Prompt Title: Big Data is Complex.

Big Data refers to extremely large data sets that are too complex and difficult to process using traditional data processing methods. Some examples of Big Data sets include:

1. Social Media Data: Social media platforms generate vast amounts of data in the form of posts, likes, comments, shares, etc. This data is unstructured, meaning it doesn't fit neatly into rows and columns like traditional data sets. Analyzing this data requires complex techniques like sentiment analysis, network analysis, and natural language processing.

2. Internet of Things (IoT) Data: IoT devices generate enormous amounts of data in real-time, such as temperature readings, location data, and sensor data. Processing this data requires distributed computing frameworks like Apache Spark and Hadoop, which can handle the volume and velocity of IoT data.

3. Financial Data: Financial institutions generate large amounts of data, including stock prices, trading volumes, and economic indicators. Analyzing this data requires machine learning algorithms that can quickly identify patterns and trends.

4. Healthcare Data: Healthcare data is generated from electronic health records, medical devices, and wearables. This data is often unstructured and complex, making it difficult to analyze using traditional statistical techniques. Machine learning algorithms can help identify patterns in this data to improve patient outcomes.

The complexity of these data sets makes them challenging to analyze using conventional techniques. Big Data requires specialized tools and techniques, such as distributed computing, machine learning, and natural language processing, to extract meaningful insights from the vast amount of data.