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Hadoop Development in Nal Stop
- 14k+ Students
- 15h 20min
- 4.7 Reviews
Big Data Hadoop Training in Nal Stop is available in different training formats. In our first format we provide hadoop training in classroom. In second format we offer hadoop online training, with the help of webinars, with high definition video and audio capability servers. We have many training institutes distributed through the globe. In India we are offering big data and hadoop training in various areas of pune such as Pimple Saudagar,Kharadi,Vimannagar,Hadapsar,Nal Stop.
Course Features
1 on 1 Sessions
VM For Practice
Video Lectures
Free Cloud Access
Free Cloud Storage
Complete Modules
24X7 Call Support
CCDH Preparation
Multiple Batches
FAQ
We will provide you complete support for java and linux.
We have designed our syllabus in such a way that it fits the suitability for CloudEra certified developer certification.
We have multiple batches running simultaneously, so if u misses a lecture u can attend it in next lecture.
We have designed our syllabus in such a way that it fits the suitability for HortonWorks certified developer certification.
Yes, we have multiple centers.
Your profile would be evaluated by experts, your resume would be rated and you would start getting calls after completion of your module.
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Syllabus
Hadoop Fundamentals
- What is Bigdata?
- Evolution of Bigdata
- Types of Data and their Significance
- Need for Bigdata Analytics
- Why Bigdata with Hadoop?
- History of Hadoop
- Why Hadoop is in demand in market nowadays?
- Limitations of SQL based Tools
- Hadoop Nodes
- Hadoop Rack
- Hadoop Cluster
- Architecture of Hadoop
- Characteristics of Namenode
- Workaround with Datanodes
- Significance of JobTracker and Tasktrackers
- Hase co-ordination with JobTracker
- Secondary Namenode usage and Workaround
- Hadoop Releases and their Significance
- Introduction to Hadoop Release-1
- Hadoop Daemons in Hadoop Release-1
- Introduction to Hadoop Release-2
- Hadoop Daemons in Hadoop Release-2
- Hadoop Cluster Demo
- Hadoop 2.x Cluster Architecture
- A Typical Production Hadoop Cluster
- Hadoop Cluster Modes
- Hadoop 2.x Configuration Files
- Single node cluster and Multi node cluster setup
- Hadoop installation
- Introduction to Hadoop FS and Processing Environment’s UIs
- How to read and write files
- Basic Unix commands for Hadoop
- Hadoop FS shell
- Hadoop releases practical
- Hadoop daemons practical
- Common Hadoop Shell Commands
- An Overview of Hadoop Administration
- How Hadoop is getting two categories Projects
- New projects on Hadoop
- Hadoop Storage – HDFS (Hadoop Distributed file system)
- Hadoop Processing Framework (Map Reduce / YARN)
- Alternates of Map Reduce
- Why NOSQL is in much demand instead of SQL
- Distributed warehouse for HDFS
- YARN Architecture
- Significance of Scalability of Operation
- Use cases where not to use Hadoop
- Use cases where Hadoop Is used
- Facebook,Twitter,Snapdeal, Flipkart
Hadoop JAVA API
- Hadoop Classes
- What is MapReduceBase?
- Mapper Class and its Methods
- What is Partitioner and types
- MapReduce Use Cases
- Traditional way VS MapReduce way
- Significance of MapReduce
- Hadoop 2. X MapReduce Architecture
- Hadoop 2. MapReduce Program
- Understanding Input Splits
- Relationship between Input Splits and HDFS Blocks
- MapReduce: Combiner & Partitioner
- Hadoop specific Data types
- Working on Unstructured Data Analytics
- What is an Iterator and its usage techniques
- Types of Mappers and Reducers
- What is Output collector and its Significance
- Workaround with Joining of datasets
- Complications with MapReduce
- Mapreduce Anatomy
- Anagram example,Teragen Example,Terasort Example
- WordCount Example
- Working with multiple mappers
- Working with weather data on multiple Data nodes in a Fully distributed Architecture
- Use Cases where MapReduce anatomy fails
- Advanced MapReduce
- Counters
- Distributed Cache
- MRunit
- Joins in MapReduce
- Reduce Side Join
- Replicated Join
- Composite Join
- Cartesian Product
- Custom Input Format
- Sequence Input Format
- XML File Parsing using MapReduce
- Interview questions based on JAVA MapReduce
Working with Pig Latin - (Fundamentals)
- Introduction to Pig Latin
