Online course detail
Big Data Hadoop Training in Gurgaon, Delhi - Gyansetu
The Digital Universe is expected to reach 44 trillion gigabytes by 2020, we are churning out roughly 3 Quintilian bytes of data on daily basis. Our certified course will make you an expert in Big data Hadoop technology with strong command on HDFS, Yarn, MapReduce, Pig, Hive, HBase, Oozie, Flume and Sqoop using real-time use cases on Retail, Social Media, Aviation, Tourism, Finance domain, AWS Cloud, Docker Kubernetes Overview for Deploying Big Data Applications.
Instructor Led Training | Free Course Repeat | Placement Assistance | Job Focused Projects | Interview Preparation Sessions
Curriculum
Gyansetu certified course on Big Data Hadoop is intended to start from basics and move gradually towards advancement, to eventually gain working command on Big Data analytics. We understand Big Data can be a daunting course and hence we at Gyansetu have d
- Introduction to Amazon Elastic MapReduce
- AWS EMR Cluster
- AWS EC2 Instance: Multi Node Cluster Configuration
- AWS EMR Architecture
- Web Interfaces on Amazon EMR
- Amazon S3
- Executing MapReduce Job on EC2 & EMR
- Apache Spark on AWS, EC2 & EMR
- Submitting Spark Job on AWS
- Hive on EMR
- Available Storage types: S3, RDS & DynamoDB
- Apache Pig on AWS EMR
- Processing NY Taxi Data using SPARK on Amazon EMR
This module will help you understand how to configure Hadoop Cluster on AWS Cloud:
- Common Hadoop ecosystem components
- Hadoop Architecture
- HDFS Architecture
- Anatomy of File Write and Read
- How MapReduce Framework works
- Hadoop high level Architecture
- MR2 Architecture
- Hadoop YARN
- Hadoop 2.x core components
- Hadoop Distributions
- Hadoop Cluster Formation
This module will help you understand Big Data:
- Configuration files in Hadoop Cluster (FSimage & editlog file)
- Setting up of Single & Multi node Hadoop Cluster
- HDFS File permissions
- HDFS Installation & Shell Commands
- Deamons of HDFS
- Node Manager
- Resource Manager
- NameNode
- DataNode
- Secondary NameNode
- YARN Deamons
- HDFS Read & Write Commands
- NameNode & DataNode Architecture
- HDFS Operations
- Hadoop MapReduce Job
- Executing MapReduce Job
This module will help you to understand Hadoop & HDFS Cluster Architecture:
- How MapReduce works on HDFS data sets
- MapReduce Algorithm
- MapReduce Hadoop Implementation
- Hadoop 2.x MapReduce Architecture
- MapReduce Components
- YARN Workflow
- MapReduce Combiners
- MapReduce Partitioners
- MapReduce Hadoop Administration
- MapReduce APIs
- Input Split & String Tokenizer in MapReduce
- MapReduce Use Cases on Data sets
This module will help you to understand Hadoop MapReduce framework:
- Job Submission & Monitoring
- Counters
- Distributed Cache
- Map & Reduce Join
- Data Compressors
- Job Configuration
- Record Reader
- Hive
- Sqoop (Data Ingestion tool)
- Map Reduce
- Pig
- Hive
- Sqoop (Data Ingestion tool)
- Map Reduce
- Pig
- Hive Installation
- Hive Data types
- Hive Architecture & Components
- Hive Meta Store
- Hive Tables(Managed Tables and External Tables)
- Hive Partitioning & Bucketing
- Hive Joins & Sub Query
- Running Hive Scripts
- Hive Indexing & View
- Hive Queries (HQL); Order By, Group By, Distribute By, Cluster By, Examples
- Hive Functions: Built-in & UDF (User Defined Functions)
- Hive ETL: Loading JSON, XML, Text Data Examples
- Hive Querying Data
- Hive Tables (Managed & External Tables)
- Hive Used Cases
- Hive Optimization Techniques
- Partioning(Static & Dynamic Partition) & Bucketing
- Hive Joins > Map + BucketMap + SMB (SortedBucketMap) + Skew
- Hive FileFormats ( ORC+SEQUENCE+TEXT+AVRO+PARQUET)
- CBO
- Vectorization
- Indexing (Compact + BitMap)
- Integration with TEZ & Spark
- Hive SerDer ( Custom + InBuilt)
- Hive integration NoSQL (HBase + MongoDB + Cassandra)
- Thrift API (Thrift Server)
- Hive LATERAL VIEW
- Incremental Updates & Import in Hive Hive Functions:
- LATERAL VIEW EXPLODE
- 2) LATERAL VIEW JSON_TUPLE ...........others...
