Artificial Intelligence Tutorial

Learn Apache Spark and Scala Programming with Hadoop Though Hadoop had established itself in the market, there were certain limitations associated with it. In the case of Hadoop, data was processed in various batches and, therefore, real-time data analytics was not enabled with Hadoop. As an added component of Hadoop, Apache Spark allows real-time data analytics including data streaming. For this reason, Apache Spark has become quite popular these days. The average salary of a data scientist who uses Apache Spark is around US$100,000.

Apache Spark is a new processing engine which is part of the Apache Software Foundation that is powering the Big Data applications around the world. It is taking over from where Hadoop MapReduce gave up or from where MapReduce started finding it increasingly difficult to cope with the exacting needs of a fast-paced enterprise.

Check out this for ‘Apache Spark Tutorial for Beginners’: https://intellipaat.com/blog/tutorial/spark-tutorial/

Businesses today are struggling to find an edge and get new opportunities or practices that drive innovation and collaboration. Large amounts of unstructured data and the need for increased speed to fulfill the real-time analytics have made this technology a real alternative for Big Data computational exercises. So let’s begin with this Apache Spark Tutorial.

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Evolution of Apache Spark Before Spark, there was MapReduce which was used as a processing framework. Initially, Spark was started as one of the research projects in 2009 at UC Berkeley AMPLab. It was later open sourced in 2010. The major intention behind this project was to create a cluster management framework that supports various computing systems based on clusters. After its release to the market, Spark grew and moved to the Apache Software Foundation in 2013. Now, most of the organizations across the world have incorporated Apache Spark for empowering their Big Data applications.

What does Spark do? Now, in this Apache Spark tutorial, we will see what does apache spark do? Spark has the capacity to handle zetta and yottabytes of data at the same time it is distributed across various servers (physical or virtual). It has a comprehensive level of APIs and developer libraries, supporting various languages like Python, Scala, Java, R, etc. It is mostly utilized in combination with distributed data stores like Hadoop’s HDFS, Amazon’s S3, and MapR-XD. And, it also used with NoSQL databases like Apache HBase, MapR-DB, MongoDB, and Apache Cassandra. Sometimes, it is also used with distributed messaging stores like Apache Kafka and MapR-ES.

Who can use Apache Spark? An extensive range of technology-based companies across the globe has moved toward Apache Spark. They were quick enough to identify the real value possessed by Spark such as Machine Learning and interactive querying. Industry leaders such as Huawei and IBM have adopted Apache Spark. The firms which were based on Hadoop, such as Hortonworks, Cloudera, and MapR, have moved to Apache Spark, already.

Originally published at www.intellipaat.com on September 6, 2019



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