converting all of your data at scale. It enables all businesses across all sectors to use it for a wide range of use cases, including data engineering, operations, and maintenance data integration, analytics, intaking information into information warehouses, and more.
The very next layers of new tech, listed from the greatest abstraction level which you communicate with to the software nearest to the data, are necessary to make ADF actually work for you.
Clearly, the demand for azure data factory professionals is quite high in the market causing tremendous competition within the industry.
To make the process of learning easier, we have divided the interview questions into four categories, they are:
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At a high level, the following series of actions will help us accomplish this:
Data Factory supports the following activities: data movement, transformation, and control activities.
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The different compute environment types that Data Factory endorses for carrying out transformation activities are listed below: -
The four main steps of the ETL also called as Extract, Transform, and Load process are as follows:
The output of a query or executable execution can be returned by a look-up activity. The outcome can be a singleton value, an array of attributes, or any transition or control flow activity like the ForEach activity. These outputs can be used in a subsequent copy data activity.
Yes, in Data Factory, parameters are a first-class, top-level concept. When running the pipeline on demand or using a trigger, we can plate thickness at the pipeline level and pass arguments.
To send code to the databricks cluster, we can use the execute notebook activity. Using the baseParameters property, we can supply parameters to a notebook activity. The default values from notebook are used if the parameters are not described or specified in the activity.
The CI/CD of the data pipelines utilizing Azure DevOps and GitHub is fully supported by Data Factory. Before publishing the final product, this allows you to refine and deliver the ETL processes incrementally. Load the data into Azure Cosmos DB, Azure SQL Azure Data Lake, Azure Data Warehouse, or whatever analytics engine the company uses and can point to from one‘s business intelligence tools once the raw data has been transformed into a form that can be consumed by businesses.
[ Related Article: GitHub CI/CD Tutorial ]
The Azure Data Factory pipeline's variables offer the capacity to store the values. They are available inside the pipeline and used for the same purposes as factors in any programming language.
Setting or modifying the values of the variables can be done using the set variable and trying to add variable activities. In a data factory, there are two different kinds of variables:
In Azure Data Factory, mapping flow of data are data transformations that are visually designed. Without writing any code, data engineers can create data transformation logic using data flows. The resulting data flows are carried out in weighted Apache Spark clusters by Azure Data Factory pipelines as activities. Utilizing the scheduling, control flow, and monitoring tools already available in Azure Data Factory, data flow activities could be operationalized.
Data flow mapping offers a completely visual experience without the need for coding. Scaled-out data processing is carried out using execution clusters that are managed by ADF. All of the path optimizations, data flow job execution, and code translation are handled by Azure Data Factory.
One of the most well-liked and frequently used activities in the Azure data factory is copy. It is employed in ETL, also known as lift and shift, which is the process of moving data through one data source to another. You can transform the data as you copy it. For instance, let's say you read data from a txt/csv file with 12 columns, but you only want to keep seven columns when writing it to the target data source. It can be transformed so that only the necessary number of columns are sent to the target data source.
At a high level, the copy activity completes the following actions:
Take information out of the source data store. Work with the data to perform the following tasks:
If you've used some of the key activities in your career, whether it be your job or a college project, you can share them here. Here are some of the most popular pursuits:
A pipeline can be scheduled using either the window of time trigger or the scheduler trigger. The trigger utilizes a wall-clock calendar timetable that can schedule pipelines on a recurring basis or periodically. There are three trigger types that the service currently supports:
Trigger for tumbling windows: A trigger that keeps a state while operating at regular intervals.
Consider utilizing Data Factory:
Azure Synapse Analytics, Azure SQL Database delimited text files from such an Azure storage account, or Azure Data Lake Storage Gen2 are all supported natively as the source and sink data sources by the mapping data flow feature. Parquet files from blob storage or Data Lake Storage Gen2 are also supported. Data from all other connectors should be staged using the Copy activity before being transformed using a Data Flow activity.
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We can create a new column predicated on our desired logic by deriving transformations from the mapping data flow. When trying to generate a derived column, we have the option to add a new one or keep updating an existing one. In the Column textbox, type the name of the new column you're creating. To replace an existing column in the schema, use the column dropdown. To begin writing the expression for the derived column, click the Enter expression textbox. To create your logic, either input it or use the expression builder.
The Lookup activity in the ADF pipeline is frequently used for setup lookup needs, and the origin dataset is accessible. Additionally, it is used to extract the data from the source dataset and send it as the activity's output. The output of a lookup activity is typically utilized in the pipeline to make additional decisions or to present any resulting configuration. Simply put, the ADF pipeline uses lookup activity to fetch data. Your pipeline logic would determine how you would use it. Depending on the dataset or query, you may be able to retrieve just the first row or all of the rows.
