Introduction to Big Data interview questions and answers
All kind of data that generates on the internet are termed as Big Data, over hundreds of GB of data are generated over the internet only by online activities. Online activity such as web activity, blogs, text, video/audio files, images, email, social network activity. Big data needs specialized systems and software tools to process all unstructured data. Data that can be generated from these activities are termed as to Big Data. Big Data is completely wide and distributed over the internet and thus, the processing big data need distributed systems and tools so as to extract information from them.
Below are the some Important 2019 Big Data interview questions and answers:
If you are looking for a job that is related to Big Data, you need to prepare for the 2019 Big Data interview questions. Though every interview is different and the scope of a job is also different, we can help you out with the top interview questions and answers, which will help you take the leap and get https://www.educba.com/what-is-nosql/ your success in your Big Data interview.
These questions are divided into two parts:
Part 1 – Big Data Interview Questions (Basic)
This first part covers basic interview questions and answers
1. What is the meaning of big data and how is it different?
Answer:
Big data is the term to represent all kinds of data generated on the internet. On the internet over hundreds of GB of data are generated only by online activity. Here, online activity implies web activity, blogs, text, video/audio files, images, email, social network activity, and so on. Big data can be referred to as data created from all these activities. Data generated online is mostly in unstructured form. Big data will also include transactions data in the database, system log files, along with data generated from smart devices such as sensors, IoT, RFID tags, and so on in addition to online activities.
Big data needs specialized systems and software tools to process all unstructured data. In fact, according to some industry estimates almost 85% of data generated on the internet is unstructured. Usually, relational databases have a structured format and the database is centralized. Hence, RDBMS processing can be quickly done using a query language such as SQL. On the other hand, big data is very large and is distributed across the internet and hence processing big data will need distributed systems and tools to extract information from them. Big data needs specialized tools such as Hadoop, Hive, or others along with high-performance hardware and networks to process them.
2. What are the characteristics of big data?
Answer:
Big data has three main characteristics: Volume, Variety, and Velocity.
Volume characteristic refers to the size of data. Estimates show that over 3 million GB of data is generated every day. Processing this volume of data is not possible in a normal personal computer or in a client-server network in an office environment with limited compute bandwidth and storage capacities. However, cloud services provide solutions to handle big data volumes and process them efficiently using distributed computing architectures.
Variety characteristic refers to the format of big data – structured or unstructured. Traditional RDBMS fits into the structured format. An example of an unstructured data format is, a video file format, image files, plain text format, from web document or standard MS Word documents, all have unique formats, and so on. Also to note, RDBMS does not have the capacity to handle unstructured data formats. Further, all this unstructured data must be grouped and consolidated which creates the need for specialized tools and systems. In addition new, data is added each day, or each minute and data grows continuously. Hence big data is more synonymous with variety.
The velocity characteristic refers to the speed at which data is created and the efficiency required to process all the data. For example, Facebook is accessed by over 1.6 billion users in a month. Likewise, there are other social network sites, YouTube, Google services, etc. Such data streams must be processed using queries in real-time and must be stored without data loss. Thus, the velocity characteristic is important in big data processing.
In addition, other characteristics include veracity and value. Veracity will determine the dependability and reliability of data and value is the value derived by organizations from big data processing.
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3. Why is big data important for organizations?
Answer:
This is the basic Big Data interview question asked in an interview. 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
4. Name some tools or systems used in big data processing?
Answer:
Big data processing and analysis can be done using,
• Hadoop
• Hive
• Pig
• Mahout
• Flume
Part 2 – Big data Interview Questions (Advanced)
Let us now have a look at the advanced Interview Questions.
5. How can big data support organizations?
Answer:
Big data has the potential to support organizations in many ways. Information extracted from big data can be used in,
• Better coordination with customers and stakeholders and to resolve problems
• Improve reporting and analysis for product or service improvements
• Customize products and services to selected markets
• Ensure better information sharing
• Support in management decisions
• Identify new opportunities, product ideas, and new markets
• Gather data from multiple sources and archive them for future reference
• Maintain databases, systems
• Determine performance metrics
• Understand interdependencies between business functions
• Evaluate organizational performance
6. Explain how big data can be used to increase business value?
Answer:
While understanding the need for analyzing big data, such analysis will help businesses to identify their position in markets, and help businesses to differentiate themselves from their competitors. For example, from the results of big data analysis, organizations can understand the need for customized products or can understand potential markets towards increasing revenue and value. Analyzing big data will involve grouping data from various sources to understand trends and information related to business. When big data analysis is done in a planned manner by gathering data from the right sources, organizations can easily generate business value and revenue by almost 5% to 20%. Some examples of such organizations are Amazon, Linkedin, WalMart, and many others.
Let us move to the next Big Data Interview Questions
7. What is big data solution implementation?
Answer:
Big data solutions are implemented at a small scale first, based on a concept as appropriate for the business. From the result, which is a prototype solution, the business solution is scaled further. This is the most popular Big Data interview questions asked in a Big Data interview Some of the best practices followed the in the industry include,
• 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
8. What are the steps involved in big data solutions?
Answer:
Big data solutions follow three standard steps in its implementation. They are:
Data ingestion: This step will define the approach to extract and consolidate data from multiple sources. For example, data sources can be social network feeds, CRM, RDBMS, etc. The data extracted from different sources is stored in a Hadoop distributed file system (HDFS).
Data storage: This is the second step, the extracted data is stored. This storage can be in HDFS or HBase (NoSQL database).
Process the data: This is the last step. The data stored must be processed. Processing is done using tools such as Spark, Pig, MapReduce, and others.
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