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Hadoop Developer / Analyst / SPARK + SCALA / Hadoop (Java + Non- Java) Track
HADOOP DEV + SPARK & SCALA + NoSQL + Splunk + HDFS (Storage) + YARN (Hadoop Processing Framework) + MapReduce using Java (Processing Data) + Apache Hive + Apache Pig + HBASE (Real NoSQL ) + Sqoop + Flume + Oozie + Kafka With ZooKeeper + Cassandra + MongoDB + Apache Splunk
Best Bigdata Hadoop Training with 2 Real-time Projects with 1 TB Data set
IT is a big myth that if a guy don’t know Java then he can’t learn Hadoop. The truth is that Only Map Reduce framework needs Java except Map Reduce all other components are based on different terms like Hive is similar to SQL, HBase is similar to RDBMS and Pig is script based.
Only MR requires Java but there are so many organizations who started hiring on specific skill set also like HBASE developer or Pig and Hive specific requirements. Knowing MapReuce also is just like become all-rounder in Hadoop for any requirement.
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HADOOP DEV + SPARK & SCALA + NoSQL + Splunk + HDFS (Storage) + YARN (Hadoop Processing Framework) + MapReduce using Java (Processing Data) + Apache Hive + Apache Pig + HBASE (Real NoSQL ) + Sqoop + Flume + Oozie + Kafka With ZooKeeper + Cassandra + MongoDB + Apache Splunk
Why we need Hadoop
Data centers and Hadoop Cluster overview
Overview of Hadoop Daemons
Hadoop Cluster and Racks
Learning Linux required for Hadoop
Hadoop ecosystem tools overview
Understanding the Hadoop configurations and Installation.
HDFS
HDFS Daemons – Namenode, Datanode, Secondary Namenode
Hadoop FS and Processing Environment’s UIs
Fault Tolerant
High Availability
Block Replication
How to read and write files
Hadoop FS shell commands
YARN
YARN Daemons – Resource Manager, NodeManager etc.
Job assignment & Execution flow
The introduction of MapReduce.
MapReduce Architecture
Data flow in MapReduce
Understand Difference Between Block and InputSplit
Role of RecordReader
Basic Configuration of MapReduce
MapReduce life cycle
How MapReduce Works
Writing and Executing the Basic MapReduce Program using Java
Submission & Initialization of MapReduce Job.
File Input/Output Formats in MapReduce Jobs
Text Input Format
Key Value Input Format
Sequence File Input Format
NLine Input Format
Joins
Map-side Joins
Reducer-side Joins
Word Count Example(or) Election Vote Count
Will cover five to Ten Map Reduce Examples with real time data.
Data warehouse basics
OLTP vs OLAP Concepts
Hive
Hive Architecture
Metastore DB and Metastore Service
Hive Query Language (HQL)
Managed and External Tables
Partitioning & Bucketing
Query Optimization
Hiveserver2 (Thrift server)
JDBC , ODBC connection to Hive
Hive Transactions
Hive UDFs
Working with Avro Schema and AVRO file format
Hands on Multiple Real Time datasets.
Apache Pig
Advantage of Pig over MapReduce
Pig Latin (Scripting language for Pig)
Schema and Schema-less data in Pig
Structured , Semi-Structure data processing in Pig
Pig UDFs
HCatalog
Pig vs Hive Use case
Hands On Two more examples daily use case data analysis in google. And Analysis on Date time dataset
Introduction to HBASE
Basic Configurations of HBASE
Fundamentals of HBase
What is NoSQL?
HBase Data Model
Table and Row.
Column Family and Column Qualifier.
Cell and its Versioning
Categories of NoSQL Data Bases
Key-Value Database
Document Database
Column Family Database
HBASE Architecture
HMaster
Region Servers
Regions
MemStore
Store
SQL vs. NOSQL
How HBASE is differed from RDBMS
HDFS vs. HBase
Client-side buffering or bulk uploads
HBase Designing Tables
HBase Operations
Get
Scan
Put
Delete
Live Dataset
Sqoop commands
Sqoop practical implementation
Importing data to HDFS
Importing data to Hive
Exporting data to RDBMS
Sqoop connectors
Flume commands
Configuration of Source, Channel and Sink
Fan-out flume agents
How to load data in Hadoop that is coming from web server or other storage
How to load streaming data from Twitter data in HDFS using Hadoop
Oozie
Action Node and Control Flow node
Designing workflow jobs
How to schedule jobs using Oozie
How to schedule jobs which are time based
Oozie Conf file
Scala
Syntax formation, Datatypes , Variables
Classes and Objects
Basic Types and Operations
Functional Objects
Built-in Control Structures
Functions and Closures
Composition and Inheritance
Scala’s Hierarchy
Traits
Packages and Imports
Working with Lists, Collections
Abstract Members
Implicit Conversions and Parameters
For Expressions Revisited
The Scala Collections API
Extractors
Modular Programming Using Objects
Spark
Architecture and Spark APIs
Spark components
Spark master
Driver
Executor
Worker
Significance of Spark context
Concept of Resilient distributed datasets (RDDs)
Properties of RDD
Creating RDDs
Transformations in RDD
Actions in RDD
Saving data through RDD
Key-value pair RDD
Invoking Spark shell
Loading a file in shell
Performing some basic operations on files in Spark shell
Spark application overview
Job scheduling process
DAG scheduler
RDD graph and lineage
Life cycle of spark application
How to choose between the different persistence levels for caching RDDs
Submit in cluster mode
Web UI – application monitoring
Important spark configuration properties
Spark SQL overview
Spark SQL demo
SchemaRDD and data frames
Joining, Filtering and Sorting Dataset
Spark SQL example program demo and code walk through
What is Kafka
Cluster architecture With Hands On
Basic operation
Integration with spark
Integration with Camel
Additional Configuration
Security and Authentication
Apache Kafka With Spring Boot Integration
Running
Usecase
Introduction & Installing Splunk
Play with Data and Feed the Data
Searching & Reporting
Visualizing Your Data
Advanced Splunk Concepts
Introduction of NoSQL
What is NOSQL & N0-SQL Data Types
System Setup Process
MongoDB Introduction
MongoDB Installation
DataBase Creation in MongoDB
ACID and CAP Theorum
What is JSON and what all are JSON Features?
