What is Apache Spark about?

What is Apache Spark about?

Apache Spark is a fast Big Data processing engine in memory equipped with Machine Learning capabilities that run up to 100 times faster than Apache Hadoop. It is a unified engine built around the ease concept.

Apache Spark is a new processing engine that is part of the Apache Software Foundation that supports Big Data applications worldwide. It takes over from where Hadoop MapReduce abandoned or where increasingly difficult on MapReduce to cope with a fast-paced enterprise’s demanding needs.

Organizations today are attempting to discover an edge and get new chances or practices that drive innovation and collaboration . A lot of unstructured information and the requirement for increased speed to satisfy the real-time analytics have made this technology a genuine option for Big Data computational activities.

Spark has the ability to deal with zetta and yottabytes of information while being distributed across different servers (physical or virtual). It has a comprehensive level of APIs and libraries for software engineers that support different computer languages like Python, Scala, Java, R, and so on. Unlike Hadoop, Spark does not come with its own file system – instead it can be integrated with many file systems including Hadoop’s HDFS, MongoDB and Amazon’s S3 system. It is for the most part used in mix with dispersed information stores like Hadoop’s HDFS, Amazon’s S3, and MapR-XD. What’s more, NoSQL databases such as Apache HBase, MapR – DB, MongoDB and Apache Cassandra are also used. It is also sometimes used in distributed messaging stores such as Apache Kafka and MapR – ES.

Used in e-Commerce industry
In the e-commerce industry, Spark finds a great application. Details of the real-time transaction can be sent to streaming algorithms such as K-means and collaborative filtering. The results can then be combined with other data sources such as product reviews, social media profiles, and customer reviews to provide clients with recommendations based on new trends.

Alibaba Taobao uses Spark on its e – commerce platform to analyze hundreds of petabytes of data. This e – commerce platform is interacting with a plethora of merchants. These interactions on this data represent a large graph and processing of machine learning.

Apache Spark is used by eBay to deliver targeted offers, improve customer experience and optimize overall performance. The Apache Spark engine is leveraged by Hadoop YARN.YARN at eBay to manage all the cluster resources to perform generic tasks. Users of eBay Spark leverage Hadoop clusters across YARN in the range of 2000 nodes, 20,000 cores, and 100 TB of RAM.

Used in Healthcare
Apache Spark uses advanced patient record analytics to determine which patients are more likely to become sick after discharge. The hospital can better deploy healthcare services to the patients identified, saving both hospitals and patients on costs.

The key features of Apache Spark Advantages are:

Speed: Spark is 100 times faster than Hadoop for large – scale data processing, irrespective of whether data is stored in memory or on disk. Spark will perform faster even if the data is stored on the disk. Spark has a world record for large – scale data on – disk sorting.

Ease of use: Spark’s approach to a cluster of data sets is crystal-clear and declarative. It has a collection of data transformation operators, dataset-specific APIs, or data frames for semi-structured and structured data manipulation. Spark also has a single application entry point.

Simplicity: Spark is designed to make it easy for rich APIs to access. It is specially designed in large data scale for quick and easy interaction. For application developers and data scientists to start working on Spark instantly, APIs are well – documented.

Support: Spark supports different programming languages such as Python, Scala, Java, R and so on. It also integrates Hadoop – based storage solutions such as MapR, Apache Cassandra, Apache HBase and Apache Hadoop (HDFS) with other storage solutions.

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