Big Data refers to the vast amount of data that is generated from various sources, such as social media, sensors, and business transactions.

Hadoop is a widely used framework for distributed processing of large datasets, enabling efficient data storage and analysis.

Data Mining involves the process of extracting valuable information and patterns from large datasets using various techniques.

Python is a widely used programming language for data analysis and machine learning due to its simplicity, rich libraries, and extensive community support.

Data Preprocessing involves the process of preparing raw data for analysis by cleaning, transforming, and integrating it into a structured format.

Tableau is a widely used tool for interactive data exploration and visualization, allowing users to create interactive dashboards and reports.

Data Lake refers to a central repository for storing large volumes of raw data in its native format, enabling flexible and scalable data storage.

Spark is a widely used distributed computing platform for large-scale data processing, offering in-memory processing and fast data analysis capabilities.

Machine Learning involves the use of statistical and computational techniques to enable computers to learn from data and make predictions.

MongoDB is a widely used open-source distributed database designed for storing and processing large volumes of data in a flexible and scalable manner.

Data Analytics involves the process of analyzing data to extract meaningful insights, identify trends, and make data-driven decisions.

Apache Kafka is a widely used distributed data processing and stream processing platform, enabling real-time data ingestion, storage, and processing.

Data Visualization involves the process of presenting data in a visual format, such as charts, graphs, and maps, to communicate insights and trends effectively.

Amazon Web Services (AWS) is a widely used cloud-based platform that provides a range of services for storing, analyzing, and managing large datasets.

Data Integration involves the process of combining data from multiple sources into a single, consistent view, enabling comprehensive data analysis and decision-making.