Common deployment architectures for AI data platforms typically follow three patterns: batch-oriented pipelines, real-time/streaming systems, and hybrid approaches. These architectures address different data processing needs, scalability requirements, and use cases. The choice depends on factors like latency tolerance, data volume, and the need for real-time insights.
Batch-oriented architectures process large datasets in scheduled intervals, making them suitable for scenarios where freshness isn’t critical. For example, training machine learning models on historical sales data or generating weekly analytics reports. These systems often use distributed storage like Hadoop HDFS or cloud object storage (e.g., Amazon S3) and batch-processing frameworks like Apache Spark. Data is ingested, transformed, and loaded into databases or data lakes (e.g., Delta Lake) through ETL pipelines. A typical workflow might involve nightly jobs that aggregate user activity logs, clean the data, and update a feature store for model training. While cost-effective for handling large volumes, batch processing introduces latency and isn’t ideal for applications requiring immediate feedback, such as fraud detection.
Real-time architectures focus on low-latency data processing, often using streaming frameworks like Apache Kafka for data ingestion and Apache Flink or Apache Kafka Streams for transformations. These systems are critical for use cases like monitoring IoT sensor data, dynamic pricing, or live user personalization. For instance, a ride-sharing app might process GPS and demand data in real time to adjust prices. Data is often stored in high-speed databases like Redis or Apache Cassandra to support rapid queries. A common setup involves Kafka topics decoupling producers (e.g., mobile apps) from consumers (e.g., ML inference services), with stream processing logic filtering or enriching events before they’re acted upon. However, maintaining real-time pipelines requires careful handling of out-of-order data, state management, and scaling complexities.
Hybrid architectures combine batch and real-time components to balance latency and accuracy. The Lambda architecture is a classic example, where a batch layer handles historical data corrections, and a speed layer processes real-time data. A modern alternative is the Kappa architecture, which uses a single stream-processing layer and reprocesses data when logic changes. For example, a recommendation system might use batch processing to train models on historical user behavior and real-time streams to incorporate recent clicks. Tools like Apache Beam allow developers to write unified code for batch and stream processing, simplifying maintenance. Hybrid approaches are flexible but add operational overhead, requiring synchronization between systems and careful handling of duplicate data. They’re often seen in large-scale platforms like e-commerce sites, where both long-term trends and immediate user interactions matter.