- Hive SCD Strategies :1) Type - 1 2) Type – 2 3) TYpe - 3
- UDF, UDTF & UDAF
- Hive Multiple Delimiters
- XML & JSON Data Loading HIVE.
- Aggregation & Windowing Functions in Hive
- Hive integration NoSQL(HBase + MongoDB + Cassandra)
- Hive Connect with Tableau
- Sqoop Installation
- Loading Data form RDBMS using Sqoop
- Fundamentals & Architecture of Apache Sqoop
- Sqoop Tools
- Sqoop Import & Import-All-Table
- Sqoop Job
- Sqoop Codegen
- Sqoop Incremental Import & Incremental Export
- Sqoop Merge
- Sqoop : Hive Import
- Sqoop Metastore
- Sqoop Export
- Import Data from MySQL to Hive using Sqoop
- Sqoop: Hive Import
- Sqoop Metastore
- Sqoop Use Cases
- Sqoop- HCatalog Integration
- Sqoop Script
- Sqoop Connectors
- Batch Processing in Sqoop
- SQOOP Incremental Import
- Boundary Queries in Sqoop
- Controlling Parallelism in Sqoop
- Import Join Tables from SQL databases to Warehouse using Sqoop
- Sqoop Hive/HBase/HDFS integration
- Pig Architecture
- Pig Installation
- Pig Grunt shell
- Pig Running Modes
- Pig Latin Basics
- Pig LOAD & STORE Operators
- Diagnostic Operators
- DESCRIBE Operator
- EXPLAIN Operator
- ILLUSTRATE Operator
- DUMP Operator
- Grouping & Joining
- GROUP Operator
- COGROUP Operator
- JOIN Operator
- CROSS Operator
- Combining & Splitting
- UNION Operator
- SPLIT Operator
- Filtering
- FILTER Operator
- DISTINCT Operator
- FOREACH Operator
- Sorting
- ORDERBYFIRST
- LIMIT Operator
- Built in Fuctions
- EVAL Functions
- LOAD & STORE Functions
- Bag & Tuple Functions
- String Functions
- Date-Time Functions
- MATH Functions
- Pig UDFs (User Defined Functions)
- Pig Scripts in Local Mode
- Pig Scripts in MapReduce Mode
- Analysing XML Data using Pig
- Pig Use Cases (Data Analysis on Social Media sites, Banking, Stock Market & Others)
- Analysing JSON data using Pig
- Testing Pig Sctipts
- Flume Introduction
- Flume Architecture
- Flume Data Flow
- Flume Configuration
- Flume Agent Component Types
- Flume Setup
- Flume Interceptors
- Multiplexing (Fan-Out), Fan-In-Flow
- Flume Channel Selectors
- Flume Sync Processors
- Fetching of Streaming Data using Flume (Social Media Sites: YouTube, LinkedIn, Twitter)
- Flume + Kafka Integration
- Flume Use Cases
- Kafka Fundamentals
- Kafka Cluster Architecture
- Kafka Workflow
- Kafka Producer, Consumer Architecture
- Kafka as PUB/SUB model
- KAFKA Terminologonliineies / Core APIs:
- Producer / Publishers
- Consumer / Subscribers
- Input Offsets
- Topic
- Topic Log
- Replication
- Retention
- Consumer Groups
- Leader
- Follower
- Mirror Maker
- Broker
- Topic Partition
- Kafka Retention Policy
- KAFKA Confluent HUB
- KAFKA Confluent Cloud
- KStream APIs
- Difference between Apache KAFKA / Confluence KAFKA
- KSQL (SQL Engine for Kafka)
- Developing Real-time application using KStream APIs
- KSQL (SQL Engine for Kafka)
- Kafka Connectors
- Kafka REST Proxy
- Kafka Offsets
- Oozie Introduction
- Oozie Workflow Specification
- Oozie Coordinator Functional Specification
- Oozie H-catalog Integration
- Oozie Bundle Jobs
- Oozie CLI Extensions
- Automate MapReduce, Pig, Hive, Sqoop Jobs using Oozie
- Packaging & Deploying an Oozie Workflow Application
- Apache Airflow Installation
- Work Flow Design using Airflow
- Airflow DAG
- Module Import in Airflow
- Airflow Applications
- Docker Airflow
- Airflow Pipelines
- Airflow KUBERNETES Integration
- Automating Batch & Real Time Jobs using Airflow
- Data Profiling using Airflow
- Airflow Integration:
- AWS EMR
- AWS S3
- AWS Redshift
- AWS DynamoDB
- AWS Lambda
- AWS Kines
- Scheduling of PySpark Jobs using Airflow
- Airflow Orchestration
- Airflow Schedulers & Triggers
- Gantt Chart in Apache Airflow
- Executors in Apache Airflow
- Airflow Metrices
- HBase Architecture, Data Flow & Use Cases
