Top 30 Apache NiFi Interview Questions with Answers

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Here are 30 interview questions commonly asked in Apache NiFi interviews, along with their answers:

1. What is Apache NiFi?

Apache NiFi is an open-source data integration tool that provides a visual interface for designing, building, and managing data flows. It allows users to process and distribute data between various systems and components.

2. What are the key features of Apache NiFi?

Key features of Apache NiFi include data ingestion, data routing, data transformation, data provenance, data security, and scalability. It also provides a web-based user interface for designing and monitoring data flows.

3. What are the core components of Apache NiFi?

The core components of Apache NiFi are Processor, Connection, FlowFile, Flow Controller, and Controller Services.

4. Explain the concept of FlowFile in Apache NiFi.

A FlowFile represents a piece of data in Apache NiFi. It encapsulates the data content and its metadata, allowing it to flow through the NiFi system and be processed by various processors.

5. What is a Processor in Apache NiFi?

A Processor in Apache NiFi is a fundamental component that performs a specific operation on incoming FlowFiles. Processors can be configured to perform tasks such as data transformation, data enrichment, data routing, and data filtering.

6. How can you connect processors in Apache NiFi?

    Processors can be connected using Connections in Apache NiFi. Connections define the flow of data between processors and specify the relationship between them, such as one-to-one, one-to-many, or many-to-one.

    7. What is the purpose of a Controller Service in Apache NiFi?

    Controller Services in Apache NiFi provide shared resources and services that can be used by multiple processors. They can be used to configure database connections, security credentials, or other resources required by processors.

    8. How can you route data based on conditions in Apache NiFi?

    Apache NiFi provides processors like RouteOnAttribute and RouteOnContent, which allow you to route data based on conditions specified in attributes or content of the FlowFiles.

    9. Explain the concept of data provenance in Apache NiFi.

    Data provenance in Apache NiFi refers to the ability to track and trace the lineage of data as it flows through the system. It provides visibility into the origin, transformations, and destinations of data within the data flow.

    10. What is the purpose of a Flow Controller in Apache NiFi?

    The Flow Controller in Apache NiFi is responsible for managing and coordinating the execution of data flows. It handles the scheduling, distribution, and load balancing of FlowFiles across the processors.

    11. How can you handle data security in Apache NiFi?

    Apache NiFi provides various security features, including user authentication, role-based access control, encrypted communication, and data encryption. These features ensure secure data processing and access control.

    12. How can you handle data compression in Apache NiFi?

    Apache NiFi provides processors such as CompressContent and DecompressContent to handle data compression and decompression. These processors support various compression formats like gzip, zip, and bzip2.

    13. Can Apache NiFi process real-time streaming data?

    Yes, Apache NiFi can process real-time streaming data. It supports data ingestion from various sources, including Kafka, MQTT, and JMS, and provides processors for real-time data processing and analysis.

    14. How can you monitor and manage data flows in Apache NiFi?

    Apache NiFi provides a web-based user interface called the NiFi UI, which allows you to monitor the status of data flows, track data provenance, view logs, and manage components such as processors and connections.

    15. What Is The NiFi Template?

    A template is a workflow that may be reused, which you may import and export across many NiFi instances. It can save a lot of time compared to generating Flow repeatedly. The template is produced in the form of an XML file.

    16. What does the term “Provenance Data” signify in NiFi?

    NiFi maintains a Data provenance library that contains all information about the FlowFile. As data continues to flow and is converted, redirected, divided, consolidated, and sent to various endpoints, all of this metadata is recorded in NiFi’s Provenance Repository. Users can conduct a search for the processing of every single FlowFile.

    17. What is a FlowFile’s “lineageStartDate”?

    This FlowFile property indicates the date and time the FlowFile was added or generated in the NiFi system. Even if a FlowFile is copied, combined, or divided, a child FlowFile may be generated. However, the lineageStartDate property will provide the timestamp for the ancestor FlowFile.

    18. How to get data from a FlowFile’s attributes?

    Numerous algorithms are available, including ExtractText, and EvaluateXQuery which can help you get data from the FlowFile attribute. Furthermore, you may design your own customized microprocessor to meet the same criteria if no off-the-shelf processor is provided.

