One of Microsoft’s
most powerful cloud tools today is Azure Data Factory. (also known as ADF) If
you want to expand your career in Microsoft Azure, you should also know about
Azure Data Factory. It gathers business data and processes it to create actionable
reports and information. Data Factory is an excerpt, transform and load (ETL)
service developed to automate data transformation.
In this blog, we will
look at the main Azure Data Factory
interview questions that you should prepare before your job interview. The
questions and answers here cover the basics, intermediate and advanced topics
that could be useful for beginners, experienced, and professionals to master
the interview. These questions also help in real-time azure data factory scenarios.
Below is a list of
some of the most common azure data
factory interview questions and their relevant answers. This blog also
includes azure data bricks interview
questions.
Azure Data Factory is
a comprehensively controlled cloud-based tool from Microsoft that automates
data transformation and movement. This data integration ETL service collects
raw data and transforms it into useful information. Through ADF, you can create
pipelines, which are data- and schedule-driven workflows.
Q2. What are the features of Azure Data Factory? Explain briefly.
Pipeline: Represents
all activities within a logical container.
Dataset: Datasets are
pointers to data used in channel activities.
Data Flow Mapping:
This means the data transformation UI logic.
Activity: In Data
Factory channels, this refers to the execution you can use to transform and
consume data.
Trigger: The trigger
exposes the execution time of the track.
Linked Service:
Represents the connection string for data sources used in channel activities.
Control Flow:
Regulates the flow of execution of pipeline activities.
Q3. Why do we need Azure Data Factory?
If 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 from multiple sources. Each source
transmits or routes data using different methods in many formats. When this
information needs to be transferred over the cloud or other storage platforms,
it must be effectively managed before sharing. Therefore, this raw data from
numerous sources should be cleaned, filtered, and transformed to remove
unwanted components before sharing.
Because it revolves
around data transfer, businesses should determine that data is collected from
multiple sources and stored in a shared location. You can also achieve data
storage and transformation through conventional warehouses. However, they have
some limitations. Traditional warehouses have customized applications to manage
their processes. However, it is time-consuming, and integrating each feature
can be hectic. Therefore, you need a way to automate the process or ensure that
workflows are developed appropriately. With Azure Data Factory, all these
procedures can be coordinated more conveniently.
Q4. What is the limit on the integration runtimes you can do if any?
You can perform any
integration runtime incidents in Azure Data Factory. There is no limit here.
However, there is a boundary to the number of virtual machine cores that the
integration runtime can use for each SSIS package implementation subscription.
Anyone pursuing Microsoft Azure certification should know and understand each
of these terms.
Q5. What is Azure Data Factory Integration Runtime?
Data Factory uses a
safe and secure computing infrastructure called the Integration Runtime. It
offers data integration capabilities across different environments. In
addition, it ensures that these activities are performed in areas as close as
possible to your data stores.
Q6. What type of computing environment does Azure Data Factory support?
Azure Data Factory
supports two types of computing environments, namely:
Self-built atmosphere: With the help of Azure Data Factory, you create and
monitor this computing environment yourself.
On-demand environment: It is an environment that is a fully managed add-on from
Azure Data Factory. A cluster is created to perform the transformation
activity.
Q7. What are the steps entangled in creating an ETL process in Data Factory?
Consider the case
where you try to retrieve data from an SQL database. Any data that needs to be
processed goes through processing before going to the Azure Data Lake Store,
where it is stored. The steps to create the ETL are given here.
Imagine that we are
using a dataset of automobiles. Start by creating a linked service for your SQL
Server database or any source data store.
Then you need a
linked service for your target store’s Data Lake Store.
Next, create a
dataset to store the data.
Set the channel and
then add the copy activity.
Finally, insert the
trigger and schedule your channel.
Q8. Enter two levels of security in Azure Data Lake Storage Gen2.
Azure Access Control
Lists: Specifies a data object a user can read, write, or perform. ACLs are
friendly to Linux or Unix users because they are POSIX compliant.
Azure Role-Based
Access Control: Includes various built-in Azure roles such as Contributor,
Owner, Reader, and more. It is allocated for two reasons – to determine who can
monitor the service and to enable the use of built-in data explorer tools.
Q9. What is Azure Table Storage?
Azure Table Storage
is fast and efficient storage that allows users to store structured data in the
cloud. This service offers a Keystore with designed schemes.
Q10. How many trigger types does Data Factory support?
Three trigger types
are supported in ADF. These are:
Tumbling Window Trigger: This trigger helps to trigger the ADF pipeline at cyclic
intervals. The pipe state is maintained by the rotating window trigger.
Schedule Trigger: This trigger helps manage ADF channels following a wall
clock schedule.
Event-based trigger: This trigger allows you to react to any event related to
the blob store.
Q11. How can you create Azure Functions?
Azure Function is a
solution for implementing small functional lines or code in a cloud
environment. With Azure Functions, you can choose the programming languages
??of your choice. Users only pay when the code is run for the first time, which
means a pay-per-use model is implemented. Features support languages ??like C#,
F#, Java, PHP, Python, and Node.JS. In addition, Azure Functions also support
constant integration and deployment. Businesses can design applications that
don’t require servers using Azure Functions.
Q12. Is it possible to pass pipeline run parameters?
Yes, it is possible
to pass pipeline run parameters. You can define channels and pass arguments by
starting the channel run with a trigger or on demand.
Q13. What requirements should you meet to run an ADF SSIS package?
To run an SSIS
package in Azure Data Factory, you must create an SSISDB catalog and an SSIS IR
hosted on a SQL database or Microsoft Azure-managed instance.
Q14. What is the purpose of Microsoft Azure Data Factory?
The primary goal of
Data Factory is to orchestrate data replication between multiple non-relational
and relational data sources hosted locally within enterprise data centers or
cloud platforms. Data Factory Service is also reasonable for transforming
received data and meeting business goals. In a typical Big Data solution, the Data
Factory Service plays the role of an ETL or ETL tool that enables data to
ingest.
Q15. Explain about Microsoft Azure Databricks.
Databricks is a fast,
interoperable, and accessible platform based on Apache Spark and optimized for
Microsoft Azure. Databricks was designed in collaboration with the founders of
Apache Spark. Also, Databricks combines the best features of Azure and
Databricks to help users accelerate innovation with a faster setup. Business
analysts, data scientists, and engineers can collaborate more effectively and
create an interactive workspace through these fluid workflows.
Winding Up
In this blog, we have
seen some of the top interview questions
for azure data factory.
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