Top 10 Big Data Interview Questions You Should Prepare For [Updated 2024]

Sample Answer
MapReduce is a programming model for processing big data sets in parallel across a distributed cluster, or an environment where many computers are networked together and operate as a single entity. It consists of two phases: the Map phase and the Reduce phase. During the Map phase, the input dataset is broken down into chunks and a map function is applied to each chunk to produce key-value pairs. The Reduce phase then takes these pairs and combines the values with the same key. For instance, if we had a large set of documents and wanted to count the frequency of each word across all documents, we could use MapReduce. The Map phase would create pairs for each word and its occurrences in a document, and the Reduce phase would combine these pairs to get the total counts per word across all documents.

Sample Answer
Structured data is data that has a predefined data model or is organized in a manner that it can be easily and efficiently processed within a fixed schema. It is typically stored in relational databases (RDBMS). Examples include data from CRM systems, ERP systems, and financial transactions. Unstructured data, on the other hand, does not have a predefined data model and is not organized in a predefined manner. This data is typically text heavy and includes things like social media posts, text messages, and videos. The main difference between the two lies in their structure and processing needs. Structured data is easier to analyze and store, while unstructured data requires more complex and advanced tools to process and understand.

Sample Answer
A Data Lake is a storage repository that holds a large amount of raw data in its native format until it is needed. Unlike a traditional database, a Data Lake stores all types of data: structured, semi-structured, and unstructured. This allows for more flexibility and scalability, as you can add any data at any time, and it can be processed later according to the business requirements. However, the drawback is that Data Lakes require strong data governance strategies to prevent them from becoming ‘Data Swamps’, where the data is not well-managed or understood.

Sample Answer
Apache Hadoop is an open-source software framework used for distributed storage and processing of large data sets. It consists of a storage part, known as Hadoop Distributed File System (HDFS), and a processing part which is a MapReduce programming model. Hadoop splits files into large blocks and distributes them across nodes in a cluster. It then transfers packaged code into nodes to process in parallel, which makes it highly efficient. Furthermore, Hadoop is fault-tolerant; when data is sent to a node, that data is also replicated to other nodes in the cluster, so in the event of a failure, there is another copy available for use.

Sample Answer
Hive is a data warehouse infrastructure tool which is used for processing structured data in Hadoop. It resides on top of Hadoop to summarize Big Data and makes querying and analyzing easy. Hive translates SQL-like queries into MapReduce jobs for easy execution and processing of extremely large volumes of data. It was developed by Facebook and is now used and developed by other companies such as Netflix and the Financial industry.

Sample Answer
Data cleaning in the context of Big Data is a crucial step before any data analysis. This process ensures that the data is accurate, consistent, and reliable. It involves several steps such as removing duplicates, handling missing values, and correcting inconsistencies. This process might involve using specific tools or writing scripts to automate the process. For instance, we might use a tool like Trifacta to help us with data cleaning.

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NoSQL databases, or ‘Not Only SQL’ databases, were designed as a solution to the scalability and flexibility issues that plague traditional SQL databases. These databases are perfect for dealing with Big Data because they are able to handle large volumes of data and can process data much faster than traditional SQL databases. Moreover, NoSQL databases are schema-less, which means they can handle unstructured data more efficiently than SQL databases. For instance, MongoDB, a type of NoSQL database, can handle a variety of data types, including text, images, and social media posts, making it a great fit for Big Data projects.

Sample Answer
Data Sparsity refers to a situation where a large majority of the values in a dataset are missing or zero. This is a common occurrence in many real-world datasets, especially in fields like text mining and bioinformatics. In the context of Big Data, sparsity can be a significant challenge because sparse data can affect the performance and accuracy of machine learning models. For instance, if most of the values are missing in a dataset, a model might not have enough information to make accurate predictions or might overfit to the non-missing values. To deal with this, we might use techniques like dimensionality reduction, or we could use specific algorithms designed to work with sparse data.

Sample Answer
Apache Spark is an open-source, distributed computing system used for big data processing and analytics. It provides an interface for programming entire clusters with implicit data parallelism and fault tolerance. What makes Spark exceptionally fast for data processing tasks is its capability to perform in-memory computations. This feature is particularly useful when processing large datasets as it significantly reduces the time spent on reading and writing to disks. Spark supports programming in Java, Scala, Python, and R, and includes libraries for diverse tasks ranging from SQL to streaming and machine learning. It can also integrate well with Hadoop and its modules, and can read data from HDFS, HBase, and Hive.

Sample Answer
A data scientist plays a critical role in handling big data. They are responsible for interpreting and analyzing large datasets, developing algorithms and predictive models to extract meaningful insights. These insights are then used to make strategic business decisions. However, handling big data comes with some challenges. Data quality is a major issue as the data can come from various sources and in different formats. Ensuring the data is clean, consistent and reliable can be difficult. Data integration is another challenge, merging data from different sources while maintaining data integrity can be complex. Lastly, scalability can be an issue, as the volume of data grows, the infrastructure and algorithms used need to be able to scale efficiently.