Big Data Processing Architectures
Processing large-scale datasets requires specialized architectures that enable efficient storage, retrieval, and computation. The two primary architectures used in Big Data Processing are Batch Processing and Stream Processing.
Batch Processing
Batch processing handles large volumes of data in chunks, typically at scheduled intervals. It is suited for workloads where real-time processing is not required. Examples include data aggregation, log analysis, and ETL (Extract, Transform, Load) pipelines.
- Hadoop MapReduce: A batch processing framework that uses a distributed file system (HDFS) and a MapReduce programming model to process vast datasets.
- Apache Hive: A SQL-like querying engine built on top of Hadoop, allowing structured querying of big datasets.
- Google BigQuery: A cloud-based data warehouse optimized for batch analytics with fast SQL querying capabilities.
Stream Processing
Stream processing handles real-time data by processing events as they arrive, making it suitable for applications like fraud detection, real-time analytics, and IoT monitoring.
- Apache Kafka: A distributed event streaming platform that ingests and processes real-time data streams.
- Apache Flink: A stream processing engine optimized for event-time processing with low latency.
- Apache Spark Streaming: A real-time extension of Apache Spark that processes live data streams.