How scalable is Lambda architecture for big data processing?

How scalable is Lambda architecture for big data processing?

Lambda architecture is designed to handle massive amounts of data by combining batch and real-time processing. Its scalability comes from its ability to distribute workloads across multiple nodes.

What are the main components of Lambda architecture?

The main components are the batch layer, speed layer, and serving layer. The batch layer processes large datasets, the speed layer handles real-time data, and the serving layer merges the results for querying.

Can you give an example of a use case for Lambda architecture?

Sure! A common use case is in real-time analytics for social media platforms, where data is processed both in real-time and in batches to provide insights.

What are the challenges of implementing Lambda architecture?

Challenges include complexity in managing multiple data processing systems, ensuring data consistency, and the need for skilled personnel to maintain the architecture.

Is there a better alternative to Lambda architecture?

Some alternatives include Kappa architecture, which simplifies the design by using a single stream processing system, and microservices architectures that can also handle scalability.