# LAMBDA ARCHITECTURE
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Lambda architecture is an approach to [big data](ahttps://searchcloudcomputing.techtarget.com/definition/big-data-Big-Data) management that provides access to [batch](https://searchdatacenter.techtarget.com/definition/batch)processing and near [real-time](https://whatis.techtarget.com/definition/real-time) processing with a **hybrid approach.**
The basic architecture of Lambda has three layers:
a) Batch
b) speed
c) serving
\>The batch layer, which typically makes use of [Hadoop](https://searchdatamanagement.techtarget.com/definition/Hadoop), is the location where all the data is stored. [MapReduce](https://searchcloudcomputing.techtarget.com/definition/MapReduce) runs regular batch processing on the totality of this data. This information is sent to a [data store](https://whatis.techtarget.com/definition/data-store) and is used to gain insights into historic data trends.
\>Alongside this slower layer, new data is captured and processed as it comes in. **The speed layer** provides business users with the ability to adjust decision making and respond quickly to rapidly emerging trends. Data that passes into this real-time layer is also copied into the larger data set for slower, batch processing. Once the real-time processing is complete, the data is cleared from the speed layer to clear the way for more incoming data. The real-time layer can operate efficiently even with a steady stream of complex data because it only has to handle the volume of data that comes in between rounds of batch processing.
\>The speed and batch layers are merged together for querying through the **serving layer** which features a [massively parallel processing](https://whatis.techtarget.com/definition/MPP-massively-parallel-processing) query engine. Having access to this combined [data set](https://whatis.techtarget.com/definition/data-set)helps ensure that accurate reporting is available at all times with low [latency](https://whatis.techtarget.com/definition/latency).
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Lambda Architecture Approach :
This approach to architecture attempts to **balance latency, throughput, and fault-tolerance** by using **batch** processing to provide comprehensive and accurate views of batch data, while simultaneously using real-time stream processing to provide views of online data.
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