{"product_id":"learn-apache-spark-full","title":"Learn Apache Spark Full","description":"\u003cp\u003eIndustries are using Hadoop extensively to analyze their data sets. The reason is that Hadoop framework is based on a simple programming model (MapReduce) and it enables a computing solution that is scalable, flexible, fault-tolerant and cost effective. Here, the main concern is to maintain speed in processing large datasets in terms of waiting time between queries and waiting time to run the program.\u003c\/p\u003e\n\u003cp\u003eSpark was introduced by Apache Software Foundation for speeding up the Hadoop computational computing software process.\u003c\/p\u003e\n\u003cp\u003eAs against a common belief, Spark is not a modified version of Hadoop and is not, really, dependent on Hadoop because it has its own cluster management. Hadoop is just one of the ways to implement Spark.\u003c\/p\u003e\n\u003cp\u003eSpark uses Hadoop in two ways – one is storage and second is processing. Since Spark has its own cluster management computation, it uses Hadoop for storage purpose only.\u003c\/p\u003e","brand":"None","offers":[{"title":"Kobo eBook","offer_id":46665692741842,"sku":"559aa0e8-bfcd-3ef4-96f7-72fff89cd44d","price":4.11,"currency_code":"CAD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0655\/8980\/5233\/files\/image_e0bb528f-ae09-42d8-a864-077b781eb161.jpg?v=1762233532","url":"https:\/\/www.indigo.ca\/products\/learn-apache-spark-full","provider":"Indigo","version":"1.0","type":"link"}