- History and Evolution of Pig Latin
- Why Pig is used only with Bigdata
- MapReduce VS Pig
- Pig Architecture and Overview of Compiler and Execution Engine
- Programming Structure in Pig
- Pig Running Modes
- Pig Components
- Pig Execution
- Pig Release and Significance of Bugfixes
- Pig Specific Datatypes
- Complex Datatypes
- Bags, Tuples, Fields
- Pig Specific Methods
- Comparison between Yahoo Pig & Facebook Hive
- Shell and Utility Commands
- Working with Grunt Shell
- Grunt commands: 17 in number
- Pig Latin: Relational Operators
- Pig Latin: File Loaders
- Pig Latin: Group Operator
- Cogroup Operator
- Joins and Cogroup
- Union
- Understanding Diagnostic Operators
- Specialized Joins in Pig
- Built in Functions
- Eval Function
- Load and Store Functions
- Math Function
- String Function
- Date Function
- Pig UDF
- Piggybank
- Parameter Substitution
- Pig Streaming
- Pig Use Cases: Aviation and Healthcare
- Pig Data Input Techniques for flatfiles
- Flatfiles: Comma separated, Tab delimited, and fixed width
- Working with Schemaless Approach
- How to attach Schema to a file/table in Pig
- Schema referencing for similar Tables and Files
- Working with Delimiters
Working with Pig Latin - II (Advanced)
- Working with Binary Storage and Text Loader
- Bigdata Operations and Read write Analogy
- Filtering Datasets
- Filtering rows with specific condition
- Filtering rows with multiple conditions
- Filtering rows with String Based Conditions
- Sorting DataSets
- Sorting rows with Specific column or columns
- Multi level Sort
- Analogy of a Sort Operation
- Grouping Datasets and Co-grouping data
- Joining DataSets
- Types of Joins supported by Pig Latin
- Aggregate Operations like average, sum, min, max, count
- Flatten Operator
- Creating a UDF (USER DEFINED FUNCTION) using java
- Calling UDF from a Pig Script
- Data validation Scripts
Working with Hive
- Overview of Hive
- Background of Hive
- Hive VS Pig
- Installation and Configuration
- Interacting HDFS using HIVE
- Map Reduce Programs through HIVE
- Hive Architecture and Components
- Hive Commands
- Loading, Filtering, Grouping
- What is Meta Storage and Meta Store
- Derby Database
- HQL
- DDL, DML, and other Sub Languages of Hive
- Data types in Hive
- Partitions and Buckets
- Hive Tables: Managed and External
- Importing Data
- Querying Data
- Managing Outputs
- Hive Scipts
- Hive UDF
- Hive Operators
- Hive Joins, Unions, and Groups
- Sample Programs in Hive
- Alter and Delete in Hive
- Partition in Hive
- Indexing
- Industry Specific Configuration of Hive Parameters
- Authentication & Authorization
- Statistics with Hive
- Archiving in Hive
- Hands-on exercise
Advanced Hive
- Understanding Hive Releases
- Hive and OLTP
- OLAP in Hive
- Hive QL: Joining Tables
- Dynamic Partitioning & Bucketing
- Serialization and Deserialization
- Custom Map/Reduce Scripts
- Hive Indexes and Views
- Hive Query Optimizers
- Hive Architecture
- Understanding Thrift Server
- User-Defined Functions
- Hue Interface for Hive
- Analyzing Data with Hive Script
- Difference between Hive and Impala
- UDFs in Hive
- Complex Use cases in Hive
Hadoop On Amazon Cloud
- Introduction to Cloud Infrastructure
- Amazon SaaS, Paas and IaaS
- Creating EC2 Instance for Processing
- Creating S3 Buckets
- Deploying Data on to the Cloud
- Choosing size of our instance
- Configuration of EMR Instance
- Creating a virtual cluster on Amazon
- Deploying project and getting stats
Sqoop
- An Overview of Sqoop
- Sqoop Real-life Connect
- Sqoop and its Uses
- Advantages of Sqoop
- Sqoop Processing
- Sqoop Execution Process
- Importing Data Using Sqoop
- Sqoop Import Process
- How to Import data to Hive and HBase?
- How to Export Data from Hadoop using Sqoop?
- Sqoop Alternative
- Sqoop Connector
HBase & Zookeeper
- Introduction to HBase
- HBase VS RDBMS
- HBase Components
- Hbase Architecture
- HBase Shell
- HBase Client API
- Data Loading Techniques
- Run Modes & Configuration
- HBase Cluster Deployment
- Regionservers and their implementation
- Client API’s and their features
- How messaging system works
- Columns and column families
- Configuring hbase-site.xml
- Available Client
- Loading Hbase with semi-structured data
- Internal data storage in hbase
- Timestamps
- Creating table with column families
- MapReduce Integration.
