NAIAD
NAIAD is an open-source distributed data processing system designed for low latency, high throughput data analysis. It combines qualities of batch and stream processing systems to enable real-time processing of large datasets.
distributed
low-latency
high-throughput
realtime-processing
large-datasets
NAIAD: Open-Source Distributed Data Processing System
NAIAD is an open-source distributed data processing system designed for low latency, high throughput data analysis. It combines qualities of batch and stream processing systems to enable real-time processing of large datasets.
What is NAIAD?
NAIAD is an open-source distributed data processing system designed for low latency and high throughput analysis of large datasets. It was developed by Microsoft Research to bridge the gap between batch processing systems like Hadoop and stream processing systems like Storm.
Some key features of NAIAD include:
- Unifies stream and batch processing models to enable real-time processing on large data
- Achieves low latency via timely dataflow, incremental processing and loop-carried state
- Provides exactly-once semantics without impacting latency or throughput
- Supports cyclic dataflows for iterative computations
- Handles fine-grained feedback loops seamlessly
- Scales efficiently through partitioning and scale-out
NAIAD can run on commodity clusters and has flexible APIs for .NET and C#. It is compatible with existing data pipelines. The unified programming model allows developers to represent streaming, batch and iterative programs using the same system. This eliminates need to combine separate platforms.
Overall, NAIAD delivers low latency, high throughput and scalable distributed data processing. It is well-suited for real-time analysis, machine learning and other applications needing fast processing of big data streams and batches.
NAIAD Features
Features
- Fault-tolerant distributed execution engine
- Supports iterative computations
- Low latency stream processing
- High throughput batch processing
- Unified programming model for batch and streaming
- In-memory caching for fast access
- Graph-based dataflow programming model
Pricing
- Open Source
Pros
Unified batch and streaming processing
Low latency
High throughput
Fault tolerance
In-memory caching
Iterative processing
Open source
Cons
Limited adoption and community
Not as fully featured as Spark or Flink
Only supports C# and F# for development
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