4.7. Big Data’s Impact on the Web#
Big data has dramatically reshaped web architecture, introducing new complexities and opportunities in how websites and web applications collect, manage, and analyze vast amounts of information. Key aspects of this transformation include data mining, metadata utilisation, and streaming data management, each bringing innovative technologies and techniques that influence modern web systems.
4.7.1. Big Data#
Big data refers to extremely large and complex datasets that traditional data processing tools cannot efficiently manage. It is characterised by the “3 Vs”:
Volume: The enormous amount of data generated every second from various sources like social media, sensors, transactions, and more.
Velocity: The speed at which new data is created and needs to be processed, often in real-time or near real-time.
Variety: The diverse types of data—structured, semi-structured, and unstructured—that range from text and images to videos and sensor data.
These attributes necessitate advanced technologies and methodologies for collection, storage, analysis, and management, fundamentally shaping modern web architecture and decision-making processes.
4.7.2. Data Mining#
Data mining involves extracting useful patterns, trends, and insights from large datasets. In the context of web architecture, it means collecting user behavior data, transaction logs, and other digital footprints to inform decision-making and improve services.
Modern web architectures handle data mining through distributed storage and processing frameworks. Technologies like Apache Hadoop use the MapReduce paradigm to process massive datasets across clusters of computers. Frameworks such as Apache Spark provide in-memory processing capabilities for faster analysis, making it feasible to sift through petabytes of data in real-time or near real-time.
4.7.3. Metadata#
Metadata is data that describes other data. In web systems, it often encompasses information about user interactions, file properties, or system logs.
How Is Metadata Helpful?
Organisation and Retrieval: Metadata helps structure large datasets, making it easier to search, retrieve, and manage information. For example, image metadata such as tags, descriptions, and creation dates assist in organising media libraries.
Data Context: By providing context to raw data, metadata aids in understanding user behavior, content relevance, and system performance. This additional layer can guide content recommendations and targeted advertising.
Improved Data Quality: Metadata can track data lineage and transformations, ensuring accuracy and consistency throughout the data lifecycle.
4.7.4. Streaming Data Management#
Streaming data refers to continuous, rapid data flows generated by sources like user clicks, sensor readings, or financial transactions. Handling this in web architecture requires systems that can process data in motion rather than in batches.
How Is Streaming Data Handled?
Message Brokers: Technologies like Apache Kafka, RabbitMQ, and Amazon Kinesis act as intermediaries that collect, buffer, and distribute streams of data to various processing systems.
**Real-Time Processing Framework**s: Tools such as Apache Storm, Apache Flink, and Spark Streaming enable real-time analysis of data as it arrives. These frameworks process streams of data on-the-fly, allowing immediate insights and actions.