- HBase: Advanced Usage, Schema Design
- Load data from pig to hbase
- Zookeeper Data Model
- Zookeeper Service
- Challenges faced in Distributed Applications
- Coordination
- Znode
- Client API Functions
- Bulk Loading
- Receiving and Inserting Data
- Filters in HBase
- Sqoop architecture
- Data Import and export in SQOOP
- Deploying quorum and configuration throughout the Cluster
Flume
- Introduction to Flume
- Introduction to Apache Flume
- Flume Model
- Flume Goals
- Scalability in Flume
- Flume Data Integration
- Flume Installation on Single Node and Multinode Cluster
- Flume Architecture and various Components
- Data Sources: Types and Variants
- Data Target: Types and Variants
- Deploying an agent onto a single node cluster
- Problems associated with Flume
- Interview questions based on Flume
Oozie and Hue
- Introduction to Apache Oozie
- Oozie: Components
- Oozie: Workflow
- Scheduling with Oozie
- Hands-on Training on Oozie Workflow
- Oozie Coordinator
- Oozie Commands
- Oozie Web Console
- Oozie for MapReduce
- Hive in Oozie
- An Overview of Hue
- Hue in Real-time Scenarios
- Use Cases in Hue
MongoDB
- Understanding MongoDB
- NoSQL Databases
- JSON and BSON
- Vertical and Horizontal Scaling
- Data Types
- MongoDB Tools
- Collection and Database
- Schema Design and Modeling
- CRUD Operations in MongoDB
- Indexing and Aggregation
- Replication and Sharding
- MongoDB Cluster Operations
Yarn Architecture
- Introduction to YARN and MR2 daemons
- Active and Standby Namenodes
- Resource Manager and Application Master
- Node Manager
- Container Objects and Container
- Namenode Federation
- Cloudera Manager and Impala
- Load balancing in cluster with namenode federation
- Architectural differences between Hadoop 1.0 and 2.0
Basics of JAVA for Hadoop
- The Java Virtual Machine
- Variables
- Data types
- Constructs: Conditional and Looping
- Types: Wrapper classes
- Object-Oriented JAVA
- Fields and Methods
- Constructors
- Overloading methods
- Garbage collection
- Nested classes
- Overriding methods
- Polymorphism
- Making methods and classes final
- Abstract classes and methods
- Interfaces
- Threads
- Classes
- The I/O Package
- JAVA Security
Spark Ecosystem and BigData
- What and How of Distributed Systems
- What are New Generation Distributed Systems
- What is Big Data and its Limitations
- What are the Limitations of MapReduce in Hadoop
- Processing: Batch and Real-time Big Data Analytics
- Hadoop Ecosystem Overview
- Understanding Apache Spark
- Evolution and Features of Spark
- Spark Ecosystem
- Modes of Spark
- Overview of Spark on a cluster
- Spark Standalone Cluster
- Spark Web User Interface.
- Language Flexibility in Spark
- Architecture of Spark
- Spark and Big Data
- APIs in Spark
- Additional Benefits of Spark
- Various Tasks of Spark on a Cluster
- Apache Spark and Hadoop Ecosystem
Scala and Spark
- Defining Scala
- Features of Scala
- Scala and Spark interdependency
- How to use Scala in other Frameworks
- What is Scala REPL
- Various Scala Operations
- Basic Data Types in Scala
- Understanding Basic Literals
- What are Operators
- Various Types of Operators
- What is Arithmetic Operator
- How to use Logical Operator
- Control Structures in Scala
- Functions and Procedures
- Anonymous Functions
- Objects and Classes
- Collections in Scala: Mutable vs Immutable Collection
- Array
- Array Buffer
- Map and Maps Operations
- Pattern Matching
- Tuples
- Lists
- Streams
Programming Techniques in Scala
- What is a Class in Scala
- Understanding Getters and Setters
- What are Custom Getters and Setters
- General Properties of Getters
- What is an Auxiliary Constructor
- What is a Primary Constructor
- Defining Singletons
- What are Companion Objects
- How to extend a Class
- Overriding Methods
- Traits as Interfaces and Layered Traits
Spark Shell and PySpark
- What is Spark Shell?
- How to create a Spark Context?
- How to load a file in Shell?
- Operations on files in Spark Shell
- Understanding SBT
- How to build a Spark Project with SBT?
