Big data FAQ with answer
Section –A
What is the need of Data analytics?
The need for Data Analytics arises due to the increasing volume, variety, and velocity of data generated by businesses and organizations. It helps in extracting meaningful insights, making informed decisions, optimizing processes, and gaining competitive advantages.
Define Big Data.
Big Data refers to large and complex datasets that cannot be effectively processed using traditional data processing applications. It encompasses the volume, variety, velocity, and veracity of data.
What are the different sources of Big Data?
The different sources of Big Data include social media platforms, IoT devices, sensors, transactional data, mobile devices, weblogs, and multimedia content.
What are different types of Data?
Different types of data include structured data (organized and easily searchable), unstructured data (not organized in a pre-defined manner), semi-structured data (partially organized), and meta-data (data about data).
Give examples of NoSQL Databases.
Examples of NoSQL databases include MongoDB, Cassandra, Couchbase, Redis, and Amazon DynamoDB.
What are different characteristics of Big Data?
Characteristics of Big Data include volume (large amount of data), velocity (speed at which data is generated), variety (different types of data), veracity (quality and reliability of data), and value (extracting meaningful insights).
Section –B
State and prove CAP Theorem.
The CAP Theorem, proposed by Eric Brewer, states that in a distributed computer system, it is impossible to simultaneously guarantee all three of the following: consistency, availability, and partition tolerance. The theorem proves that in the event of a network partition, one has to choose between consistency and availability.
Differentiate between Horizontal and Vertical Scalability.
Horizontal Scalability involves adding more machines or nodes to a system to handle increased load or data volume. It typically involves adding more servers to distribute the load.
Vertical Scalability involves increasing the resources (CPU, RAM, storage) on a single machine to handle increased load or data volume. It typically involves upgrading the existing server hardware.
Horizontal Scalability involves adding more machines or nodes to a system to handle increased load or data volume, while Vertical Scalability involves increasing the resources (CPU, RAM, storage) on a single machine to handle increased load or data volume.
Section –C
Differentiate between SQL and NoSQL and NewSQL Databases
SQL databases are relational databases that use structured query language for defining and manipulating data. Examples include MySQL, PostgreSQL, and Oracle.
NoSQL databases are non-relational databases that do not require a fixed schema and are suitable for handling large volumes of unstructured data. Examples include MongoDB, Cassandra, and Couchbase.
NewSQL databases are a category of SQL databases that provide the scalability of NoSQL systems while still maintaining ACID properties of traditional relational databases. Examples include Google Spanner and CockroachDB.
SQL databases are relational databases using structured query language, NoSQL databases are non-relational and suitable for handling unstructured data, while NewSQL databases combine scalability of NoSQL with ACID properties of SQL.
Differentiate between Big Data Analytics and Business Intelligence.
Big Data Analytics involves the analysis of large and complex datasets to uncover hidden patterns, correlations, and other insights.
It often involves the use of advanced analytical techniques and technologies to derive value from data.
Business Intelligence focuses on the analysis of historical data to support decision-making processes within an organization. It provides insights into past performance and trends using reporting, querying, and data visualization tools.
Big Data Analytics analyzes large, varied datasets to uncover patterns and insights, while Business Intelligence focuses on historical data for decision-making.
Section –D
Define Big Data Analytics and also explain its different types by taking suitable examples.
Big Data Analytics involves examining large datasets to uncover insights. Types include descriptive, predictive, prescriptive, and diagnostic analytics.
Big Data Analytics refers to the process of examining large and varied datasets to uncover hidden patterns, unknown correlations, market trends, customer preferences, and other useful information.
It involves the use of advanced analytics techniques such as machine learning, data mining, predictive analytics, and text mining.
Example types include descriptive analytics, predictive analytics, prescriptive analytics, and diagnostic analytics.
What is NoSQL Database? Explain the different types of NoSQL Databases.
NoSQL databases are non-relational databases for handling unstructured data. Types include document-oriented, key-value stores, column-oriented, and graph databases.
A NoSQL (Not Only SQL) database is a type of database that provides a mechanism for storage and retrieval of data that is modeled in means other than the tabular relations used in relational databases.
Types of NoSQL databases include document-oriented (e.g., MongoDB), key-value stores (e.g., Redis), column-oriented (e.g., Cassandra), and graph databases (e.g., Neo4j).
Each type is optimized for specific data models and use cases.