    19. What occurs to the ControllerService when a DataFlow is used to generate a template?

    When a template is produced via DataFlow and if it has an associated ControllerService, a new instance of the control system service will be generated throughout the import process.

    20. What occurs if you save a passcode in a DataFlow and use it to generate a template?

    A password is a very sensitive piece of information. As a result, when publishing the DataFlow as templates, the password is removed. Once you export the template into another NiFi system, whether the same or a different one, you must enter the password once more.

    21 . What is the component of the flow file?

    A FlowFile is formed from two parts.

    Content. The content may be a stream of bytes that contains a pointer to the particular data being processed within the data flow and is transported from source to destination. confine mind flow file itself doesn’t contain the info, rather it’s a pointer to the content data. the particular content is going to be within the Content Repository of NiFi.
    Attributes. The attributes are key-value pairs that are related to the info and act because of the metadata for the flow file. These attributes generally want to store values that actually provide context to the info. a number of the samples of attributes are filename, UUID,
    MIME Type, Flowfile creating time, etc.

    22. What’s Bulleting and the way it helps in NiFi?

    If you would like to understand if any problems occur during a data flow. you’ll sign up the logs for love or money interesting, it’s far more convenient to possess notifications crop up on the screen. If a Processor logs anything as a WARNING or ERROR, we’ll see a “Bulletin Indicator” show up within the top-right-hand corner of the Processor.
    This indicator seems like a sticky note and can be shown for five minutes after the event occurs. Hovering over the bulletin provides information about what happened in order that the user doesn’t need to sift through log messages to seek out it. If during a cluster, the bulletin also will indicate which node within the cluster emitted the bulletin. we will also change the log level at which bulletins will occur within the Settings tab of the Configure dialog for a Processor.

    23 . What’s the role of Apache NiFi in the Big Data Ecosystem?

    The main roles Apache NiFi is suitable for in Big Data Ecosystem are.

    • Data acquisition and delivery.
    • Transformations of knowledge.
    • Routing data from different sources to the destination.
    • Event processing.
    • End-to-finish provenance.
    • Edge intelligence and bi-directional communication.

    24 . What’s a processor?

    NiFi processors are the building block and most ordinarily used components in NiFi. Processors are the blocks that we drag and drop on the canvas and data flows are made from multiple processors. Processors are often used for bringing data into the system like GetHTTPS, GetFile, ConsumeKafka, etc. or are often used for performing some quiet data transformation or enrichment, for instance, SplitJSON, ConvertAvroToOrc, ReplaceText, ExecuteScript, etc.

    25. How does NiFi Support a Huge Volume of Payload during a Dataflow?

    The huge volume of knowledge can transit from DataFlow. As data moves through NiFi, a pointer to the info is being passed around, mentioned as a flow file. The content of the flow file is merely accessed as required.

    25. What is the distinction between NiFi’s FlowFile and Content repositories?

    The FlowFile Library is where NiFi stores information about a particular FlowFile that is currently online in the stream.

    The Content Repository stores the exact bytes of a FlowFile’s information.

    26. What does “deadlock in backpressure” imply?

    Assume you’re using a processor, such as PublishJMS, to release the information to the target list. The destination queue, on the other hand, is full, and your FlowFile will be sent to the failed relationship. And when you retry the unsuccessful FlowFile, the incoming backpressure linkage becomes full, which might result in a backpressure stalemate.

    27. What is the remedy for the “back pressure deadlock”?

    There are several alternatives, including

    The administrator can temporarily boost the failed connection’s backpressure level.
    Another option to explore in this scenario is to have Reporting Tasks monitor the flow for big queues.

    28. How does NiFi ensure the delivery of messages?

    This is accomplished by implementing an effective permanent write-ahead logging and information repository.

    29. Can you utilize the fixed Ranger setup on the HDP to work with HDF?

    Yes, you may handle HDF with a single Ranger deployed on the HDP. Nevertheless, the Ranger that comes with HDP doesn’t include NiFi service definition, and so must be installed manually.

    30. Is NiFi capable of functioning as a master-slave design?

    No, starting with NiFi 1.0, the 0-master principle is taken into account. Furthermore, each unit in the NiFi network is identical. The Zookeeper service manages the NiFi cluster. Apache ZooKeeper appoints a single point as the Cluster Administrator, and ZooKeeper handles redundancy seamlessly.

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