Big Data Interview Questions

Big Data Interview Questions

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Big data interview questions, Big data analysis, , big data genre, big data datasets, big data blog, big data k-medoids, big data job roles.

The term Big Data was coined by Roger Mougalas back in 2005. However, the application of big data and the quest to understand the available data is something that has been in existence for a long time.

Big Data Interview Questions and Answers

1. What is Big Data?

Data that is huge in Volume (size), Variety, and Velocity (speed) is known as big data.

Structured, unstructured, and semi-structured data are all types of big data. Most of today’s big data is unstructured, including videos, photos, web pages, and multimedia content. Each type of big data requires a different set of big data tools for storage and processing.

2. Explain in detail the 3 different types of big data?

Big data is classified in three ways:

  1. Structured Data
  2. Unstructured Data
  3. Semi-Structured Data

Structured Data:

Structured data can be defined as the data that resides in a fixed field within a record. It is bound by a certain schema, so all the data has the same set of properties.

Structured data is also called relational data. It is split into multiple tables to enhance the integrity of the data by creating a single record to depict an entity.

Semi-Structured Data:

Semi-structured data is not bound by any rigid schema for data storage and handling. The data is not in the relational format and is not neatly organized into rows and columns like that in a spreadsheet. However, there are some features like key-value pairs that help in discerning the different entities from each other.

Unstructured Data:

Unstructured data is the kind of data that doesn’t adhere to any definite schema or set of rules. Its arrangement is unplanned and haphazard. Photos, videos, text documents, and log files can be generally considered unstructured data.

3. What are the three steps involved in Big Data?

The three essential steps involved in Big Data are:

i.Data ingestion:

Data ingestion, the process of collecting or streaming information from various sources like log files, social media files, and SQL databases.

ii.Data storage:

To Load or store the extracted data in HDFS or NoSQL by the HBase. It can be easily accessed and processed by applications.

iii.Data processing:

The last step in the deployment of the solution is data processing. Usually, data processing is done via frameworks like Hadoop, Spark, MapReduce, Flink, and Pig, to name a few.

4.  Explain The Five Vs of Big Data?

Big Data is defined as a collection of large and complex unstructured data sets from where insights are derived from Data Analysis using open-source tools like Hadoop.

The five Vs of Big Data are –

Volume – Amount of data in Petabytes and Exabytes
Variety – Includes formats like videos, audio sources, textual data, etc.
Velocity – Everyday data growth which includes conversations in forums, blogs, social media posts, etc.
Veracity – Degree of the accuracy of data available
Value – Deriving insights from collected data to achieve business milestones and new heights.

5. Where does Big Data come from?

There are three sources of Big Data.

Social Data: It comes from the social media channel’s insights on consumer behavior.
Machine Data: It consists of real-time data generated from sensors and weblogs. It tracks user behavior online.
Transaction Data: It generated by large retailers and B2B Companies frequent basis

6. Tell us about the relationship between Big data and Hadoop.

We need a system to process Big Data. Hadoop is a free and open-source platform developed by the Apache Software Foundation. When it comes to processing large amounts of data, Hadoop is a must-have.

Big Data is problem and Hadoop is solution.

7. List the most commonly used Big data tools.

There are numerous Big Data tools on the market today. Some offer storage and processing services. Some only offer storage and various APIs for processing, while others offer analytical services, and so on.

  1. Hadoop
  2. Spark
  3. HPCC
  4. Storm
  5. Cassandra
  6. Stats iQ
  7. Cloudera
  8. Openrefine

Etc.

  • Hadoop is an Apache foundation open-source Big Data platform. The beauty of it is that it can run on common hardware.
  • Spark is yet another Apache Foundation tool. It adds the ability to process streams. It also has in-memory data processing capabilities. As a result, it is much faster.
  • HPCC is an abbreviation for High-Performance Computing Cluster. It is a highly scalable super computing platform.
  • Cloudera is abbreviated as CDH.It is an enterprise level big data tool.
8. Why is big data important for organisations?

