LambdavsKappa.md

Feature	Lambda Architecture	Kappa Architecture
Data Ingestion	Typically involves landing raw data into a distributed file system (e.g., HDFS, S3) for the batch layer, and a message queue (e.g., Kafka, Kinesis) for the speed layer.	Primarily relies on a durable, ordered message queue (e.g., Apache Kafka) as the central point of data ingestion for all data.
Design Philosophy	Dual pipeline for batch and speed, merging at serving layer.	Single, unified stream processing pipeline for all data.
Codebase Management	Separate codebases for batch and speed processing, leading to potential duplication.	Single codebase for stream processing logic, simplifying maintenance.
Data Flow	Batch data processed periodically, real-time data processed continuously, merged for queries.	All data (historical and real-time) ingested and processed as a continuous stream.
Operational Complexity	Higher due to managing and orchestrating two distinct processing systems.	Lower, with a single stream processing system to manage and monitor.
Latency Characteristics	Low latency for real-time views, higher latency for batch views.	Consistent latency for all data processing, dependent on stream processing capabilities.
Data Consistency	Can be challenging to ensure consistency between the batch and speed layers' outputs.	Easier to achieve consistency as all data undergoes the same processing logic.
Update/Fix Complexity	Requires modifications and deployments in both batch and speed layers.	Typically involves re-processing the entire historical stream with the updated logic.
Fault Tolerance	Redundancy through dual processing, but recovery can involve coordinating both layers.	Relies heavily on the fault tolerance of the stream processing system; simpler recovery (reprocessing).
Development Overhead	Often requires implementing business logic twice.	Single implementation of business logic for all data.
Ideal Use Cases	Systems needing guaranteed accurate historical views alongside low-latency real-time insights, leveraging existing batch infrastructure.	Systems prioritizing operational simplicity, consistent processing, and near real-time analytics across all data.

Created using ChatGPT and also used: https://ozh.github.io/ascii-tables/