## 3 Challenges in Big Data for Machine Learning
### Handling Variety and Velocity of Data  
Big data are characterized not only by their sheer volume but also by their variety and velocity. Variety refers to the wide range of data types that must be processed, including structured data (e.g., databases), semi-structured data (e.g., XML files), and unstructured data (e.g., text, images, and video). Each of these data types requires a different processing technique, and integrating them into a cohesive analysis pipeline is a significant challenge. For example, combining numerical data from sensors with textual data from social me dia and visual data from surveillance cameras into a single predictive model requires sophisticated data fusion techniques [239; 240].  
Recent advancements in DL have introduced specialized architectures for addressing these challenges. Transformers, originally designed for NLP, have demonstrated exceptional versatility in handling unstructured data such as text and images. Models like BERT (Bidirectional Encoder Representations from Transformers) and GPT (Generative Pre-trained Transformer) use self-attention mechanisms to capture contextual relationships in sequential data, enabling state-of-the-art performance in tasks like machine translation, text summarization, and image captioning. Beyond NLP, transformers have been adapted to handle tabular data and multi-modal datasets, further expanding their applicability to semi-structured data scenarios [241; 242; 243].  
For semi-structured data represented in graph formats, Graph Neural Networks (GNNs) have emerged as a powerful paradigm. GNNs such as Graph Convolutional Networks (GCNs) and Graph Attention Networks (GATs) leverage the structural relationships inherent in graph data to propagate and aggregate information across nodes and edges. This makes them particularly effective in domains such as social network analysis, molecular property prediction, and knowledge graph reasoning. For instance, GNNs can model complex relationships between entities in a knowledge graph, enhancing applications like recommendation systems and fraud detection [244; 245; 246].  
The velocity of big data--the speed at which new data are generated and need to be processed--adds another layer of complexity. In many applications, such as real-time analytics, autonomous vehicles, and financial trading, decisions must be made within milliseconds of receiving new data. Traditional batch processing methods are often insufficient for these scenarios, necessitating the development of real-time data processing frameworks that can handle high-throughput data streams with low latency. Tools such as Apache Kafka and Apache Flink have made significant strides in this area. Furthermore, combining these real-time data frameworks with advanced models like transformers and GNNs has shown promise in enabling end-to-end pipelines that process, analyze, and make predictions on diverse datasets at unprecedented speeds. As a result, the integration of transformers and GNNs into big data analytics represents a significant advancement in the field, addressing the challenges of both variety and velocity. These models not only enhance the ability to process unstructured and semi-structured data but also pave the way for the development of more adaptable and robust systems capable of operating in dynamic environments [247; 248; 249].