Wednesday, September 28, 2022

Big Data Analytics Interview Questions

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Questions About Experience And Background With Big Data

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Often, interviewers want to know what type of experience you have and to learn more about your background, especially as it relates to working with big data. Here are some of the common questions an interviewer might ask you to learn more about your big data experience:

  • Do you have experience with big data?

  • How much experience do you have with Hadoop?

  • Why is Hadoop associated with big data?

  • What do you feel are the most useful features of Hadoop?

  • Do you have experience selecting the optimal hardware configuration for Hadoop?

  • How do you manage data preparation?

  • Please tell me about the different Hadoop daemons.

  • What would you tell a colleague when they have difficulty accessing a file in the HDFS?

  • Have you used HBase with Flume and, if so, what was that process like?

  • Do you have experience with HBase?

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What Is Meant By Data Governance

The overall data management including its availability, integrity, usability, security, etc. is termed as data governance. For effective data governance, there should be a council about data governance, well-defined procedures and an effective plan for the implementation of those procedures and practices.

When it is ensured that there is an integrity of the given data and also it is trustworthy, we get the expected business benefits out of that data. As businesses depend more and more on data for making business decisions, data governance becomes very important and more critical.

A uniform and consistent data access across different business applications should be ensured. A team of data governance is responsible for implementing these policies and defined procedures regarding the handling of data. This team may include data managers, business managers and other staff related to the handling of data. Some Associations are dedicated to the promotion of best practices in data governance. These are:

  • Data Governance Institute
  • Data Management Association
  • Data Governance Professionals Organization etc.
  • There can be many use cases where we see the data governance can play a crucial role. Some of these use cases can be as enlisted below:

  • Mergers and Acquisitions,
  • Some various strategies and steps need to be incorporated to have good data governance in place.

  • You need to decide the data ownership.
  • Define the policies regarding data storage, availability, backup, security, etc.
  • Q9 Can You Tell How To Create Stories In Tableau

    Stories are used to narrate a sequence of events or make a business use-case. The Tableau Dashboard provides various options to create a story. Each story point can be based on a different view or dashboard, or the entire story can be based on the same visualization, just seen at different stages, with different marks filtered and annotations added.

    To create a story in Tableau you can follow the below steps:

    • Click the New Story tab.
    • In the lower-left corner of the screen, choose a size for your story. Choose from one of the predefined sizes, or set a custom size, in pixels.
    • By default, your story gets its title from its sheet name. To edit it, double-click the title. You can also change your titles font, color, and alignment. Click Apply to view your changes.
    • To start building your story, drag a sheet from the Story tab on the left and drop it into the center of the view.
    • Click Add a caption to summarize the story point.
    • To highlight a key takeaway for your viewers, drag a text object over to the story worksheet and type your comment.
    • To further highlight the main idea of this story point, you can change a filter or sort on a field in the view, then save your changes by clicking Update above the navigator box.

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    Big Data Analyst Interview Questions

    As a big data analyst, you will be responsible for analyzing large data sets to identify patterns and trends. In your job interview, you will be expected to demonstrate your analytical skills and knowledge of big data tools and techniques.

    Before you continue

    As a big data analyst, you will be responsible for analyzing large data sets to identify patterns and trends. In your job interview, you will be expected to demonstrate your analytical skills and knowledge of big data tools and techniques. Be prepared to discuss your experience working with big data and to identify some of the challenges you have faced. The interviewer will also want to know how you have used data to improve decision making in your organization.

    Q9 If You Are Given An Unsorted Data Set How Will You Read The Last Observation To A New Dataset

    Interview Questions Big Data Analytics

    We can read the last observation to a new dataset using end = dataset option.

    For example:

    data example.newdataset set example.olddataset end=last If last run 

    Where newdataset is a new data set to be created and olddataset is the existing data set. last is the temporary variable which is set to 1 when the set statement reads the last observation.

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    In What All Modes Can Hadoop Be Run

    Hadoop can be run in three modes:

    • Standalone Mode: The default mode of Hadoop, standalone mode uses a local file system for input and output operations. This mode is mainly used for debugging purposes, and it does not support the use of HDFS. Further, in this mode, there is no custom configuration required for mapred-site.xml, core-site.xml, and hdfs-site.xml files. This mode works much faster when compared to other modes.
    • Pseudo-distributed Mode : In the case of pseudo-distributed mode, you need the configuration for all the three files mentioned above. All daemons are running on one node thus, both master and slave nodes are the same.
    • Fully distributed mode : This is the production phase of Hadoop, what it is known for, where data is used and distributed across several nodes on a Hadoop cluster. Separate nodes are allotted as master and slave nodes.