Big data is important because by processing big data, organisations can obtain insight information related to:

  • Cost reduction
  • Improvements in products or services
  • To understand customer behavior and markets
  • Effective decision making
  • To become more competitive
9. What are the most common input formats in Hadoop?

The most common input formats in Hadoop are –

  • Key-value input format
  • Sequence file input format
  • Text input format
10. What are the different file formats that can be used in Hadoop?

File formats used with Hadoop, include –

  • CSV
  • JSON
  • Columnar
  • Sequence files
  • AVRO
  • Parquet file

17. How businesses could be benefited with Big Data?

  • Big data analysis helps with the business to render real-time data.
  • It can influence to make a crucial decision on strategies and development of the company.
  • Big data helps within a large scale to differentiate themselves in a competitive environment.

18. What will happen with a NameNode that doesn’t have any data?

A NameNode without any data doesn’t exist in Hadoop. If there is a NameNode, it will contain some data in it or it won’t exist.

19. How do “reducers” communicate with each other?

This is a tricky question. The “MapReduce” programming model does not allow “reducers” to communicate with each other. “Reducers” run in isolation.

20. How are the missing values controlled in big data?

  • Missing values are the values which are not present in a single column. When we do not address the missing values, users may end up with incorrect data and, as a consequence, incorrect results.
  • So, already when we handle Big Data, we must correctly treat the incomplete data in order to find the accurate sample. There are several approaches to dealing with missing values.
  • We can either discard the data or use data imputation to replace it.
  • If indeed the number of missing values is limited, it is normal practice to depart it alone. If the number of cases exceeds a certain threshold, data imputation is performed.
  • In facts and figures, there are several techniques for estimating so-called missing values:
  • Regression
  • MLE stands for Maximum Likelihood Estimation.
  • Deletion in Lists/Pairs
  • Imputation of multiple data.

23.What are the challenges in the Virtualization of Big Data testing?

Virtualization is an essential stage in testing Big Data. The Latency of the virtual machine generates issues with timing. Management of images is not hassle-free too.

24. What are some of the interesting facts about Big Data?

  • According to the experts of the industry, digital information will grow to 40 zettabytes by 2020
    Surprisingly, every single minute of a day, more than 500 sites come into existence. This data is certainly vital and also awesome
  • With the increase in the number of smartphones, companies are funneling their money into it by carrying mobility to the business with apps
  • It is said that Walmart collects 2.5 petabytes of data every hour from its consumer transactions

25. How Does One Deploy Big Data Solution?

Big data solution is deployed in three steps. The first is data ingestion, which entails collecting data from different sources and extracting it through real-time streaming or batches. The second is data storage, where the data is kept in a database after extraction. The last step is data processing, done through different frameworks such as Hadoop, Spark, and Flink.

26. What are the types of Big Data?

Structured data
Unstructured data
Semi-structured data

27. How does Big Data work?

The way big data processing frameworks operate is that the source data is divided and processed by multiple machines in parallel.

28. What are your experiences in big data?

If you have had previous roles in the field of big data, outline your title, functions, responsibilities and career path. Include any specific challenges and how you met those challenges. Also mention any highlights or achievements related either to a specific big data project or to big data in general. Be sure to include any programming languages you’ve worked with, especially as they pertain to big data.

29. What is an “outlier” in the context of big data?

An outlier is a data point that’s abnormally distant from others in a group of random samples. The presence of outliers can potentially mislead the process of machine learning and result in inaccurate models or substandard outcomes. In fact, an outlier can potentially bias an entire result set. That said, outliers can sometimes contain nuggets of valuable information.

30. Are Hadoop and Big Data interconnected?

Big Data is a resource, and Hadoop is an open-source software application that helps to manage that resource by achieving a set of goals and objectives. To extract actionable insights, Hadoop is used to process, store, and analyze complex unstructured data sets using proprietary algorithms and methods. So, Yes, they are related, but they are not the same.

31. Why is big data important for organizations?

Big data is important because by processing big data, organizations can obtain insight information related to:

  • Cost reduction
  • Improvements in products or services
  • To understand customer behavior and markets
  • Effective decision making
  • To become more competitive

32. How can you process Big Data?

  • There are various frameworks for Big Data processing.
  • One of the most popular is MapReduce.
  • It consists of mainly two phases called Map phase and the Reduce phase.
  • In between Map and Reduce phase there is an intermediate phase called Shuffle.
  • The given job is divided into two tasks:
  • Map tasks
  • Reduce tasks.

