Facts About Big Data & Infycle’s expertise of Big data training in Chennai.
Big data training in chennai will be a totally blooming industry for the following 10 years in India.
Statistics uncover that 16% of the organizations have the required analytics ability set up to deal with big data ventures. Experts are scrambling to get prepared and guaranteed in what’s relied upon to be the hottest new tech skill: Hadoop. With squads of Big data experts, Infycle’s turned out to be one of the best Big data training in Chennai whose Training course and certification is intended to give you skills in building amazing big data applications utilizing Hadoop by performing assignments on the real time Hadoop cluster.
Excelling Big Data Hadoop Training In Chennai – Way to become Big Data Specialist!
Hence to excel in this industry and compete with a lot of skilled people, learning Big Data Hadoop training in chennai is a primary chore for all job seekers. Those who join Big Data course in chennai, can learn and implement real time user cases, earn knowledge on maintaining data in HDFS, overall MapReduce architecture as well as HDFS infographic.
To become a Hadoop certified professional, one must search for best and trusted software training institutes around them. Suppose a person is interested in learning Big Data Training in Chennai, he/she must compare and choose the right Big Data Hadoop Courses package from the best software training organization in Chennai. Further learning these vast data analytics concepts is nevertheless an easier task. Hence being trained up from highly qualified and experienced teaching experts, one can accomplish his/her dream job come true.
Big Data Training in Chennai with Infycle Technologies!
We strive to enlighten the path of your career with potential skills.
Certificate Course
Build a positive impact on your business. Check out what we have to offer.
| May 30th | Mon-Fri(21 Days) | Timing 07:00 AM to 09:00 AM (IST) | Enroll Now |
| June 11th | Mon-Fri(21 Days) | Timing 07:00 AM to 09:00 AM (IST) | Enroll Now |
| July 30th | Mon-Fri(21 Days) | Timing 07:00 AM to 09:00 AM (IST) | Enroll Now |
Only Big Data Training Institute in chennai that provides data experiential Learning!
Infycle Big Data Certification Course is provided by Big Data Hadoop industry specialists, and it covers in and out information on big data hadoop course content tools namely MapReduce, Hive, Pig, HBase, Spark, Oozie, Flume, Sqoop HDFS and YARN.
Big Data Training and Placement in Chennai -Why Infycle?
Infycle Big Data Training in Chennai, provides big data fundamentals and Hadoop instructional class which is intended to assist you with becoming a talented Hadoop engineer through a hands-on industry-based venture.
We, Infycle Training provides the following benefits:
- Trainees will get a prompt response to any training related questions, either specialized or something else. We prompt our learners not to hold up until the following class to look for answers to any specialized issue.
- Training meetings are directed by a well-experienced instructor with ongoing models.
- We give training and placement guidance with your preferred choice.
- As a feature of this training, you will be dealing with projects and assignments that have gigantic implications in real industry situations, therefore helping you quickly track your profession easily.
- Toward the end of this training program, there will be tests that neatly reflect the sort of inquiries posed in the particular certification tests and assist you with scoring better.
- Infycle effectively gives placement to all trainees who have effectively finished the training.
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Best Big Data Training in Chennai- Infycle:
Benefits of big data includes several known and unknow benefits and some of them are exclusive.
- Excelling Interviews with Live projects – The most significant question you will be asked is, your proficiency & expertise in Big Data and what are the projects you have worked on?”. With Infycle, you can able to answer every question like a pro! Besides, our training and projects we taught you will help you to handle the tasks effortlessly.
- Trust built for Infycle Big Data Training In Chennai – since our brand “Infycle ” widely trusted by most of the top MNC’s & organizations for it’s quality of training. So, your profile will have high level standards to attract any type of companies.
- Upgrade your skills every day with Infycle- At regular intervals, every day our team & trainers will come up with new experiments, ideas, tools, technologies to sharpen your skill. Also, we arrange week after week Webinars, Podcasts, online training sessions through which you can gain values & knowledge at the same time.
Find Better solution!
Are you still not aware about the course? Infycle is open to clarify all your doubts. Reach us for your queries.
Big Data Hadoop Training in Chennai- Infycle Course Assets.
To improve your knowledge and our training modules we explore out our methodologies!