- Apache HBase Configuration
- HBase Shell & general commands
- HBase Schema Design
- HBase Data Model
- HBase Region & Master Server
- HBase & MapReduce
- Bulk Loading in HBase
- Create, Insert, Read Tables in HBase
- HBase Admin APIs
- HBase Security
- HBase vs Hive
- Backup & Restore in HBase
- Apache HBase External APIs (REST, Thrift, Scala)
- HBase & SPARK
- Apache HBase Coprocessors
- HBase Case Studies
- HBase Trobleshooting
- Cassandra Installation
- CASSANDRA ARCHITECTURE LAYERS & ITS RELATED COMPONENTS
- Cassandra Configuration
- Operating Cassandra
- Cassandra Tools
- Cqlsh
- Nodetool
- SSTables
- Cassandra Stress
- Partitioners in Cassandra BLOOM FILTERS
- Tunning Cassandra Performance
- Read/Write Cassandra
- Cassandra Queries (CQL)
- CASSANDRA COMPACTION STRATEGIES
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- Spark RDDs Actions & Transformations.
- Spark SQL : Connectivity with various Relational sources & its convert it into Data Frame using Spark SQL.
- Spark Streaming
- Understanding role of RDD
- Spark Core concepts : Creating of RDDs: Parrallel RDDs, MappedRDD, HadoopRDD, JdbcRDD.
- Spark Architecture & Components.
- AWS Lambda:
- AWS Lambda Introduction
- Creating Data Pipelines using AWS Lambda & Kinesis
- AWS Lambda Functions
- AWS Lambda Deployment
- AWS Lambda:
- AWS GLUE :
- GLUE Context
- AWS Data Catalog
- AWS Athena
- AWS Quiksight
- AWS GLUE :
- AWS Kinesis
- AWS S3
- AWS Redshift
- AWS EMR & EC2
- AWS ECR & AWS Kubernetes
- How to manage & Monitor Apache Spark on Kubernetes
- Spark Submit Vs Kubernetes Operator
- How Spark Submit works with Kubernetes
- How Kubernetes Operator for Spark Works.
- Setting up of Hadoop Cluster on Docker
- Deploying IMR , Sqoop & Hive Jobs inside Hadoop Dockerized environment.
1) Docker Installation
2) Docker Hub
3) Docker Images
4) Docker Containers & Shells
5) Working with Docker Containers
6) Docker Architecture
7) Docker Push & Pull containers
8) Docker Container & Hosts
9) Docker Configuration
10) Docker Files (DockerFile)
11) Docker Building Files
12) Docker Public Repositories
13) Docker Private Registeries
14) Building WebServer using DockerFile
15) Docker Commands
16) Docker Container Linking?
17) Docker Storage
18) Docker Networking
19) Docker Cloud
20) Docker Logging
21) Docker Compose
22) Docker Continuous Integration
23) Docker Kubernetes Integration
24) Docker Working of Kubernetes
25) Docker on AWS
1) Overview
2) Learn Kubernetes Basics
3) Kubernetes Installation
4) Kubernetes Architecture
5) Kubernetes Master Server Components
a) etcd
b) kube-apiserver
c) kube-controller-manager
d) kube-scheduler
e) cloud-controller-manager
6) Kubernetes Node Server Components
a) A container runtime
b) kubelet
c) kube-proxy
d) kube-scheduler
e) cloud-controller-manager
7) Kubernetes Objects & Workloads
a) Kubernetes Pods
b) Kubernetes Replication Controller & Replica Sets
8) Kubernetes Images
9) Kubernetes Labels & Selectors
10) Kubernetes Namespace
11) Kubernetes Service
12) Kubernetes Deployments
a) Stateful Sets
b) Daemon Sets
c) Jobs & Cron Jobs
13) Other Kubernetes Components:
a) Services
b) Volume & Persistent Volumes
c) Lables, Selectors & Annotations
d) Kubernetes Secrets
e) Kubernetes Network Policy
14) Kubernetes API
15) Kubernetes Kubectl
16) Kubernetes Kubectl Commands
17) Kubernetes Creating an App
18) Kubernetes App Deployment
19) Kubernetes Autoscaling
20) Kubernetes Dashboard Setup
21) Kubernetes Monitoring
22) Federation using kubefed
Course Description
Apache Hadoop is a collection of network of multiple computers involved in solving and computing tremendous amount of data. Data Storage in Hadoop is done in a distributed file system, known as HDFS that provides very high bandwidth through different clusters.
