Master in Bigdata Analyst

Big Data means tremendously huge data. Just for you to get an idea how huge it is, on an average day Facebook will have around 700+ terabytes of data, which is roughly 7,15,000+ Gigabytes of data. When calculated for a year this becomes roughly 250+ Petabytes of data (1 petabyte = 1024 Terabytes) i.e. roughly 2,55,500 Terabytes of data or 26,16,32,000 Gigabytes of data. Now imagine storing and processing all this data (more than 1000 Exabyte; 1 Exabyte = 1024 Petabytes) along with data from other such sources (which could all add up to zettabyte or yottabyte of data) in a single open source framework and that is Hadoop for you. This data could consist of more than Trillions of data of Billions of people from social media, banks, internet, mobile data etc. Hadoop Distributed Files System – HDFS (a software of Apache Software Foundation) provides software frameworks for storage and processing of Big Data. Learn more with SMEClabs, BIG DATA APACHE HADOOP taught in detail.

Bigdata Apache Hadoop Certification Training

Key Highlights

Bigdata Apache Hadoop Spark Scala Course Topics to be Covered:

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What you’ll learn

Learning Outcomes

Who this course is for:

This Bigdata Apache Hadoop Spark Scala course from SMEClabs will make you ready to switch careers on big data Hadoop and spark. After watching this, you will understand about Hadoop, HDFS, YARN, Map reduce, python, pig, hive, oozie, sqoop, flume, HBase, No SQL, Spark, Spark sql, Spark Streaming.

Why Spark?

Apache Spark is an open-source cluster computing framework for Hadoop community clusters. It qualifies to be one of the best data analytics and processing engines for large-scale data with its unmatchable speed, ease of use, and sophisticated analytics. Following are the advantages and features that make Apache Spark a crossover hit for operational as well as investigative analytics:

Bigdata Apache Hadoop Spark Scala Training Syllabus:
  • Linux (Ubuntu/Centos) – Tips and Tricks
  • Basic(core) Java Programming Concepts – OOPS
  • Learning Objectives: In this module, you will understand what Big Data is, the limitations of the traditional solutions for Big Data problems, how Hadoop solves those Big Data problems, Hadoop Ecosystem, Hadoop Architecture, HDFS, Anatomy of File Read and Write & how MapReduce works.
  • Topics:
    • Introduction to Big Data & Big Data Challenges
    • Limitations & Solutions of Big Data Architecture
    • Hadoop & its Features
    • Hadoop Ecosystem
    • Hadoop 2.x Core Components
    • Hadoop Storage: HDFS (Hadoop Distributed File System)
    • Hadoop Processing: MapReduce Framework
    • Different Hadoop Distributions
  •  
  • Hadoop 2.x Architecture
  • Typical workflow
  • HDFS Commands
  • Writing files to HDFS
  • Reading files from HDFS
  • Rack awareness
  • Hadoop daemons
  •  
  • Before MapReduce
  • MapReduce overview
  • Word count problem
  • Word count flow and solution
  • MapReduce flow
  •  
  • Data Types
  • File Formats
  • Explain the Driver, Mapper and Reducer code
  • Configuring development environment – Eclipse
  • Writing unit test
  • Running locally
  • Running on cluster
  • Hands on exercises
  •  
  • Anatomy of MapReduce job run
  • Job submission
  • Job initialization
  • Task assignment
  • Job completion
  • Job scheduling
  • Job failures
  • Shuffle and sort
  • Hands on exercises
  •  
  • File Formats – Sequence Files
  • Compression Techniques
  • Input Formats – Input splits & records, text input, binary input
  • Output Formats – text output, binary output, lazy output
  • Hands on exercises
  •  
  • Counters
  • Side data distribution
  • MapReduce combiner
  • MapReduce partitioner
  • MapReduce distributed cache
  • Hands exercises
  •  
  • Hive Architecture
  • Types of Metastore
  • Hive Data Types
  • HiveQL
  • File Formats – Parquet, ORC, Sequence and Avro Files Comparison
  • Partitioning & Bucketing
  • Hive JDBC Client
  • Hive UDFs
  • Hive Serdes
  • Hive on Tez
  • Hands-on exercises
  • Integration with Tableau
  •  
  • Flume Architecture
  • Flume Agent Setup
  • Types of sources, channels, sinks Multi Agent Flow
  • Hands-on exercises
  • Introduction to Apache Pig
  • MapReduce vs Pig
  • Pig Components & Pig Execution
  • Pig Data Types & Data Models in Pig
  • Pig Latin Programs
  • Shell and Utility Commands
  • Pig UDF & Pig Streaming
  • Testing Pig scripts with Punit
  • Aviation use-case in PIG
  • Pig Demo of Healthcare Dataset
  •  
  • HBase Data Model
  • HBase Shell
  • HBase Client API
  • Hive Data Loading Techniques
  • Apache Zookeeper Introduction
  • ZooKeeper Data Model
  • Zookeeper Service
  • HBase Bulk Loading
  • Getting and Inserting Data
  • HBase Filters
  •  
  • Sqoop Architecture
  • Sqoop Import Command Arguments, Incremental Import
  • Sqoop Export
  • Sqoop Jobs
  • Hands-on exercises
  •  
  • Spark Basics
  • What is Apache Spark?
  • Spark Installation
  • Spark Configuration
  • Spark Context
  • Using Spark Shell
  • Resilient Distributed Datasets (RDDs) – Features, Partitions, Tuning Parallelism
  • Functional Programming with Spark
  • ark Basics
  • What is Apache Spark?
  • Spark Installation
  • Spark Configuration
  • Spark Context
  • Using Spark Shell
  • Resilient Distributed Datasets (RDDs) – Features, Partitions, Tuning Parallelism
  • Functional Programming with Spark
  •  
  • Oozie
  • Oozie Components
  • Oozie Workflow
  • Scheduling Jobs with Oozie Scheduler
  • Demo of Oozie Workflow
  • Oozie Coordinator
  • Oozie Commands
  • Oozie Web Console
  • Oozie for MapReduce
  • Combining flow of MapReduce Jobs
  • Hive in Oozie
  • Hadoop Project Demo
  • Hadoop Talend Integration
  •  
  • Log File Analysis covering Flume, HDFS, MR/Pig, Hive, Tableau
  • Crime Data Analysis Covering Oozie, Sqoop, HDFS, Hive, HBase, RestFul Client.
  • Hadoop Use Cases in Insurance Domain
  • Hadoop Use Cases in Retail Domain
  •  

Detailed Syllabus

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