Apache Spark with Scala Online Training
- Description
- Curriculum
- Reviews
Apache Spark with Scala online training is created to help you to master, with Scala and the Spark Ecosystem, which includes RDD, SQL, and MLlib.
Also, this Apache Spark training is live, instructor-led & helps you master key Apache Spark concepts, with hands-on demonstrations.
This Apache course is fully immersive where you can learn and interact with the instructor and your peers.
This certification training course is ideal for professionals aspiring for a career in the field of real-time big data analytics, analytics professionals, research professionals, IT developers and testers, data scientists, BI and reporting professionals, and students who want to gain a thorough understanding of Apache Spark.
- Target audience
- Students
- Big Data and Hadoop Professionals
- IT professionals
Prerequisites
Professionals with experience in IT and Students eager to learn can undergo this training.
Why Should I choose Lara Online Training?
- We provide Very in-depth course material with Real Time Scenarios for each topic with its Solutions for, Apache with Scala Online Training
- At Lara, we provide case studies in real-time applications with a professional explanation.
- We do Schedule the sessions based upon your comfort by our Highly Qualified Trainers and Real-time Experts.
- Class recordings are available immediately after the session for future reference.
- We do Normal Track, Fast Track, and Weekend Batches for Apache Spark with Scala Online Training.
- We assist you with cost Effective and Flexible Payment Schemes.
- At Lara, we provide Placement Assistance.
- We provide Assessment and Mock Interviews
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1What is Big Data?Text lesson
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2Big Data Customer ScenariosText lesson
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3Limitations and Solutions of Existing Data Analytics Architecture with Uber Use CaseText lesson
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4How Hadoop Solves the Big Data Problem?Text lesson
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5What is Hadoop?Text lesson
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6Hadoop’s Key CharacteristicsText lesson
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7Hadoop Ecosystem and HDFSText lesson
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8Hadoop Core ComponentsText lesson
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9Rack Awareness and Block ReplicationText lesson
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10YARN and its AdvantageText lesson
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11Hadoop Cluster and its ArchitectureText lesson
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12Hadoop: Different Cluster ModesText lesson
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13Hadoop Terminal CommandsText lesson
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14Big Data Analytics with Batch & Real-time ProcessingText lesson
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15Why Spark is needed?Text lesson
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16What is Spark?Text lesson
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17How Spark differs from other frameworks?Text lesson
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18Spark at Yahoo!Text lesson
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19What is Scala?Text lesson
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20Why Scala for Spark?Text lesson
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21Scala in other FrameworksText lesson
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22Introduction to Scala REPLText lesson
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23Basic Scala OperationsText lesson
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24Variable Types in ScalaText lesson
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25Control Structures in ScalaText lesson
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26Foreach loop, Functions and ProceduresText lesson
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27Collections in Scala- ArrayText lesson
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28ArrayBuffer, Map, Tuples, Lists, and moreText lesson
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29Functional ProgrammingText lesson
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30Higher Order FunctionsText lesson
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31Anonymous FunctionsText lesson
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32Class in ScalaText lesson
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33Getters and SettersText lesson
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34Custom Getters and SettersText lesson
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35Properties with only GettersText lesson
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36Auxiliary Constructor and Primary ConstructorText lesson
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37SingletonsText lesson
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38Extending a ClassText lesson
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39Overriding MethodsText lesson
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40Traits as Interfaces and Layered TraitsText lesson
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41Spark’s Place in Hadoop EcosystemText lesson
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42Spark Components & its ArchitectureText lesson
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43Spark Deployment ModesText lesson
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44Introduction to Spark ShellText lesson
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45Writing your first Spark Job Using SBTText lesson
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46Submitting Spark JobText lesson
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47Spark Web UIText lesson
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48Data Ingestion using SqoopText lesson
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49Challenges in Existing Computing MethodsText lesson
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50Probable Solution & How RDD Solves the ProblemText lesson
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51What is RDD, It’s Operations, Transformations & ActionsText lesson
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52Data Loading and Saving Through RDDsText lesson
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53Key-Value Pair RDDsText lesson
