Mastering Big Data: Hadoop Developer Course at ETLHIVE
Unlock the Power of Big Data Processing with Comprehensive Training and Hands-On Experience
Big Data Hadoop Course In Pune
Big Data Hadoop Course in Pune is available in different training formats. In our first format, we provide Hadoop training in the classroom. In the 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 throughout the globe. In India, we are offering big data and Hadoop training with placement in various areas of Pune such as Pimple Saudagar, Kharadi, Vimannagar, Hadapsar, Nal Stop.
Â
Syllabus
- 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 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
Programming Languages & Tools
Certificates
Obtaining Your Certification
Upon successful completion of any course at Etlhive, participants receive a certificate attesting to their proficiency in the respective subject matter. These certificates serve as tangible evidence of the skills acquired during the training, enhancing the credibility of individuals in the job market and validating their expertise to potential employers. Etlhive certificates are recognized for their industry relevance and are highly regarded by leading organizations, providing a competitive edge to certificate holders. The validation process ensures that the certifications are earned through rigorous learning and assessment methods, reflecting real-world application and mastery of the concepts taught. With Etlhive certificates, individuals can showcase their commitment to continuous learning and professional development, opening doors to new career opportunities and advancement prospects.
Training students for leading brands
Frequently asked questions
Hadoop is an open-source framework designed for distributed storage and processing of large datasets across clusters of computers. It’s crucial in handling big data efficiently, enabling businesses to extract valuable insights from vast amounts of information.
This course is ideal for aspiring data engineers, software developers, or anyone interested in big data processing. It’s suitable for professionals seeking to enhance their skills in managing and analyzing large datasets using Hadoop technologies.
Throughout the course, you’ll gain a comprehensive understanding of Hadoop ecosystem components such as HDFS, MapReduce, Hive, Pig, HBase, and more. You’ll also learn to develop, deploy, and optimize Hadoop applications to tackle real-world big data challenges effectively.
While prior programming experience, especially in Java, is beneficial, it’s not mandatory. Our course covers programming concepts relevant to Hadoop development, making it accessible to beginners and experienced professionals alike.
Yes, absolutely. We emphasize a practical, hands-on approach throughout the course. You’ll work on projects and exercises designed to reinforce theoretical concepts, ensuring you gain valuable real-world experience.
- Â
You’ll work on various projects ranging from data ingestion and processing to analysis and visualization. These projects simulate real-world scenarios, providing you with the opportunity to apply your skills to solve practical big data challenges.
Yes, upon successful completion of the course and assessment, you’ll receive a certification from ETLHIVE. This certification validates your proficiency as a Hadoop Developer, enhancing your credibility in the job market.
- Â
- By mastering Hadoop technologies and gaining hands-on experience, you’ll be equipped with the skills sought after by top companies worldwide. Our course curriculum is designed in consultation with industry experts to ensure it aligns with current industry demands, making you job-ready upon completion.
Our Placements
We don’t give just assurances, we actually placed candidates
Aditya Mule
AI ML Developer TrooTech 
Dhruv Bhambani
Python DeveloperPosition2 
Vaibhav Singh Chouhan
Python DeveloperANMsoft 
Rushikesh Tarakh
Data Analytics InternD'Moksha Homes 
Atharva Ingale
Python DeveloperNexgensis 
Supriya Rai
Data Analytics InternRedpluto Analytics 
Shweta Kolapkar
Data Analytics InternRedpluto Analytics 
Shantanu Bokey
Jr. Data ScientistXpressbees 
Gaurav Lokhande
Data AnalystVodafone Idea 
Hitesh Das
Hitesh DasVodaphone Idea Limited 
Testimonials
PRATIKSHA BHALEROA
"I have enrolled for the data science course here and it is the best institute for this course. The trainer, Vaibhav Bajaj is a data science expert who has used the algorithms he teaches in real-world projects so you can be sure to learn the right way of doing this.
Read More
AKSHAT GOYAL
"My name is Akshat Goyal. I have done BE Mechanical in electrical. the purpose of doing this course is that I didn’t know the ABC of coding. In this course my mentor was Vaibhav Bajaj sir, and he is very experienced person in the feld of AI and ML.My experience
Read More
ESHWAR PANDARKAR
"I was one of those who just checked the review and admitted for Data Science course with R Programming (Teacher Mr Vaibhav Sir. Let’s rst talk about teaching, Sir is just Amazing and Excellent covers each and every point starting from basics
Read More
SAURABH JUNGHARE
"It is a nice learning experience with ETLHIVE. Vaibhav sir has in-depth knowledge of data science and explains the concepts in a simple way. Each session comprises of practical as well which helps in getting hands-on practice
Read More
SAGAR SHIRKE
I completed Data Science with Python under Vaibhav Sir. Although the duration of the course was more than the expected time, the trainer was very interactive and gave ample time to
come up with any doubts during session. The trainer has very
Read More
RANJEET GAIKWAD
I am a student of Data Science at this institute. Vaibhav Sir's teaching technique is excellent and well formed. He explains every topic with minute details by covering theory as well as practical session. We got revision session as
Read More
SHUBHADA THALKAR
"I have completed an 8-month online data science course from the Etlhive-Wakad in Pune city. I took their structured online course to get me into the IT field, and I am very satisfied and proud of my decision. It is the best online course.
Recent Posts