33. Can you name the companies that use Big Data?

  • Facebook
  • Adobe
  • Yahoo
  • Twitter
  • Ebay

34. What do you know about the term “Big Data”?

  • Big Data is a term associated with complex and large datasets.
  • A relational database cannot handle big data, and that’s why special tools and methods are used to perform operations on a vast collection of data.
  • Big data enables companies to understand their business better and helps them derive meaningful information from the unstructured and raw data collected on a regular basis.
  • Big data also allows the companies to make better business decisions backed by data.

35. Explain the core methods of a Reducer.

There are three core methods of a reducer. They are-

setup() – Configures different parameters like distributed cache, heap size, and input data.
reduce() – A parameter that is called once per key with the concerned reduce task
cleanup() – Clears all temporary files and called only at the end of a reducer task.

36. What are the different types of Automated Data Testing available for Testing Big Data?

Following are the various types of tools available for Big Data Testing:

  • Big Data Testing
  • ETL Testing & Data Warehouse
  • Testing of Data Migration
  • Enterprise Application Testing / Data Interface /
  • Database Upgrade Testing

37. What is the difference between the testing of Big data and Traditional databases?

  • Developer faces more structured data in the case of conventional database testing as compared to testing of Big data which involves both structured and unstructured data.
  • Methods for testing are time-tested and well defined as compared to an examination of big data, which requires R&D Efforts too.
  • Developers can select whether to go for “Sampling” or manual by “Exhaustive Validation” strategy with the help of an automation tool.

38. What are the challenges in Automation of Testing Big data?

Organizational Data, which is growing every data, ask for automation, for which the test of Big Data needs a highly skilled developer. Sadly, there are no tools capable of handling unpredictable issues that occur during the validation process. Lots of Focus on R&D is still going on.

39. What are the challenges in the Virtualization of Big Data testing?

Virtualization is an essential stage in testing Big Data. The Latency of the virtual machine generates issues with timing. Management of images is not hassle-free too.

40. What are the respective components of HDFS and YARN?

The two main components are:

Slave Node: This is the node which acts as a slave node to store the data for processing and use by the Name Node
Name Node: It is the master node for processing metadata information for data blocks within the HDFS

Two main components of YARN are:

Node Manager: It executes the task on each single Data Node
Resource Manager: This component receives the processing request and accordingly designate to respective Node managers depending on processing needs

41. What do you mean by commodity hardware?

  • This is yet another Big Data interview question you’re most likely to come across in any interview you sit for.
  • Commodity Hardware refers to the minimal hardware resources needed to run the Apache Hadoop framework.
  • Any hardware that supports Hadoop’s minimum requirements is known as ‘Commodity Hardware.’

42. Why is Hadoop used in Big Data analytics?

Hadoop is an open-source framework in Java, and it processes even big volumes of data on a cluster of commodity hardware. It also allows running many exploratory data analysis tasks on full datasets, without sampling. Features that make Hadoop an essential requirement for Big Data are –

  • Data collection
  • Storage
  • Processing
  • Runs independently

43. What is the main difference between Sqoop and distCP?

DistCP is used for transferring data between clusters, while Sqoop is used for transferring data between Hadoop and RDBMS, only.

44. What happens if a NameNode isn’t populated with any data?

In Hadoop, a NameNode with no data does not exist. If a NameNode exists, it will either contain data or will not exist.

45. What is big data solution implementation?

  • To have clear project objectives and to collaborate wherever necessary
  • Gathering data from the right sources
  • Ensure the results are not skewed because this can lead to wrong conclusions
  • Be prepared to innovate by considering hybrid approaches in processing by including data from
  • structured and unstructured types, include both internal and external data sources
  • Understand the impact of big data on existing information flows in the organization. (company)

46. Define Active and Passive Namenodes?

Active NameNode runs and works in the cluster whereas Passive NameNode has comparable data like active NameNode.

