9 Questions in a “Big Data” Interview

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9 Questions in a "Big Data" Interview

 1. Where does Big Data come from, and what is it? What is the procedure?

Big Data describes data sets that are so large and complex that they cannot be effectively managed by traditional methods. Videos, photos, audio files, websites, and other forms of multimedia content are all part of the Big Data lexicon.
There are countless methods by which businesses can gather the information they require.

  • Internet cookies
  • Email tracking
  • Smartphones
  • Smartwatches
  • Formats for Making Financial Transactions Online
  • Website interactions
  • Transaction histories
  • Social media posts
  • Companies that collect and resell information about customers and other profitable demographics are referred to as “third-party trackers.”

Big data work can be broken down into three categories of tasks:

  • The term “integration” refers to the process of bringing together disparate datasets and shaping them into a form suitable for analysis and the gleaning of insights.
  • Management: Big data should be collected and kept in a central location. Large portions of Big Data are unstructured, making them inappropriate for traditional relational databases. These systems are designed to store information in a tabular, row-by-column format.
  • With Big Data, businesses can gain valuable market insights, such as information on consumer preferences and shopping habits. You can see these in action when you analyze massive data sets with AI/ML-powered analysis tools.

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2. What are the 5 V’s in Big Data?

  • Large amounts of information are being generated and stored in data warehouses, which reflects the volume. There’s a chance the data will reach arbitrary heights, and that means processing and analyzing massive amounts of information. Which could go beyond terabytes and petabytes if they exist.
  • The term “velocity” is used to describe how quickly new information is generated in real time. Imagine the volume of posts made to social media platforms like Facebook, Instagram, and Twitter every second, hour, or more.
  • Big Data is a collection of data in a wide range of formats, including structured, unstructured, and semi-structured information. Since this data is so dissimilar, it calls for specialized methods of analysis and processing that make use of tailored algorithms.
  • Data veracity refers to how trustworthy the information is, or, in a nutshell, the quality of the data used in an analysis.
  • Value: Data has no value until it is processed and analyzed. From it, we can glean useful data.

3. The strategic use of Big Data in business.

Data is now an indispensable resource for companies of all sizes and in all industries. In order to stay ahead of the competition, many businesses today rely on big data analytics.
Part of the big data process is verifying the company’s collected datasets. Experts in the field of big data must also be aware of what the business expects from the software and how the information will be put to use.

here are some steps:

  • Analytics’ primary focus is improving the decision-making process, and big data continues to provide the foundation for this. With so much information at their disposal, businesses can make more informed decisions more quickly with the help of big data. In today’s fast-paced society, it is crucial for businesses to be able to adapt quickly to new market conditions and operational shifts.
  • Companies can now exercise fine-grained control over their assets thanks to the availability of detailed information made possible by the advent of big data. That means they can increase output, decrease the need for repairs, and lengthen the lifespan of critical assets, depending on the data source. This provides an edge over the competition by ensuring that the business is making the most of its resources while also reducing expenses.
  • Big data can help companies cut costs by providing insights into where savings can be made. Businesses can use the information they collect to pinpoint areas where they can save money without negatively impacting operations, such as by analyzing energy consumption or evaluating the efficiency of staff operating patterns.
  • Customers are more likely to participate in surveys when they feel comfortable sharing information about their preferences and behaviors online. This can lead to more in-depth analyses and, ultimately, more sales. Through the use of collected data, businesses are better able to cater their offerings to the individual needs of their customers, while also providing the individualized service that many modern consumers have come to expect.
  • Analytics can also help businesses in finding additional avenues for revenue generation and growth. For instance, businesses can determine a course of action based on an understanding of customer preferences and behavior. It’s likely that companies can sell the data they collect, opening up new avenues of profit and partnerships.

4. How is Hadoop and Big Data related?

In any discussion of “Big Data,” Hadoop inevitably comes up. So, from the point of view of an interview, this is a very important question. Which you will probably encounter. Hadoop is a free and open-source software framework for storing, processing, and analyzing large, unstructured data sets for the purpose of learning new things. As you can see, the connection between Hadoop and Big Data works like this.

5. Defend Hadoop’s function in Big Data analytics.

Due to the large volume of structured, semi-structured, and unstructured data that makes up big data, processing and analyzing it can be a challenging endeavor. Some kind of technological aid was required to speedily process the data. With its storage and processing capacities, Hadoop is used for this reason. Hadoop, moreover, is free and accessible to the public. Business solutions can benefit from this in terms of cost.

This framework has gained a lot of traction in recent years due to the fact that it facilitates the distributed processing of massive data sets by means of ad hoc computer clusters that execute simple programming models.

