A Way to Tune Apache Spark for Machine Learning

Apache Spark is a software framework for managing huge quantities of data that is available as open-source software. It has been created and maintained by Apache Software, with contributions from a variety of additional external sources. A bigdata certificate course may be completed online by applicants. Apache Spark is the most recent data processing framework to emerge from the open-source community. A large-scale data analysis engine, it will almost certainly replace Hadoop’s MapReduce in the near future. Apache Spark is associated words in the sense that the Scala shell is the most casual method to start a Spark job in close style. Apache Spark is most often preferred by working professionals.

Apache Spark is used for Big Data and Machine Learning applications, respectively.

Apache Spark is the buzzword in the field of big data. This is an open-source high-speed data processing engine for Hadoop data. The hallmarks of Apache Spark are incredibly high speed, easy to use, and high-end data analytics. Apache Spark can work as a parallel framework with Apache Hadoop which facilitates quick and easy development of big data applications on Hadoop. It is an alternative to Hadoop’s MapReduce. Apache Spark was developed by the University of California’s AMPLab in the year 2009.

High-speed processing 

High-speed processing of large volumes of complex data is the mainstay of Big Data. Thus, companies involved in Big Data require frameworks that can process large volumes of data at high speed. Apps developed using Apache Spark in Hadoop clusters run 100 times faster in memory and 10 times faster on disk.

The high speed of processing by Apache Spark is achieved by Resilient Distributed Dataset. RDD methods used by Spark enable it to store data transparently in the memory and read/write it to the disk only when required. This way Spark reduces the time for reading/writing to disk significantly. Consequently, the processing speed is accelerated.

Advanced analytics 

Apache Spark developers supports advanced analytics. This is the reason why it has become popular among data scientists and developers. Spark supports complex operations such as SQL queries, data streaming and complex analytics for machine learning, graph algorithms and many more. Its stack of libraries includes graphsX, MLlib, Spark SQL and Spark Streaming.

MLlib is a machine learning library that is scalable and provides high-quality algorithms and incredibly high speed. It can be used in Java, Python and Scala languages.

Spark SQL is the most commonly used interface used by developers for developing applications. Spark SQL is concerned with the processing of structured data using data frame. Spark SQL has SQL-2003 compliant interface for querying data. The standard interface is also provided for reading/writing to and from other data stores such as Apache Hive, Apache ORC, Apache Parquet, JSON and JDBC.

GraphX is a graph computation engine that enables users to process graph-structured data. It has a library of distributed algorithms. For example, the GraphFrames package enables you to graph operations on data frames.

Spark Streaming enables the development of applications that not only process batch data but also real-time and near real-time data. In Apache Hadoop, batch and stream processing are different entities. Spark Streaming converts stream data into a continuous set of micro-batches. The micro-batches are processed using Apache Spark API. Thus, both streaming and batch operations have the same code and run on the same framework. Spark Streaming easily integrates with data sources such as Twitter, HDFS, Kafka and Flume.

Multiple language support 

Apache Spark supports a host of programming languages such as Java, Python, Scala and R. Thus, developers can create and run applications in their preferred programming languages. Spark also has a built-in set of more than 80 high-level operators for querying data from role of Devops, SQL, R, Python and Scala shells.

High flexibility 

Apache Spark can not only run independently in cluster mode but also in Hadoop YARN, Kubernetes, Apache Mesos and Cloud. It can access multiple data sources such as Cassandra, HDFS, HBase and Hive. Thus, Spark can be used for migrating Hadoop applications that are Spark-friendly.

Multiple applications 

Apache Spark is useful for machine learning applications. MLlib, the machine learning library of Spark, is scalable, highly language compatible and swift. Moreover, it can perform high-grade analytical tasks such as clustering, classification and dimensionality reduction. Thus, Spark can be used for sentiment analysis, predictive intelligence, predictive analysis and customer segmentation.

Fog Computing is required to process the massive volumes of data generated by devices connected through IoT. Apache Spark has all the features required for fog computing such as parallel processing of machine learning, graph analytics algorithms and low latency. Thus, Spark is highly suited for fog computing in IoT devices. The high-speed interactive analysis is enabled by Spark.

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