We study big-data hybrid-data-processing lambda architecture, which consolidates low-latency real-time frameworks with high-throughput Hadoop-batch frameworks over a massively distributed setup.In particular, real-time and batch-processing redken shades eq 07m driftwood engines act as autonomous multi-agent systems in collaboration.We propose a Multi-Agent Lambda Architecture (MALA) for e-commerce data analytics.
We address the high-latency problem of Hadoop MapReduce jobs by simultaneous processing at the speed layer to the requests which require a quick turnaround time.At the same time, the batch layer in parallel provides comprehensive coverage of data by intelligent blending of stream and historical data through the weighted voting method.The cold-start problem of streaming services is addressed through the initial offset from historical batch data.
Challenges of high-velocity data ingestion is resolved with distributed message queues.A proposed multi-agent decision-maker component is placed at the MALA stack as the gateway of the data pipeline.We prove efficiency of our batch model by implementing an array of features for an diegojavierfares.com e-commerce site.
The novelty of the model and its key significance is a scheme for multi-agent interaction between batch and real-time agents to produce deeper insights at low latency and at significantly lower costs.Hence, the proposed system is highly appealing for applications involving big data and caters to high-velocity streaming ingestion and a massive data pool.