Big Data Hadoop Certification Training Course
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Achieving a Big Data Hadoop certification prepares you for success as a Big Data Developer. This Big Data and Hadoop training help you... MSRP: Was: Now: $1,238 (You save ) Brand SIMPLILEARN SKU: BDH UPC: Duration: 3 Months Delivery: eLearning Course Date : Course Date: Optional Current Stock: Quantity: Increase Quantity: Decrease Quantity: Adding to cart… The item has been added Request a Quote Share this course Facebook Email Print Twitter Linkedin Pinterest Home All Courses Big Data Hadoop Certification Training Course Big Data Hadoop Certification Training Course Big Data Hadoop course lets you master the concepts of the Hadoop framework, Big data tools, and methodologies. Achieving a Big Data Hadoop certification prepares you for success as a Big Data Developer. This Big Data and Hadoop training help you understand how the various components of the Hadoop ecosystem fit into the Big Data processing lifecycle. Take this Big Data and Hadoop online training to explore Spark applications, parallel processing, and functional programming. Course Overview Course Content Exam & Certification FAQs About the course The Big Data Hadoop certification training is designed to give you an in-depth knowledge of the Big Data framework using Hadoop and Spark. In this hands-on Hadoop course, you will execute real-life, industry-based projects using Integrated Lab. Upskilling in Big Data and Analytics field is a smart career decision.The global HADOOP-AS-A-SERVICE (HAAS) Market in 2019 was approximately USD 7.35 Billion. The market is expected to grow at a CAGR of 39.3% and is anticipated to reach around USD 74.84 Billion by 2026. Eligibility Big Data Hadoop certification training online course is best suited for IT, Data Management, and Analytics professionals looking to gain expertise in Big Data Hadoop, including Software Developers and Architects, Senior IT professionals, Testing and Mainframe professionals, Business Intelligence professionals, Project Managers, Aspiring Data Scientists, Graduates looking to begin a career in Big Data Analytics. Pre-requisites Professionals entering into Big Data Hadoop certification training should have a basic understanding of Core Java and SQL. If you wish to brush up your Core Java skills, offers a complimentary self-paced course Java essentials for Hadoop as part of the course syllabus. Skills Covered Realtime data processing Functional programming Spark applications Parallel processing Spark RDD optimization techniques Spark SQL Course Content Big Data Hadoop Certification Training Course Lesson 0: Course Introduction 0.1 Course Introduction 0.2 Accessing Practice Lab Lesson 1: Introduction to Big Data and Hadoop 1.1 Introduction to Big Data and Hadoop 1.2 Introduction to Big Data 1.3 Big Data Analytics 1.4 What is Big Data 1.5 Four Vs Of Big Data 1.6 Case Study: Royal Bank of Scotland 1.7 Challenges of Traditional System 1.8 Distributed Systems 1.9 Introduction to Hadoop 1.10 Components of Hadoop Ecosystem: Part One 1.11 Components of Hadoop Ecosystem: Part Two 1.12 Components of Hadoop Ecosystem: Part Three 1.13 Commercial Hadoop Distributions 1.14 Demo: Walkthrough of Cloudlab 1.15 Key Takeaways Knowledge Check Lesson 2: Hadoop Architecture,Distributed Storage (HDFS) and YARN 2.1 Hadoop Architecture Distributed Storage (HDFS) and YARN 2.2 What Is HDFS 2.3 Need for HDFS 2.4 Regular File System vs HDFS 2.5 Characteristics of HDFS 2.6 HDFS Architecture and Components 2.7 High Availability Cluster Implementations 2.8 HDFS Component File System Namespace 2.9 Data Block Split 2.10 Data Replication Topology 2.11 HDFS Command Line 2.12 Demo: Common HDFS Commands HDFS Command Line 2.13 YARN Introduction 2.14 YARN Use Case 2.15 YARN and Its Architecture 2.16 Resource Manager 2.17 How Resource Manager Operates 2.18 Application Master 2.19 How YARN Runs an Application 2.20 Tools for YARN Developers 2.21 Demo: Walkthrough of Cluster Part One 2.22 Demo: Walkthrough of Cluster Part Two 2.23 Key Takeaways Knowledge Check Hadoop Architecture,Distributed Storage (HDFS) and YARN Lesson 3: Data Ingestion into Big Data Systems and ETL 3.1 Data Ingestion into Big Data Systems and ETL 3.2 Data Ingestion Overview Part One 3.3 Data Ingestion 3.4 Apache Sqoop 3.5 Sqoop and Its Uses 3.6 Sqoop Processing 3.7 Sqoop Import Process Assisted Practice: Import into Sqoop 3.8 Sqoop Connectors 3.9 Demo: Importing and Exporting Data from MySQL to HDFS Apache Sqoop 3.9 Apache Flume 3.10 Flume Model 3.11 Scalability in Flume 3.12 Components in