- How to run Spark Project with SBT?
- What is a Local Mode?
- Spark Mode
- Distributed Persistence
- Built-in Libraries for Spark
- The PySpark Shell
- The PySpark Shell – Advanced
- Spark Tools
- PySpark Integration with Jupyter Notebook
- Case Study: Analyzing Airlines Data with PySpark
Working with Key/Value Pairs
- Creating Pair RDDs
- Transformations on Pair RDDs
- Aggregations, Grouping Data, Joins, Sorting Data
- Data Partitioning
- Determining an RDDs Partitioner
- Operations that Benefit from Partitioning
- Operations that Affect Partitioning
- Loading and Saving Data
- File Formats: JSON, Comma-Separated and Tab-Separated Values
- File Formats: Sequence Files, Object Files
- Hadoop Input and Output Formats
- Filesystems: Local/Regular FS
- Amazon S3 and HDFS
- Databases: Java Database Connectivity
- Cassandra, HBase, ElasticSearch
RDD and Spark
- Defining RDDs
- Transformations in RDD
- Various Actions in RDD
- Lazy Evaluations
- Passing Functions to Spark: Python, Scala, Java
- How to load data in RDD?
- How to save data through RDD?
- Scala RDD Extensions 00:00
- What are Double RDD Methods?
- RDD Methods
- Java Pair RDD Methods
- General RDD Methods
- Java RDD Methods
- Common Java RDD Methods
- Spark Java Function Classes
- Understanding Key-Value Pair RDD in Scala and Java
- MapReduce and RDD
- Spark and Hadoop Integration
- HDFS and Yarn
Spark Streaming
- What is Spark Streaming?
- Architecture and Abstraction of Spark Streaming
- Streaming Word Count
- What is a Micro Batch?
- Understanding DStreams
- Input DStreams and Receivers
- Basic and Advanced Sources
- Input Sources: Core Sources and Cluster Sizing
- Transformations in Spark Streaming
- Stateless and Stateful Transformations
- Transformations on DStreams
- Spark Streaming and Fault Tolerance
- Driver Fault Tolerance
- Worker Fault Tolerance
- Receiver Fault Tolerance
- Enabling Checkpointing
- Socket Stream and File Stream
- Stateful Operations
- Window Operations and its Types
- Join Operations-Stream-Dataset Joins
- Join Operations-Stream-Stream Joins
- Parallelism Level
Machine Learning with MLlib
- What is Machine Learning?
- System Requirements
- Machine Learning with Spark
- Spam Classification
- Data Types: Understand and working with Vectors
- Algorithms: Statistics, Classification, and Regression
- Mllib Clustering
- Mllib Collaborative Filtering
- Dimensionality Reduction
- Model Evaluation
- Configuring Algorithms
- Caching RDDs to Reuse
- Recognizing Sparsity
- Level of Parallelism
- Pipeline API
Spark SQL and GraphX
- Initializing Spark SQL
- SchemaRDDs
- Caching
- Loading and Saving Data
- Apache Hive, Parquet, JSON
- JDBC/ODBC Server
- Working with Beeline
- Spark SQL UDFs
- Hive UDFs
- What is a Graph-Parallel System?
- What are the Limitations of Graph-Parallel System?
- What is GraphX?
- What is Property Graph?
- What are Graph Operators?
- List of Operators
- Property and Structural Operators
- Subgraphs
- Join Operators
- How to create a Graph using GraphX
- Understanding Hive and Spark SQL
- An Insight into Spark SQL and SQL Context
- Hive and Spark SQL Integration
- Hive Queries through Spark
- Various Testing Tips in Scala
- Shared Variables
- Broadcast Variables
- Accumulators
On-time notifications
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On-site and remote support
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On-time notifications
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Students' Reviews
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Good teaching And Practical training labs are well equipped. And advance training in spark is also good. The course has given good conceptual understanding of the subject Good overall knowledge of Hadoop Ecosystem.
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Best training institute in Pune for Big Data Hadoop trainers are very helpful and Management is very supportive. It was a nice experience. Did a many real time projects
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Etlhive is an excellent training institute where they put emphasis on hands on practise. The trainers are really awesome. They would answer each and every query. In particular, Mr. Virendra did a good job of teaching our batch of hadoop and spark/scala. All the best Mr. Virendra and Etlhive.
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The Course Gave Good Understanding of Hadoop Ecosystem The Overall Training was Good The Trainer Manu Sir Has A Good Knowledge Of The Subject Overall The Course Has given good conceptual understanding Of The Subject..
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