The main objective of Big Data Analytics is to analyze vast amounts of data to uncover hidden patterns, trends, and insights that can inform decision-making and improve business outcomes.

Veracity refers to the accuracy and reliability of data, which is not a defining characteristic of Big Data. The three main characteristics of Big Data are Volume, Variety, and Velocity.

Cloud Computing plays a crucial role in Big Data Analytics by offering scalable infrastructure, on-demand access to computing resources, and enabling collaboration and data sharing among users.

PaaS is the preferred cloud computing model for Big Data Analytics as it provides a ready-to-use platform with pre-configured tools and services, allowing developers to focus on building and deploying analytics applications without worrying about managing the underlying infrastructure.

Distributed systems excel in handling large-scale data processing tasks by distributing data and computations across multiple nodes, resulting in improved scalability and fault tolerance.

Apache Hadoop, Apache Spark, and Apache Flink are popular distributed computing frameworks used for Big Data Analytics. Each framework has its own strengths and is suitable for different types of analytics workloads.

HDFS is a distributed file system designed to store and manage large data sets efficiently. It provides fault tolerance and data replication mechanisms to ensure data availability and reliability.

TaskTracker is the component in Hadoop responsible for executing tasks in parallel. It receives tasks from the JobTracker and assigns them to worker nodes for processing.

Apache Spark offers several advantages over Hadoop, including faster processing speed due to its in-memory computing capabilities, a simplified programming model with APIs like Spark SQL and DataFrame, and improved fault tolerance through its resilient distributed datasets (RDDs).

Apache Kafka is a popular distributed stream processing engine that is often used in conjunction with Apache Spark. Kafka provides a scalable and fault-tolerant platform for ingesting, storing, and processing real-time data streams.

A data lake serves as a central repository for storing raw and unstructured data in its native format. It enables data exploration and analysis by providing tools and frameworks for data processing, integration, and visualization.

Lambda Architecture is a popular approach for real-time data processing and analytics. It combines batch processing for historical data with stream processing for real-time data, providing a comprehensive solution for handling both types of data.

Apache Druid is a specialized distributed system designed for time-series data analysis. It offers fast query performance, scalability, fault tolerance, and support for real-time data ingestion, making it a suitable choice for analyzing large volumes of time-series data.

Apache Cassandra is a widely used distributed database designed for handling large-scale structured data. It provides high scalability, fault tolerance, and efficient query performance, making it suitable for applications that require fast access to large data sets.

Apache Kudu is a columnar-oriented distributed database designed for real-time analytics. It offers a columnar storage format for efficient data compression and retrieval, in-memory caching for faster query performance, and support for ACID transactions, making it suitable for applications that require high performance and reliability.