Any data in an Azure Data Factory or Synapse pipeline can have its metadata retrieved using the Get Metadata activity. The Get Metadata activity's output can be used in conditional expressions to sample predictions or to consume the metadata in later activities. It receives a dataset as input and outputs metadata details. The following connectors are supported right now, along with the corresponding retrievable metadata. The returned metadata can only be up to 4 MB in size.
One of the most important components of any coding-related task is debugging, which is necessary to test the software for any potential bugs. It also offers the choice of debugging the pipeline without actually running it.
For instance, let's say you have a pipeline with three activities and want to focus on debugging the second action only. By setting the cut-off point at the second activity, you can achieve this. You can press the circle at the activity's top to add a breakpoint.
The main function of an ADF is to coordinate data copying among numerous relational and non-relational sources of data that are hosted locally, in data centers, or the cloud. Additionally, you can use the ADF Service to transform the information that has been ingested to meet business needs. ADF Service is utilized as an ETL or ELT tool for loading data in the majority of Big Data solutions.
[ Check out Top Open Source ETL Tools ]
The system from which the data will be used or executed is referred to as the data source. Data can be in binary, text, CSV, JSON, or any other format. It might be an appropriate database, but it could also be an image, video, or audio files.
We must specify the name of the sheet from which we must load data when using an Excel connector inside of a data factory. When dealing with data from a single or small number of sheets, this approach is nuanced. However, if we have many sheets (say, 10+), changing the hard-coded sheet name repeatedly can become tedious. To accomplish this, we can use a data factory binary data format plug and point it at the excel file without having to specify which sheet(s) to use. The copy activity will allow us to copy the data from each and every sheet in the file.
The data factory does not directly support nested looping for any looping action (for each / until). One for each and until loop activities, on the other hand, contain execute pipeline activities that may contain loop activities. In this manner, we can achieve nested looping because when we call the loop activity, it will inadvertently call another loop activity.
An effective strategy for finishing this task would've been:
Excellent data movement and transition functionalities are offered by Azure Data Factory. There are, however, some restrictions as well.
We should have installed the self-hosted assimilation runtime on the onsite machine where the SQL Server Instance is offered to host in order to copy data from an on-premises SQL Database using Azure Data Factory.
Microsoft Azure's Azure Data Factory is a fully managed, serverless, cloud-based ETL and data integration service for automating the transfer of data from its original location to, say, a data lake or centralized data using ETL also called extract-transform-load. You can use it to build and execute data pipelines that move and transform the data as well as execute scheduled pipelines.
It is a Microsoft cloud-based tool that supports the ETL and ELT paradigms and offers cloud-based information for data analytics at scale.
ADF is a service that really can orchestrate and implement processes to transform vast stores of raw business information into usable business insights, which is necessary given the growing amount of big data.
Due to the following features, Azure Data Factory differs from other ETL tools: -
A comprehensive end-to-end framework for data engineers is offered by the collection of interlinked systems that make up Data Factory. The same is summed up in the paragraph below.
In Data Factory, we can run a pipeline in one of three ways:
In Data Factory, Linked Services are primarily used for two purposes:
The computing foundation for Azure Data Factory pipelines is called the Integration Runtime, or IR. It acts as a link between various activities and associated services. It offers the computer environment in which the linked provider or activity can be dispatched or run directly. As a result, the task can be carried out in the area that is closest to the aim data stores or calculate service.
One should select an integration runtime based on their network environment requirements and data integration capabilities from the three types supported by Azure Data Factory.
[ Related Article: How to Create Package in SSIS? ]
Before we can run an SSIS package, we must first start creating an SSIS integration runtime and an SSISDB catalog hosted in the Azure SQL server database or an Azure SQL-managed instance.
Data sets, Pipelines, linked services, triggers, integration runtimes, and private endpoints, all have a default limit of 5000 in a data factory. If necessary, one can submit an online support ticket to increase the restriction to a higher number.
ADF can also perform more complicated transformations by calling webhooks, running PySpark code in Databricks, initiating a custom virtual machine, etc. Data can be delivered to a variety of locations after the movements and transformations are complete, including an outbound FTP, SQL database, and a plain file system.
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Madhuri is a Senior Content Creator at MindMajix. She has written about a range of different topics on various technologies, which include, Splunk, Tensorflow, Selenium, and CEH. She spends most of her time researching on technology, and startups. Connect with her via LinkedIn and Twitter .
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