47. What exactly is fsck?

The acronym fsck stands for File System Check. It is a command that is used by HDFS. This command is used to check for inconsistencies and problems in the file. For example, if a file has any missing blocks, HDFS is notified via this command.

48. What are the different methods to deal with big data?

Because Big Data provides a business with a competitive advantage over its competitors, a business can decide to tap the potential of Big Data based on its needs and streamline the various business activities based on its goals.As a result, the approaches to dealing with Big Data must be determined based on your business requirements and available budgetary resources.

49. What are the languages used in order to query the big data?

There are several languages available for querying Big Data. Some of these programming languages are functional, dataflow, declarative, or imperative. Big Data querying is frequently fraught with difficulties. As an example:

  • Data that is unstructured
  • Latency
  • Fault tolerance
  • By ‘unstructured data,’ we mean that the data, as well as the various data sources, do not adhere to any specific format or protocol.
  • By ‘latency,’ we mean the amount of time it takes certain processes, such as Map-Reduce, to produce a result.
  • By ‘fault tolerance,’ we mean the steps in the analysis that allow for partial failures, reverting to previous results, and so on.

50. What are the Test Parameters for the Performance?

Data Storage which validates the data is being stored on various systemic nodes
Logs that confirm the production of commit logs.
Concurrency establishing the number of threads being performed for reading and write operation
Caching confirms the fine-tuning of “key cache” & “row cache” in settings of the cache.
Timeouts are establishing the magnitude of query timeout.
Parameters of JVM are confirming algorithms of GC collection, heap size, and much more.
Map-reduce which suggests merging, and much more.
Message queue, which confirms the size, message rate, etc

51. What happens if a NameNode isn’t populated with any data?

In Hadoop, a NameNode with no data does not exist. If a NameNode exists, it will either contain data or will not exist.

52. Why is big data important for organizations?

Big data is important because by processing big data, organizations can obtain insight information related to:

  • Cost reduction
  • Improvements in products or services
  • To understand customer behavior and markets
  • Effective decision making
  • To become more competitive

53. Define Active and Passive Namenodes?

Active NameNode runs and works in the cluster whereas Passive NameNode has comparable data like active NameNode

54. How does Big Data work?

The way big data processing frameworks operate is that the source data is divided and processed by multiple machines in parallel.

55. How is Big Data applied within a business?

Retail: Log interactions and actions undertaken by the customer to predict behavior or increase profit.
Manufacturing: Use insights to boost quality and output.
Banking: Use machine learning to predict outcomes and identify potential fraud scenarios.
Healthcare: Detect patterns to improve existing or find new ways to take care of patients.

56. What are the tools used for extraction of big data?

There are numerous Big Data extraction tools available. Flume, Kafka, Nifi, Sqoop, Chukwa, Talend, Scriptella, Morphlines, and so on. Apart from data extraction, these tools also help with data modification and formatting.

There are several methods for extracting Big Data:

  • Batched
  • Continuous
  • Real-time
  • Asynchronous

57. How are the missing values controlled in big data?

  • Regression
  • MLE stands for Maximum Likelihood Estimation.
  • Deletion in Lists/Pairs
  • Imputation of multiple data.

58. Mention what is the difference between data mining and data profiling?

The difference between data mining and data profiling is that

Data profiling: It targets on the instance analysis of individual attributes. It gives information on various attributes like value range, discrete value and their frequency, occurrence of null values, data type, length, etc.

Data mining: It focuses on cluster analysis, detection of unusual records, dependencies, sequence discovery, relation holding between several attributes, etc.

59. Explain what is the criteria for a good data model?

Criteria for a good data model includes

  • It can be easily consumed
  • Large data changes in a good model should be scalable
  • It should provide predictable performance
  • A good model can adapt to changes in requirements

60. Explain what is K-mean Algorithm?

  • K mean is a famous partitioning method. Objects are classified as belonging to one of K groups, k chosen a priori.
  • In K-mean algorithm,
  • The clusters are spherical: the data points in a cluster are centered around that cluster
    The variance/spread of the clusters is similar: Each data point belongs to the closest cluster

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