Gyansetu certified course on Big Data Hadoop is intended to start from basics and move gradually towards advancement, to eventually gain working command on Big Data analytics. We understand Big Data can be a daunting course and hence we at Gyansetu have divided it into easily understandable format that covers all possible aspects of big data.
- Concepts of MapReduce framework & HDFS filesystem
- Setting up of Single & Multi-Node Hadoop cluster
- Understanding HDFS architecture
- Writing MapReduce programs & logic
- Learn Data Loading using Sqoop from structured sources
- Understanding Flume Configuration used for data loading
- Data Analytics using Pig
- Understanding hive for data analytics
- Scheduling MapReduce, Pig, Hive, Sqoop Jobs using Oozie
- Understanding Kafka messaging system
- MapReduce and HBase Integration
- Spark Introduction
Gyansetu Big Data Training in Gurgaon will help you to understand core principles in Big Data Analytics and to gain core expertise in analysis of large datasets from various sources:
- Expertise in PySpark/ Scala-Spark
- Real Time Data Storage inside Data Lake (Real Time ETL)
- Big Data Related Services on AWS Cloud
- Deploying BIG Data application in Production environment using Docker & Kubernetes
- Experience in Real Time Big Data Projects
Our Big Data Experts have realized that Learning Hadoop standalone doesn’t qualify candidates to clear the interview process. Interviewers demand and expectations from the candidates are more nowadays. They expect proficiency in advanced concepts like-
All the advanced level topics will be covered at Gyansetu in a classroom/online Instructor led mode with recordings.
Knowledge of Java, SQL is good to start Hadoop Training in Gurgaon. However, Gyansetu offers a complementary instructor led course on Java & SQL before you start Hadoop course.
- Our placement team will add Big Data skills & projects in your CV and update your profile on Job search engines like Naukri, Indeed, Monster, etc. This will increase your profile visibility in top recruiter search and ultimately increase interview calls by 5x.
- Our faculty offers extended support to students by clearing doubts faced during the interview and preparing them for the upcoming interviews.
- Gyansetu’s Students are currently working in Companies like Sapient, Capgemini, TCS, Sopra, HCL, Birlasoft, Wipro, Accenture, Zomato, Ola Cabs, Oyo Rooms, etc.
Gyansetu is providing complimentary placement service to all students. Gyansetu Placement Team consistently work on industry collaboration and associations which help our students to find their dream job right after the completion of training.
- Gyansetu trainer’s are well known in Industry; who are highly qualified and currently working in top MNCs.
- We provide interaction with faculty before the course starts.
- Our experts help students in learning Technology from basics, even if you are not good in basic programming skills, don’t worry! We will help you.
- Faculties will help you in preparing project reports & presentations.
- Students will be provided Mentoring sessions by Experts.
Reviews
Placement
Chetan
Placed In:
KPMG
Placed On – October 20 , 2018Review:
Gyansetu has the latest certified courses, very good team of trainers. Institute staff is very polite and cooperative. Good option if you are searching for an IT training institute in Gurgaon.
Amit
Placed In:
Wipro
Placed On – September 15 , 2019Review:
From my learning experience I can say that Gyansetu is one of the best platform to learn niche technologies from every angle starting from scratch to advance level with projects and assignments.
Ankur
Placed In:
Genpact
Placed On – March 09 , 2017Review:
Both instructors and support staff are outstanding.The course material, project assignments are well designed and mapped with industry standards. You get to learn by working on full-fledged ML projects.
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Structure your learning and get a certificate to prove it.
Projects
- Pre processing includes standard scaling, means normalizing the data followed by cross validation techniques to check the compatibility of the data.
- In data modeling, using Decision Tree with Random forest and Gradient Boost hyper parameter tuning techniques to tune our model.
- In the end, evaluating the mode, by measuring confusion matrix with accuracy of 98% and a trained model, which will show all the fraud transaction on PLCC & DC cards on tableau dashboard.
