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# [mailto:jpildush@myseneca.ca?subject=DPS921 Joseph Pildush]
# [mailto:racali@myseneca.ca;jpildush@myseneca.ca?subject=DPS921 eMail All]
== Introduction ==
Intel Data Analytics Acceleration Library, also known as Intel DAAL, is a library created by Intel in 2015 to solve problems associated with Big Data.
Intel DAAL is available for Linux, OS X and Windows platforms and it is available for the C++, Python, and Java programming platforms.
Intel DAAL is optimized to run on a wide range of devices ranging from home computers to data centers and it uses Vectorization to deliver best performances.
Intel DAAL helps speed big data analytics by providing highly optimized algorithmic building blocks for all data analysis stages and by supporting different processing modes.
The data analysis stages covered are:
* Pre-processing
* Transformation
* Analysis
* Modeling
* Validation
* Decision Making
The different processing modes are:
* Batch processing - Data is stored in memory and processed all at once.
* Online processing - Data is processed in chunks and then the partial results are combined during the finalizing stage. This is also called Streaming.
* Distributed processing - Similarly to MapReduce Consumers in a cluster process local data (map stage), and then the Producer process collects and combines partial results from Consumers (reduce stage). Developers can choose to use the data movement in a framework such as Hadoop or Spark, or explicitly coding communications using MPI.
== Sources ==
* [https://software.intel.com/en-us/daal Intel's DAAL website]
* [https://software.intel.com/en-us/blogs/daal Intel's Blog Post about Intel DAAL]
* [https://software.intel.com/en-us/daal-programming-guide Intel DAAL Guide Book]