6. Break down what Hadoop’s primary features are.

Hadoop is a free software project that was designed to process and store massive amounts of data in a decentralized fashion.

The Backbone of Hadoop:

  • Hadoop’s primary data storage mechanism is the Hadoop Distributed File System (HDFS). HDFS is used to house all of the massive amounts of information. Its primary intent is to accommodate large datasets in inexpensive hardware.
  • MapReduce in Hadoop is the layer in charge of processing data. In HDFS, it submits a request to process both structured and unstructured data. By breaking up the processing of the data into separate tasks, it is responsible for the parallel processing of a large amount of data. Specifically, there are two phases of processing: the Map phase and the Reduce phase. A data block is read at the Map stage and made available to the executors (computers/nodes/containers) for processing. When at the reduce stage, all processed data is compiled into a single set.
  • Hadoop’s processing framework is known as YARN. YARN manages resources and offers various data processing engines, including real-time streaming, data science, and batch processing.

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7. Explain the features of Hadoop.

Hadoop assists in not only store data but also processing big data. It’s the safest way to deal with complex data problems. Some salient features of Hadoop are 

  • Distributed Processing – Hadoop aids in the distributed processing of data, which results in faster processing times. Hadoop HDFS uses distributed file systems to store its data and the MapReduce framework for its parallel data processing.
  • Open Source – Hadoop is a free framework because it is open-source. The source code can be modified to fit the needs of the end user.
  • Fault Tolerance – Hadoop can handle errors very well. It makes three copies of every block, spread across different nodes. This replication factor is flexible and can be adjusted to meet specific needs. If one of the nodes fails, we can still access the data by using another. After a node fails, the system will automatically find it and restore the lost information.
  • Scalability –  It has different hardware, and we can start using the new one right away.
  • Reliability – Hadoop’s data is stored in a secure, independent fashion on the cluster. Thus, Hadoop ecosystem data is fault-tolerant and unaffected by hardware failures.

8. How is HDFS different from traditional NFS?

NFS (Network File system): A communication standard for granting remote users access to stored data. Files stored on the disk of a remote device can be accessed with the same ease by NFS clients as if they were stored locally.

HDFS (Hadoop Distributed File System): When multiple computers, or nodes, are connected via a network, they can use a system called a distributed file system to store and access data. HDFS is reliable because it stores multiple replicas of files in the file system, with replication set to level 3 by default.

The key distinction is in how they handle failure and replication. HDFS is built to be resilient in the face of disruptions. Because of this, NFS cannot tolerate errors of any kind.

Benefits of HDFS over NFS:HDFS also facilitates the creation of multiple copies of files, which helps with fault tolerance. This alleviates the common problem of multiple clients trying to access the same file at the same time. More so than with NFS, reading performance scales better due to files having multiple images on different physical disks.

9. What is data modelling and what is the need for it.

The IT industry has used data modeling as a business for decades. The data model is a method for acquiring intimate familiarity with the data and thus arriving at the diagram. Businesses and IT experts are more likely to grasp the data and its potential applications when presented with it in a visual format.

Models for Data, Different Types

Conceptual models, logical models, and physical models are the three most common types of data representations. Imagine them as a step up from an abstract design to a detailed road map of the database’s infrastructure and final shape.

  • Conceptual Data Model:
    The most basic and generalized model of data is the conceptual one. The model undergoes minimal annotation, but the structure and controls of the data relationships are established. Included are such things as the fundamental market rules that must be followed, the depths or entity classes of data that you intend to cover, and any other regulations that may restrict your layout choices. When a project is still in its early stages, data models are useful.
  • Logical Data Model:
    The logical data model adds to the conceptual model’s framework, but with a greater emphasis on relationships. Thus, many common annotations focus on broad properties or attributes of data rather than individual data elements. Data warehousing initiatives can benefit from this model as a result.
  • Physical Data Model:
    The physical data model is the final step prior to database production, and it typically takes into account properties and rules unique to the database management system being used.

Advantages Of Data Modeling:

  • When it comes to managing their data, businesses can reap a number of advantages from adopting a data-modeling strategy.
  • Foreseeing the best course of action requires cleaning, organizing, and modeling your data before you even consider building a database. As data modeling improves data quality, databases become more constrained, error-prone, and poorly designed.
  • In data modeling, you can see the data flow and the structure you’ve imagined for the data. This helps staff members understand data activity and their role in the bigger picture of data management. It also improves the flow of information between teams within a company.
  • The deeper database design made possible by data modeling leads to more advanced applications and data-driven business insights.