oaxino Posted December 16, 2022 Report Share Posted December 16, 2022 Published 12/2022MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHzLanguage: English | Size: 640.96 MB | Duration: 1h 55mA Basic to Advanced Overview for processing Big Data with SparkWhat you'll learnOOPS and Functional Programming in ScalaApache Spark FrameworkAdvanced Spark ProgrammingIntegrating Spark with KafkaSpark MLib - Machine LearningSpark Streaming, SparkSQL, Spark GraphX etc.RequirementsIntermediate programming experience in Python or ScalaBeginner experience with the DataFrame APIBasic understanding of Machine Learning conceptsDescriptionApache Spark is a cluster computing platform designed to be fast and general-purpose. On the speed side, Spark extends the popular MapReduce model to efficiently support more types of computations, including interactive queries and stream processing. Speed is important in processing large datasets, as it means the difference between exploring data interactively and waiting minutes or hours. One of the main features Spark offers for speed is the ability to run computations in memory, but the system is also more efficient than MapReduce for complex applications running on disk. On the generality side, Spark is designed to cover a wide range of workloads that previously required separate distributed systems, including batch applications, iterative algorithms, interactive queries, and streaming. By supporting these workloads in the same engine, Spark makes it easy and inexpensive to combine different processing types, which is often necessary in production data analysis pipelines. In addition, it reduces the management burden of maintaining separate tools. Spark is designed to be highly accessible, offering simple APIs in Python, Java, Scala, and SQL, and rich built-in libraries. It also integrates closely with other Big Data tools. In particular, Spark can run in Hadoop clusters and access any Hadoop data source, including Cassandra.OverviewSection 1: Module 1Lecture 1 Functions and Procedures in ScalaLecture 2 Call By Name ParameterLecture 3 Functions with Named ArgumentsLecture 4 Functions with Variable ArgumentsLecture 5 Recursion FunctionsLecture 6 Default Parameters for a FunctionLecture 7 Nested FunctionsLecture 8 Anonymous FunctionsLecture 9 Strings in ScalaLecture 10 Arrays in ScalaLecture 11 Scala CollectionsLecture 12 Lists in ScalaLecture 13 Sets in ScalaLecture 14 Maps in ScalaLecture 15 Tuples in ScalaLecture 16 Options in ScalaLecture 17 Exception Handling in ScalaLecture 18 Pattern MatchingLecture 19 Scala TraitsLecture 20 Scala Files Input OutputLecture 21 Extractors in ScalaProfessionals aspiring to learn the basics of Big Data Analytics,Spark Developer,Analytics Professionals,ETL DevelopersDownload linkrapidgator.net:https://rapidgator.net/file/fc38d5127c1feec21a0f39d15181bfb4/ohbiz.Learn.Apache.Spark.And.Scala.From.Scratch.rar.htmluploadgig.com:https://uploadgig.com/file/download/405d1d6039199a58/ohbiz.Learn.Apache.Spark.And.Scala.From.Scratch.rarnitroflare.com:https://nitroflare.com/view/BF3516285D4BDC1/ohbiz.Learn.Apache.Spark.And.Scala.From.Scratch.rar1dl.net:https://1dl.net/v4kk09cc9n0k/ohbiz.Learn.Apache.Spark.And.Scala.From.Scratch.rar Link to comment Share on other sites More sharing options...
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