Flume's Architecture 3.13 Configuring Flume Components 3.15 Demo: Ingest Twitter Data 3.14 Apache Kafka 3.15 Aggregating User Activity Using Kafka 3.16 Kafka Data Model 3.17 Partitions 3.18 Apache Kafka Architecture 3.19 Producer Side API Example 3.20 Consumer Side API 3.21 Demo: Setup Kafka Cluster 3.21 Consumer Side API Example 3.22 Kafka Connect 3.23 Key Takeaways 3.26 Demo: Creating Sample Kafka Data Pipeline using Producer and Consumer Knowledge Check Data Ingestion into Big Data Systems and ETL Lesson 4: Distributed Processing - MapReduce Framework and Pig 4.1 Distributed Processing MapReduce Framework and Pig 4.2 Distributed Processing in MapReduce 4.3 Word Count Example 4.4 Map Execution Phases 4.5 Map Execution Distributed Two Node Environment 4.6 MapReduce Jobs 4.7 Hadoop MapReduce Job Work Interaction 4.8 Setting Up the Environment for MapReduce Development 4.9 Set of Classes 4.10 Creating a New Project 4.11 Advanced MapReduce 4.12 Data Types in Hadoop 4.13 OutputFormats in MapReduce 4.14 Using Distributed Cache 4.15 Joins in MapReduce 4.16 Replicated Join 4.17 Introduction to Pig 4.18 Components of Pig 4.19 Pig Data Model 4.20 Pig Interactive Modes 4.21 Pig Operations 4.22 Various Relations Performed by Developers 4.23 Demo: Analyzing Web Log Data Using MapReduce 4.24 Demo: Analyzing Sales Data and Solving KPIs using PIG Apache Pig 4.25 Demo: Wordcount 4.26 Key takeaways Knowledge Check Distributed Processing - MapReduce Framework and Pig Lesson 5: Apache Hive 5.1 Apache Hive 5.2 Hive SQL over Hadoop MapReduce 5.3 Hive Architecture 5.4 Interfaces to Run Hive Queries 5.5 Running Beeline from Command Line 5.6 Hive Metastore 5.7 Hive DDL and DML 5.8 Creating New Table 5.9 Data Types 5.10 Validation of Data 5.11 File Format Types 5.12 Data Serialization 5.13 Hive Table and Avro Schema 5.14 Hive Optimization Partitioning Bucketing and Sampling 5.15 Non Partitioned Table 5.16 Data Insertion 5.17 Dynamic Partitioning in Hive 5.18 Bucketing 5.19 What Do Buckets Do 5.20 Hive Analytics UDF and UDAF Assisted Practice: Synchronization 5.21 Other Functions of Hive 5.22 Demo: Real-Time Analysis and Data Filteration 5.23 Demo: Real-World Problem 5.24 Demo: Data Representation and Import using Hive 5.25 Key Takeaways Knowledge Check Apache Hive Lesson 6: NoSQL Databases - HBase 6.1 NoSQL Databases HBase 6.2 NoSQL Introduction Demo: Yarn Tuning 6.3 HBase Overview 6.4 HBase Architecture 6.5 Data Model 6.6 Connecting to HBase HBase Shell 6.7 Key Takeaways Knowledge Check NoSQL Databases - HBase Lesson 7: Basics of Functional Programming and Scala 7.1 Basics of Functional Programming and Scala 7.2 Introduction to Scala 7.3 Demo: Scala Installation 7.3 Functional Programming 7.4 Programming with Scala Demo: Basic Literals and Arithmetic Operators Demo: Logical Operators 7.5 Type Inference Classes Objects and Functions in Scala Demo: Type Inference Functions Anonymous Function and Class 7.6 Collections 7.7 Types of Collections Demo: Five Types of Collections Demo: Operations on List 7.8 Scala REPL Assisted Practice: Scala REPL Demo: Features of Scala REPL 7.9 Key Takeaways Knowledge Check Basics of Functional Programming and Scala Lesson 8: Apache Spark Next Generation Big Data Framework 8.1 Apache Spark Next Generation Big Data Framework 8.2 History of Spark 8.3 Limitations of MapReduce in Hadoop 8.4 Introduction to Apache Spark 8.5 Components of Spark 8.6 Application of In-Memory Processing 8.7 Hadoop Ecosystem vs Spark 8.8 Advantages of Spark 8.9 Spark Architecture 8.10 Spark Cluster in Real World 8.11 Demo: Running a Scala Programs in Spark Shell 8.12 Demo: Setting Up Execution Environment in IDE 8.13 Demo: Spark Web UI 8.14 Key Takeaways Knowledge Check Apache Spark Next Generation Big Data Framework Lesson 9: Spark Core Processing RDD 9.1 Processing RDD 9.1 Introduction to Spark RDD 9.2 RDD in Spark 9.3 Creating Spark RDD 9.4 Pair RDD 9.5 RDD Operations 9.6 Demo: Spark Transformation Detailed Exploration Using Scala Examples 9.7 Demo: Spark Action Detailed Exploration Using Scala 9.8 Caching and Persistence 9.9 Storage Levels 9.10 Lineage and DAG 9.11 Need for DAG 9.12 Debugging in Spark 9.13 Partitioning in Spark 9.14 Scheduling in Spark 9.15 Shuffling in Spark 9.16 Sort Shuffle 9.17 Aggregating Data with Pair RDD 9.18 Demo: Spark Application with Data Written Back to HDFS and Spark UI 9.19 Demo: Changing Spark Application Parameters 9.20 Demo: Handling Different File Formats 9.21 Demo: Spark RDD with Real-World Application 9.22 Demo: Optimizing Spark Jobs Assisted Practice: Changing Spark Application Params 9.23 Key Takeaways Knowledge