    Career Transition

    What Do You Mean By Data Stewardship

    Data stewardship means owning accountability regarding data availability, accessibility, accuracy, consistency, etc. To ensure data stewardship, a team of different people is formed. This team includes data managers, data engineers, business analysts, policymakers, etc. A Data Steward is responsible for data proficiency and the management of an organization’s data. He is also expected to critically handle almost all the things that are related to data policies, processing, data governance and look over the organizations information assets in compliance with the different policies and the other regulatory obligations.

    A Data Steward is supposed to answer the following questions:

  • What is the importance of this particular data to an organization?
  • How long the data should be stored?
  • What could be the improvements in the quality of the data insights?
  • He also looks after the data protection, data authorization and access depending upon the defined roles. Any breach would be immediately noted and brought to the notice of the management. He has to ensure that the practices regarding data retention, archival as well as disposal requirements have complied with the organizational policies and the various regulations in place. While ensuring transparency, he also has to check that the data privacy and security is not breached. A Data Steward should ensure the quality of data and should take different measures to keep it intact in consultation with the various stakeholders.

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    What Are The Factors Or Issues To Be Considered While Building Big Data Models

    Special emphasis needs to be given when building Big Data Models. It is so because the Big Data itself is less predictable when compared to the other traditional kind of data. It is a little bit complex process as it involves reorganizing and rearranging the business data by the business processes.

    To support the business objectives, the data models need to be designed to have logical inter-relationships among the various business data.

    Then these logical designs need to be translated into the corresponding physical models.

    Big Data is significantly different than the traditional data, the old data modelling techniques do no longer apply to Big Data. So you are required to apply different approaches for modelling Big Data.

    The data interfaces should be designed to incorporate elasticity and openness due to the unpredictable nature of the Big Data to accommodate future changes.

    Here the focus should not be on a schema but on designing a system. We should also take into consideration the various Big Data modeling tools out there. Not all the Big Data present there should be considered for modeling. Only the data appropriate to your business concerns should be selected to build models around.

    How Do You Treat Outliers In A Dataset

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    An outlier is a data point that is distant from other similar points. They may be due to variability in the measurement or may indicate experimental errors.

    The graph depicted below shows there are three outliers in the dataset.

    To deal with outliers, you can use the following four methods:

    • Drop the outlier records
    • Try a new transformation

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    How Do I Prepare For A Data Science Interview

    As you would for any other technical interview make sure that youve got the basics down, and can execute ideas in code. Of course, you should also present a good resume and be prepared to summarize past experiences.

    On a more general note, you should also research the company and the specific role youre applying for. You want to ask questions about the software and the company itself, as it serves to highlight your enthusiasm for the role. It may also be worth looking at reviews on Glassdoor to get a sense of the company and past employees experiences.

    How Would You Go About A Data Analytics Project

    A candidate must know the five key steps to an analytics project:

    • Data Exploration: Identify the core business problem. Identify the potential data dimensions that are impactful. Set up databases to collect âBig dataâ from all such sources.
    • Data Preparation: Using queries and tools, begin to extract the data and look for outliers. Drop them from the primary data set as they represent abnormalities that are difficult to model/predict.
    • Data Modelling: Next start preparing a data model. Tools such as SPSS, R, SAS, or even MS Excel may be used. Various regression models and statistical techniques need to be explored to come up with a plausible model.
    • Validation: Once a rough model is in place, use some of the later data to test it. Modifications may be made accordingly.
    • Implementation & Tracking: Finally, the validated model needs to be deployed through processes & systems. Ongoing monitoring is required to check for deviations so that further refinements may be made.

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    What Is The Reason Behind Using Hadoop In Big Data Analytics

    Businesses generate a lot of data in a single day and the data generated is unstructured in nature. Data analysis with unstructured data is difficult as it renders traditional big data solutions ineffective. Hadoop comes into the picture when the data is complex, large and especially unstructured. Hadoop is important in Big Data analytics because of its characteristics:

    • Data storage
    • Collection plus extraction of data

    Difference Between Point Estimates And Confidence Interval

    100+ Commonly Asked Data Science Interview Questions

    Confidence Interval: A range of values likely containing the population parameter is given by the confidence interval. Further, it even tells us how likely that particular interval can contain the population parameter. The Confidence Coefficient is denoted by 1-alpha, which gives the probability or likeness. The level of significance is given by alpha.

    Point Estimates: An estimate of the population parameter is given by a particular value called the point estimate. Some popular methods used to derive Population Parameters Point estimators are – Maximum Likelihood estimator and the Method of Moments.

    To conclude, the bias and variance are inversely proportional to each other, i.e., an increase in bias results in a decrease in the variance, and an increase in variance results in a decrease in bias.

