Machine learning is the real reason for Apache Spark because, at the end of the day, you don't want to just ship and transform data from A to B (a process called ETL (Extract Transform Load)). You want to run advanced data analysis algorithms on top of your data, and you want to run these algorithms at scale. This is where Apache Spark kicks in.
Apache Spark, in its core, provides the runtime for massive parallel data processing, and different parallel machine learning libraries are running on top of it. This is because there is an abundance on machine learning algorithms for popular programming languages like R and Python but they are not scalable. As soon as you load more data to the available main memory of the system, they crash.
Apache Spark in contrast can make use of multiple computer nodes to form a cluster and even on a single node can spill data transparently to disk therefore avoiding the main memory bottleneck. Two interesting machine learning libraries are shipped with Apache Spark, but in this work we'll also cover third-party machine learning libraries.
The Spark MLlib module, Classical MLlib, offers a growing but incomplete list of machine learning algorithms. Since the introduction of the DataFrame-based machine learning API called SparkML, the destiny of MLlib is clear. It is only kept for backward compatibility reasons.
This is indeed a very wise decision, as we will discover in the next two chapters that structured data processing and the related optimization frameworks are currently disrupting the whole Apache Spark ecosystem. In SparkML, we have a machine learning library in place that can take advantage of these improvements out of the box, using it as an underlying layer.
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SparkML will eventually replace MLlib. Apache SystemML introduces the first library running on top of Apache Spark that is not shipped with the Apache Spark distribution. SystemML provides you with an execution environment of R-style syntax with a built-in cost-based optimizer. Massive parallel machine learning is an area of constant change at a high frequency. It is hard to say where that the journey goes, but it is the first time where advanced machine learning at scale is available to everyone using open source and cloud computing.
Deep learning on Apache Spark uses H20, Deeplearning4j and Apache SystemML, which are other examples of very interesting third-party machine learning libraries that are not shipped with the Apache Spark distribution.
While H20 is somehow complementary to MLlib, Deeplearning4j only focuses on deep learning algorithms. Both use Apache Spark as a means for parallelization of data processing. You might wonder why we want to tackle different machine learning libraries.
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The reality is that every library has advantages and disadvantages with the implementation of different algorithms. Therefore, it often depends on your data and Dataset size which implementation you choose for best performance.
However, it is nice that there is so much choice and you are not locked in a single library when using Apache Spark. Open source means openness, and this is just one example of how we are all benefiting from this approach in contrast to a single vendor, single product lock-in. Although recently Apache Spark integrated GraphX, another Apache Spark library into its distribution, we don't expect this will happen too soon. Therefore, it is most likely that Apache Spark as a central data processing platform and additional third-party libraries will co-exist, like Apache Spark being the big data operating system and the third-party libraries are the software you install and run on top of it.