Environment: Hadoop YARN, Spark Core, Spark Streaming, Spark SQL, Scala, Kafka, Hive, Amazon AWS, Elastic Search, Zookeeper
Tools & Techniques used : PySpark MLIB,Spark Streaming, Python (Jupiter Notebook, Anaconda), Machine Learning packages: Numpy, Pandas, Matplot, Seaborn, Sklearn ,Random forest and Gradient Boost, Confusing matrix Tableau
Description : Build a predictive model which will predict fraud transaction on PLCC &DC cards on daily bases. This includes data extraction then data cleaning followed by data pre processing.
Environment: Hadoop YARN, Spark Core, Spark Streaming, Spark SQL, Scala, Kafka, Hive
Tools & Techniques used : Hadoop+HBase+Spark+Flink+Beam+ML stack, Docker & KUBERNETES, Kafka, MongoDB, AVRO, Parquet
Description : The aim is to create a Batch/Streaming/ML/WebApp stack where you can test your jobs locally or to submit them to the Yarn resource manager. We are using Docker to build the environment and Docker-Compose to provision it with the required components (Next step using Kubernetes). Along with the infrastructure, We are check that it works with 4 projects that just probes everything is working as expected. The boilerplate is based on a sample search flight Web application.
Big Data Hadoop Training in Gurgaon, Delhi - Gyansetu Features
FAQs
- What type of technical questions are asked in interviews?
- What are their expectations?
- How should you prepare?
We have seen getting a relevant interview call is not a big challenge in your case. Our placement team consistently works on industry collaboration and associations which help our students to find their dream job right after the completion of training. We help you prepare your CV by adding relevant projects and skills once 80% of the course is completed. Our placement team will update your profile on Job Portals, this increases relevant interview calls by 5x.
Interview selection depends on your knowledge and learning. As per the past trend, initial 5 interviews is a learning experience of
Our faculty team will constantly support you during interviews. Usually, students get job after appearing in 6-7 interviews.
- What type of technical questions are asked in interviews?
- What are their expectations?
- How should you prepare?
We have seen getting a technical interview call is a challenge at times. Most of the time you receive sales job calls/ backend job calls/ BPO job calls. No Worries!! Our Placement team will prepare your CV in such a way that you will have a good number of technical interview calls. We will provide you interview preparation sessions and make you job ready. Our placement team consistently works on industry collaboration and associations which help our students to find their dream job right after the completion of training. Our placement team will update your profile on Job Portals, this increases relevant interview call by 3x.
Interview selection depends on your knowledge and learning. As per the past trend, initial 8 interviews is a learning experience of
Our faculty team will constantly support you during interviews. Usually, students get job after appearing in 9-10 interviews.
- What type of technical questions are asked in interviews?
- What are their expectations?
- How should you prepare?
We have seen getting a technical interview call is hardly possible. Gyansetu provides internship opportunities to the non-working students so they have some industry exposure before they appear in interviews. Internship experience adds a lot of value to your CV and our placement team will prepare your CV in such a way that you will have a good number of interview calls. We will provide you interview preparation sessions and make you job ready. Our placement team consistently works on industry collaboration and associations which help our students to find their dream job right after the completion of training and we will update your profile on Job Portals, this increases relevant interview call by 3x.
Interview selection depends on your knowledge and learning. As per the past trend, initial 8 interviews is a learning experience of
Our faculty team will constantly support you during interviews. Usually, students get job after appearing in 9-10 interviews.
Yes, a one-to-one faculty discussion and demo session will be provided before admission. We understand the importance of trust between you and the trainer. We will be happy if you clear all your queries before you start classes with us.
We understand the importance of every session. Sessions recording will be shared with you and in case of any query, faculty will give you extra time to answer your queries.
Yes, we understand that self-learning is most crucial and for the same we provide students with PPTs, PDFs, class recordings, lab sessions, etc, so that a student can get a good handle of these topics.
We provide an option to retake the course within 3 months from the completion of your course, so that you get more time to learn the concepts and do the best in your interviews.
We believe in the concept that having less students is the best way to pay attention to each student individually and for the same our batch size varies between 5-10 people.
Yes, we have batches available on weekends. We understand many students are in jobs and it's difficult to take time for training on weekdays. Batch timings need to be checked with our counsellors.
Yes, we have batches available on weekdays but in limited time slots. Since most of our trainers are working, so either the batches are available in morning hours or in the evening hours. You need to contact our counsellors to know more on this.
Total duration of the Hadoop course is 80 hours (40 Hours of live instructor led training and 40 hours of self paced learning).
You don’t need to pay anyone for software installation, our faculties will provide you all the required software and will assist you in the complete installation process.
Our faculties will help you in resolving your queries during and after the course.
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