Check Spark Core Processing RDD Lesson 10: Spark SQL - Processing DataFrames 10.1 Spark SQL Processing DataFrames 10.2 Spark SQL Introduction 10.3 Spark SQL Architecture 10.4 DataFrames 10.5 Demo: Handling Various Data Formats 10.6 Demo: Implement Various DataFrame Operations 10.7 Demo: UDF and UDAF 10.8 Interoperating with RDDs 10.9 Demo: Process DataFrame Using SQL Query 10.10 RDD vs DataFrame vs Dataset Processing DataFrames 10.11 Key Takeaways Knowledge Check Spark SQL - Processing DataFrames Lesson 11: Spark MLLib - Modelling BigData with Spark 11.1 Spark MLlib Modeling Big Data with Spark 11.2 Role of Data Scientist and Data Analyst in Big Data 11.3 Analytics in Spark 11.4 Machine Learning 11.5 Supervised Learning 11.6 Demo: Classification of Linear SVM 11.7 Demo: Linear Regression with Real World Case Studies 11.8 Unsupervised Learning 11.9 Demo: Unsupervised Clustering K-Means Assisted Practice: Unsupervised Clustering K-means 11.10 Reinforcement Learning 11.11 Semi-Supervised Learning 11.12 Overview of MLlib 11.13 MLlib Pipelines 11.14 Key Takeaways Knowledge Check Spark MLLib - Modeling BigData with Spark Lesson 12: Stream Processing Frameworks and Spark Streaming 12.1 Stream Processing Frameworks and Spark Streaming 12.1 Streaming Overview 12.2 Real-Time Processing of Big Data 12.3 Data Processing Architectures 12.4 Demo: Real-Time Data Processing 12.5 Spark Streaming 12.6 Demo: Writing Spark Streaming Application 12.7 Introduction to DStreams 12.8 Transformations on DStreams 12.9 Design Patterns for Using ForeachRDD 12.10 State Operations 12.11 Windowing Operations 12.12 Join Operations stream-dataset Join 12.13 Demo: Windowing of Real-Time Data Processing 12.14 Streaming Sources 12.15 Demo: Processing Twitter Streaming Data 12.16 Structured Spark Streaming 12.17 Use Case Banking Transactions 12.18 Structured Streaming Architecture Model and Its Components 12.19 Output Sinks 12.20 Structured Streaming APIs 12.21 Constructing Columns in Structured Streaming 12.22 Windowed Operations on Event-Time 12.23 Use Cases 12.24 Demo: Streaming Pipeline Spark Streaming 12.25 Key Takeaways Knowledge Check Stream Processing Frameworks and Spark Streaming Lesson 13: Spark GraphX 13.1 Spark GraphX 13.2 Introduction to Graph 13.3 Graphx in Spark 13.4 Graph Operators 13.5 Join Operators 13.6 Graph Parallel System 13.7 Algorithms in Spark 13.8 Pregel API 13.9 Use Case of GraphX 13.10 Demo: GraphX Vertex Predicate 13.11 Demo: Page Rank Algorithm 13.12 Key Takeaways Knowledge Check Spark GraphX 13.14 Project Assistance Practice Projects Car Insurance Analysis Transactional Data Analysis K-Means clustering for telecommunication domain Free Courses Core Java Linux Training How do I unlock my Big Data Hadoop training course completion certificate? Complete 85% of the course and complete one project and one simulation test with a minimum score of 80% FAQs What is Big data? Big data refers to a collection of extensive data sets, including structured, unstructured, and semi-structured data coming from various data sources and having different formats.These data sets are so complex and broad that they can't be processed using traditional techniques. When you combine big data with analytics, you can use it to solve business problems and make better decisions. What is Big Data Hadoop used for? Hadoop is an open-source software environment that stores data and runs on commodity hardware clusters. It offers a large amount of storage, a huge processing capacity, and the ability to conduct nearly unlimited concurrent tasks or jobs. Hadoop course is meant to make you a certified big data practitioner by offering you extensive practical training in the Hadoop Ecosystem. How do I enrol in this online training? You can enrol in this training on our website and make an online payment using any of the following option Visa Credit or Debit Card MasterCard American Express Diners Club PayPal Once payment is received, you will automatically receive a payment receipt and access information via email. Buy Now Close × Our Most Popular Training Courses Java Certification Training Course MSRP: Was: Now: $941 SIMPLILEARN Duration 6 Months View Course Everything you need to become a PRINCE2 Practitioner, all in one place. Course Name Exam Access/Duration Price (Incl Tax) Java Certification Training Course 6 Months MSRP: Was: Now: $941 course info CCBA® Certification Training Course MSRP: Was: Now: $824 SIMPLILEARN Duration 6 Months View Course Everything you need to become a PRINCE2 Practitioner, all in one place. 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