{"id":1899,"date":"2020-05-07T06:35:39","date_gmt":"2020-05-07T10:35:39","guid":{"rendered":"http:\/\/sys4seq.com\/?p=1899"},"modified":"2022-06-07T17:25:30","modified_gmt":"2022-06-07T21:25:30","slug":"building-a-real-time-big-data-pipeline-2-spark-core-hadoop-scala","status":"publish","type":"post","link":"https:\/\/sys4seq.com\/index.php\/2020\/05\/07\/building-a-real-time-big-data-pipeline-2-spark-core-hadoop-scala\/","title":{"rendered":"Building a real-time big data pipeline 2 : Spark Core, Hadoop, Scala"},"content":{"rendered":"<p>Apache Spark is a general-purpose, in-memory cluster computing engine for large scale data processing. Spark can also work with Hadoop and its modules. The real-time data processing capability makes Spark a top choice for big data analytics. The spark core has two parts. 1) Computing engine and 2) Spark Core APIs.<\/p>\n<p><a href=\"https:\/\/adinasarapu.github.io\/big-data\/2020\/02\/blog-post-spark\/\" target=\"_blank\" rel=\"noopener\">&gt;&gt;&gt;<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"Apache Spark is a general-purpose, in-memory cluster computing engine for large scale data processing. Spark can also work with Hadoop and its modules. The real-time data processing capability makes Spark a top choice for big data analytics. The spark core has two parts. 1) Computing engine and 2) Spark Core APIs. &gt;&gt;&gt;","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_mi_skip_tracking":false,"_monsterinsights_sitenote_active":false,"_monsterinsights_sitenote_note":"","_monsterinsights_sitenote_category":0},"categories":[44,43],"tags":[49,50,54],"_links":{"self":[{"href":"https:\/\/sys4seq.com\/index.php\/wp-json\/wp\/v2\/posts\/1899"}],"collection":[{"href":"https:\/\/sys4seq.com\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/sys4seq.com\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/sys4seq.com\/index.php\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/sys4seq.com\/index.php\/wp-json\/wp\/v2\/comments?post=1899"}],"version-history":[{"count":5,"href":"https:\/\/sys4seq.com\/index.php\/wp-json\/wp\/v2\/posts\/1899\/revisions"}],"predecessor-version":[{"id":1969,"href":"https:\/\/sys4seq.com\/index.php\/wp-json\/wp\/v2\/posts\/1899\/revisions\/1969"}],"wp:attachment":[{"href":"https:\/\/sys4seq.com\/index.php\/wp-json\/wp\/v2\/media?parent=1899"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/sys4seq.com\/index.php\/wp-json\/wp\/v2\/categories?post=1899"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/sys4seq.com\/index.php\/wp-json\/wp\/v2\/tags?post=1899"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}