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How Apache Spark Became Essential For Machine Learning

How Apache Spark Became Essential For Machine Learning

Disha Misal
Many top technology corporations like Yahoo, eBay, Facebook and Amazon are now actively using Apache Spark in their services. That is because Apache Spark is the fastest engine for big data processing. Spark runs on memory (RAM) instead of a disk and hence carries out the data processing faster. Spark is more efficient than big data Hadoop and faster than accessing data from disk. It provides rich APIs in Java, Python, R and Scala. Its main objective is to provide a unified platform for big data applications. It can integrate with the Hadoop ecosystem very conveniently.

Why Apache Spark?

Many processes in machine learning are computationally heavy. Distributing these processes via Apache Spark is the easiest, fastest and most efficient way. In industrial applications there is a need for an engine which is powerful enough to process data in real time and can perform in batch mode, as well as an engine that can perform in-memory processing. Apache Spark provides real-time streaming, interactive processing, graph processing, in-memory processing and batch processing with a very fast and simple interface. That is why it has gained a lot of importance to use with ML applications.

Use Cases:

Following are some of the most popular applications of the Apache Spark engine in various fields:

Entertainment: It is used in the gaming industry to discover patterns from the potential firehose of real-time in-game events and respond to them within no time. Tasks such as player retention, targeted advertising, auto-adjustment of complexity in the game can be deployed to it.

E-commerce: In e-commerce industry, real-time transaction information could be passed to a streaming clustering algorithm like k-means and the results of this can be combined or merged with other unstructured data sources and can be used to continuously improve recommendations over time with new trends and demands. Unstructured data sources can be anything like feedback from customers. ML algorithms process the millions of interactions by the user with the e-commerce platform, after they are represented in the form of (complicated) graphs. This is done using Apache Spark.

Finance and security: In the finance and security industry, Apache Spark is used to detect fraud or intrusion systems and authentication. Along with ML, it can analyse the business spend of an individual and it provides the necessary things that the bank must suggest in order to bring the individual to newer avenues of their products. It identifies problems in the financial industry quickly and accurately. These industries benefit if they know whether a particular transaction is a case of fraud or not. PayPal uses ML techniques like deep learning and neural networks for this application. The library, MLib, provides several algorithms like decision trees, SVMs, logistic regression, naïve Bayes, random forest and gradient boosting trees. Security providers can explore real-time data for any unethical or harmful activity.

Healthcare: Apache Spark is used to analyse the information of the patients based on their past records to predict which patients are prone to have health problems in the future. Spark is also used in genomic data sequencing to reduce the processing time.

Media: Some websites use Apache Spark along with MongoDB, which is an open source document database that uses document-oriented data models and a non-structured query language. It shows video recommendations to the users based on their history.

Apache Spark And ML

Many organisations have been using Apache Spark with ML algorithms. Yahoo, for example, uses ML algorithms along with Apache Spark to identify the news topics that the users would be interested in. If ML alone is deployed for this application, it requires 20000 lines of C or C++ code. But with Apache Spark, the programming code can be just as long as 150 lines. Netflix is another example that uses Apache Spark for real-time streaming so that better online video recommendations based on the user history, can be provided. Streaming devices depend on the event data, and Apache Spark ML capabilities are put together to provide efficient video recommendations.

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Spark library has a library for ML labelled as MLib. This Apache Spark library has algorithms for the functions of classification, regression, clustering, collaborative filtering, dimensionality reduction, etc. The classification includes classifying things into different categories. For example, in emails the classification is done in categories of inbox, sent, drafts, spam and so on. Clustering example is bifurcating the news on the basis of the title and content of the news. Some websites and applications show users advertisements and products to buy on the basis of their previous purchases. This is an example of collaborative filtering. Some of them also work with streaming data. For example, linear regression using least square or k-means clustering. Customer segmentation and sentiment analysis are also applications of Apache Spark with MLib.

Overall Summary Of Apache Spark:

Apache Spark helps in some challenging and computationally exhaustive tasks like processing high volumes of real-time and archived data, thereby integrating the complex capabilities such as ML and graph algorithms. It brings big data processing to the market. Terabytes of event data taken from the users is used in real-time interactions like video-streaming, or any kind of streaming for that matter.

Apache Spark provides a very powerful API for ML applications. Its goal is to make practical ML easy. It has lower-level optimisation primitives and higher-level pipeline APIs. It is largely used for predictive analytics solutions, recommendation engines and fraud detection systems being the most popular ones.

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