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54Other Pair RDDs, Two Pair RDDsText lesson
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55RDD LineageText lesson
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56RDD PersistenceText lesson
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57WordCount Program Using RDD ConceptsText lesson
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58RDD Partitioning & How It Helps Achieve ParallelizationText lesson
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59Passing Functions to SparkText lesson
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60Need for Spark SQLText lesson
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61What is Spark SQL?Text lesson
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62Spark SQL ArchitectureText lesson
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63SQL Context in Spark SQLText lesson
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64User Defined FunctionsText lesson
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65Data Frames & DatasetsText lesson
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66Interoperating with RDDsText lesson
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67JSON and Parquet File FormatsText lesson
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68Loading Data through Different SourcesText lesson
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69Spark – Hive IntegrationText lesson
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70Why Machine Learning?Text lesson
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71What is Machine Learning?Text lesson
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72Where Machine Learning is Used?Text lesson
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73Face Detection: USE CASEText lesson
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74Different Types of Machine Learning TechniquesText lesson
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75Introduction to MLlibText lesson
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76Features of MLlib and MLlib ToolsText lesson
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77Various ML algorithms supported by MLlibText lesson
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81Need for KafkaText lesson
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82What is Kafka?Text lesson
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83Core Concepts of KafkaText lesson
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84Kafka ArchitectureText lesson
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85Where is Kafka Used?Text lesson
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86Understanding the Components of Kafka ClusterText lesson
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87Configuring Kafka ClusterText lesson
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88Kafka Producer and Consumer Java APIText lesson
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89Need of Apache FlumeText lesson
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90What is Apache Flume?Text lesson
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91What is Apache Flume?Text lesson
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92Basic Flume ArchitectureText lesson
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93Flume SourcesText lesson
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94Flume SinksText lesson
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95Flume ChannelsText lesson
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96Flume ConfigurationText lesson
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97Integrating Apache Flume and Apache KafkaText lesson
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98Drawbacks in Existing Computing MethodsText lesson
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99Why Streaming is Necessary?Text lesson
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100What is Spark Streaming?Text lesson
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101Spark Streaming FeaturesText lesson
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102Spark Streaming WorkflowText lesson
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103How Uber Uses Streaming DataText lesson
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104Streaming Context & DStreamsText lesson
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105Transformations on DStreamsText lesson
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106Describe Windowed Operators and Why it is UsefulText lesson
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107Important Windowed OperatorsText lesson
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108Slice, Window and ReduceByWindow OperatorsText lesson
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109Stateful OperatorsText lesson
Excellent rating
Based on 16 reviewsTrustindex verifies that the original source of the review is Google. It was great working and learning experience with krish on scripting issue. Communication with Management is very responsive and helpful and special thanks to krish for his patience. sujith BTrustindex verifies that the original source of the review is Google. Trainer explained well.completed all topics and helped in technical issues Laxmi MTrustindex verifies that the original source of the review is Google. Gulshan who is the trainer is very good at giving insights of the RPA and he is good on clearing all my doubts. The institute also helped me find the right trainer Srivalli Anand MakkapatiTrustindex verifies that the original source of the review is Google. Thank you so much, Mr. Kishore, for the online IT training. That was a great experience with you. I have learned a lot from you. You are a very knowledgeable person and helpful in answering questions. I appreciate your replies to me promptly through text messages or email. I understand you overall. Asif JafriTrustindex verifies that the original source of the review is Google. The explanation is excellent. Trainer explained with live examples and secenarios Eswar vakadaTrustindex verifies that the original source of the review is Google. Excellent experience. Kishore is a valuable resource, Lara online training should be proud of him. Nasir JavedTrustindex verifies that the original source of the review is Google. that was a best practice and I have learned alot new things. thank you kishore Muscle PassionTrustindex verifies that the original source of the review is Google. Thank you so much for give us such a great training I learn a lot and it adds value to my knowledge If got a job I should be able to do it without any trouble thanks. Mohammad Zahid_Qureshi