JSON and XML Difference
CRUD Operations – Create , Read, Update, Delete
Cassandra Introduction
Cassandra – Different Data Supports
Cassandra – Architecture in Detail
Cassandra’s SPOF & Replication Factor
Cassandra – Installation & Different Data Types
Database Creation in Cassandra
Tables Creation in Cassandra
Cassandra Database and Table Schema and Data
Update, Delete, Insert Data in Cassandra Table
Insert Data From File in Cassandra Table
Add & Delete Columns in Cassandra Table
Cassandra Collections
IT folks who want to change their profile in a most demanding technology which is in demand by almost all clients in all domains because of below mentioned reasons-
+91 8055223360
Course completion certificate and Global Certifications are part of our all Master Program
Course completion certificate and Global Certifications are part of our all Master Program
DataQubez University creates meaningful big data & Data Science certifications that are recognized in the industry as a confident measure of qualified, capable big data experts. How do we accomplish that mission? DataQubez certifications are exclusively hands on, performance-based exams that require you to complete a set of tasks. Demonstrate your expertise with the most sought-after technical skills. Big data success requires professionals who can prove their mastery with the tools and techniques of the Hadoop stack. However, experts predict a major shortage of advanced analytics skills over the next few years. At DataQubez, we’re drawing on our industry leadership and early corpus of real-world experience to address the big data & Data Science talent gap.
How To Become Certified Big Data – Hadoop Developer
Certification Code – DQCP – 502
Certification Description – DataQubez Certified Professional Big Data – Hadoop Developer
Define and deploy a rack topology script, Change the configuration of a service using Apache Hadoop, Configure the Capacity Scheduler, Create a home directory for a user and configure permissions, Configure the include and exclude DataNode files
Restart an Cluster service, View an application’s log file, Configure and manage alerts Troubleshoot a failed job
Configure NameNode, Configure ResourceManager, Copy data between two clusters, Create a snapshot of an HDFS directory, Recover a snapshot, Configure HiveServer2
Import data from a table in a relational database into HDFS, Import the results of a query from a relational database into HDFS, Import a table from a relational database into a new or existing Hive table, Insert or update data from HDFS into a table in a relational database, Given a Flume configuration file, start a Flume agent, Given a configured sink and source, configure a Flume memory channel with a specified capacity
Write and execute a Pig script, Load data into a Pig relation without a schema, Load data into a Pig relation with a schema, Load data from a Hive table into a Pig relation, Use Pig to transform data into a specified format, Transform data to match a given Hive schema, Group the data of one or more Pig relations, Use Pig to remove records with null values from a relation, Store the data from a Pig relation into a folder in HDFS, Store the data from a Pig relation into a Hive table, Sort the output of a Pig relation, Remove the duplicate tuples of a Pig relation, Specify the number of reduce tasks for a Pig MapReduce job, Join two datasets using Pig, Perform a replicated join using Pig
Write and execute a Hive query, Define a Hive-managed table, Define a Hive external table, Define a partitioned Hive table, Define a bucketed Hive table, Define a Hive table from a select query, Define a Hive table that uses the ORCFile format, Create a new ORCFile table from the data in an existing non-ORCFile Hive table, Specify the storage format of a Hive table Specify the delimiter of a Hive table, Load data into a Hive table from a local directory Load data into a Hive table from an HDFS directory, Load data into a Hive table as the result of a query, Load a compressed data file into a Hive table, Update a row in a Hive table, Delete a row from a Hive table, Insert a new row into a Hive table, Join two Hive tables, Set a Hadoop or Hive configuration property from within a Hive query.
Frame big data analysis problems as Apache Spark scripts, Optimize Spark jobs through partitioning, caching, and other techniques, Develop distributed code using the Scala programming language, Build, deploy, and run Spark scripts on Hadoop clusters, Transform structured data using SparkSQL and DataFrames
Radical Technologies is truly progressing and offer best possible services. And recognition towards Radical Technologies is increasing steeply as the demand is growing rapidly.
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It was a pleasent experience analyzing HADOOP workload using Apache SPARK & SCALA. It enhances the processing speed and efficiency with spark's in-memory computational abilities.
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Course Provider Name: Radical Technologies
Course Provider URL: https://radicals.in/
Radical Technologies is a recognized leader in training of Administrative and Soft ware Development courses since 1995 to empower IT individuals with competitive advantage of exploiting untapped jobs in IT sector