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    Data Analytics Interview Questions

    Data analytics is an essential practice used in many industries, and understanding the best ways to answer analytics questions during an interview can help you secure such jobs. As a data analytics professional, such as a data analyst or data scientist, you help organizations make significant business decisions.

    To prove you are the best person for the role, you need to impress interviewers by showcasing your technical analytics knowledge and relevant experiences. In this article, we list general, background and in-depth interview questionsalong with example answersto help you prepare for an interview as a data analytics professional.

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    How Are Missing Values Handled In Big Data

    Missing values refer to the values that are not present for a particular column. If we do not take care of the missing values, it may lead to erroneous data and in turn incorrect results. So before processing the Big Data, we are required to properly treat the missing values so that we get the correct sample. There are various ways to handle missing values.

    We can either drop the data or decide to replace them with the data imputation.

    If the number of missing values is small, then the general practice is to leave it. If the number of cases is more then the data imputation is done.

    There are certain techniques in statistics to estimate the so-called missing values:

  • Regression
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    What Is The Use Of Recordreader In Hadoop

    Though InputSplit defines a slice of work, it does not describe how to access it. This is where the RecordReader class comes into the picture it takes the byte-oriented data from its source and converts it into record-oriented key-value pairs such that it is fit for the Mapper task to read it. Meanwhile, InputFormat defines this Hadoop RecordReader instance.

    How Is Business Revenue Linked With Big Data

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    Nowadays Big Data has become a business norm. One can not continue in the business and remain competitive by neglecting Big Data. Big Data offers you the insights which otherwise you may not be able to discover.

    These insights help you to decide your inventory management, production, marketing, service offerings, etc. which are directly related to business revenue. Big Data helps you to increase the efficiency at every stage of a business and thus, in turn, reduces the overall expenses making you more competitive and profitable.

    To increase business revenue, you have various options such as:

  • Increase sales
  • Increase efficiency etc.
  • Increasing sales is not an easy task. It depends on the market demands and customer

    Preferences. How will you come to know about market demands and what does the customer want? You can get the proper answers for such questions by analyzing the Big Data. Big Data contains valuable information and insights that need to be discovered and utilized as per your requirements. By analyzing Big Data, you can get various patterns, trends, customer insights, etc. Such insights will assist you in formulating your business strategies accordingly and increase the chances of customer conversion and ultimately increase your revenues.

    Thus by harnessing the inherent potential of Big Data, you can increase efficiency, reduce costs and in turn increase revenues and the overall business growth.

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    Hadoop Hdfs Interview Questions And Answers

    1. What is a block and block scanner in HDFS?

    Block – The minimum amount of data that can be read or written is generally referred to as a block in HDFS. The de

    size of a block in HDFS is 64MB.

    Block Scanner – Block Scanner tracks the list of blocks present on a DataNode and verifies them to find any kind of checksum errors. Block Scanners use a throttling mechanism to reserve disk bandwidth on the datanode.

    2. Explain the difference between NameNode, Backup Node and Checkpoint NameNode.

    NameNode: NameNode is at the heart of the HDFS file system which manages the metadata i.e. the data of the files is not stored on the NameNode but rather it has the directory tree of all the files present in the HDFS file system on a hadoop cluster. NameNode uses two files for the namespace-

    fsimage file- It keeps track of the latest checkpoint of the namespace.

    edits file-It is a log of changes that have been made to the namespace since checkpoint.

    Checkpoint Node-

    Checkpoint Node keeps track of the latest checkpoint in a directory that has same structure as that of NameNodes directory. Checkpoint node creates checkpoints for the namespace at regular intervals by downloading the edits and fsimage file from the NameNode and merging it locally. The new image is then again updated back to the active NameNode.

    BackupNode:

    3. What is commodity hardware?

    4. What is the port number for NameNode, Task Tracker and Job Tracker?

    NameNode 50070

    Task Tracker 50060

    HFS High Availability

    How Do You Convert Unstructured Data To Structured Data

    An open-ended question and there are many ways to achieve this.

    • Programming: Coding/ Programming is the most tried out method to transform unstructured data into a structured form. Programming is advantageous to accomplish because we get independence with it, which you can use to change the structure of the data in any form possible. Several programming languages, such as Python, Java, etc., can be used.
    • Data/Business Tools: Many BI tools support the drag and drop functionality for converting unstructured data into structured data. One cautious thing before using BI tools is that most of these tools are paid, and we have to be financially capable to support these tools. For people who lack both experience and skills needed for option 1, this is the way to go.

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    Data Analyst Interview Questions: Statistics

    Statistics is a branch of mathematics dealing with data collection and organization, analysis, interpretation, and presentation. Statistics can be divided into two categories: Differential and Inferential Statistics. This field is related to mathematics and thus gives a kickstart to Data Analysis career.

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