Course Attributes
Our course training is an extreme level of knowledge transfer that could help you in the right way!
| Course Name | Big Data |
|---|---|
| Skill Level | Beginner, Intermediate, Advanced |
| Total Learners | 5000+ |
| Course Duration | 500 Hours |
| Course Material |
|
| Student Portal | Yes |
| Placement Assistance | Yes |
Our Schedules
We are available for you in a flexible manner for the best support.
We give you the right path!
We consider your goals and provide you the clear vision of the career growth.
Get the Globalized knowledge
Infycle works with motive of delivering only the best to the students to enhance their skills!
Master up your skills with certification!
Enrich the status of your profile by gaining the certification of the course to upgrade yourself.
FAQ's
We prove our confidence by giving a right solution to you! Some of the solutions provided here.
Who can take up the Big Data course?
Big data is a best, highly demanded and beneficial course. If you are interested then you can take up the training. It is affordable for fresh graduates, experienced developers and other software professionals.
What are the prerequisites for Big Data training?
There are no prerequisites for getting trained in Bid Data course. It is beneficial if you have prior knowledge of programming.
Do Infycle offers Placement after doing big data course?
Infycle is proud to present that we have placed 5000+ students in various reputed firms. Our expert placement team provides 100% placement guidance and helps you to mock the interview and get placed with best offers.
Is there any additional benefits in the training?
Infycle Technologies supports you with the best training sessions. In addition to the course, we support you with projects cases, query session and resume building that adds points to your profile to grab the job opportuinities.
Is Big Data is hard to learn?
No. It is basically a database language and uses general language for easy understanding. With our experienced faculty team at Infycle, we make it more easier to learn.
What is my career scope in Big Data?
With everything going digital, all the firms and industries depend a database to maintain records.So with every changing moment, the career opportunities for big data developer is wide open in all sectors.There is a huge demand for database administrators with high paychecks.
Questions?
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Let's get in touch
Give us a call or drop by anytime, we endeavour to answer all enquiries within 24 hours on business days.
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Big Data Hadoop Course Content
Have a glance through our course syllabus and precisely formulated modules of Big Data.
Bigdata Hadoop Administration Course Content
Introduction to Bigdata and the Hadoop Ecosystem
- Why we need Bigdata
- Real time use cases of Bigdata Overview
- Introduction to Apache Hadoop and the Hadoop Ecosystem
- Apache Hadoop Overview
- Data Ingestion and Storage
- Data Locality
- Data Analysis and Exploration
- Other Ecosystem Tools
Hadoop Ecosystem Installation
- Ubuntu 14.04 LTS Installation through VMware Player
- Installing Hadoop 2.7.1 on Ubuntu 14.04 LTS (Single-Node Cluster)
- Apache Hive Installation
- MySQL Installation
- Apache Sqoop and Flume Installation
- Kafka and spark Installation
- Scala SBT Installation
Apache Hadoop File Storage (HDFS)
- Why we need HDFS
- Apache Hadoop Cluster Components
- HDFS Architecture
- Failures of HDFS 1.0
- High Availability and Scaling
- Pros and Cons of HDFS
- Basics File system Operations
- Hadoop FS or HDFS DFS – The Command-Line Interface
- Decommission methods for Data Nodes.
- Exercise and small use case on HDFS.
- Block Placement Policy and Modes
- Configuration files handling
- Federation of HDFS
- FSCK Utility (Heart Beat and Block report).
- Reading and Writing Data in HDFS
- Replica Placement Strategy
- Fault tolerance
MapReduce
- Overview and Architecture of Map Reduce
- Components of MapReduce
- How MapReduce works
- Flow and Difference of MapReduce Version
- YARN Architecture
- Working with YARN
- Types of Input formats & Output Formats
Apache Hive
- Installation on Ubuntu 14.04 With MySQL Database Metastore
- Overview and Architecture
- Command execution in shell and HUE
- Hive Data Loading methods, Partition and Bucketing
- External and Managed tables in Hive
- File formats in Hive
- Hive Joins
- Serde in Hive
Apache Sqoop
- Overview and Architecture
- Import and Export
- Sqoop Incremental load
- Managing Directories
- File Formats
- Boundary Query and Split-by
- Delimiter and Handling Nulls
- Sqoop import all tables
- Apache Pig Overview and Architecture
- MapReduce Vs Pig
- Data types of Pig
- Pig Data loading methods
- Pig Operators and execution modes
- Performance Tuning in Pig
- Type casting in Pig
- Data Validation in Pig
- Pig script execution in shell/HUE
Apache Hbase
- Introduction to NoSQL/CAP theorem concepts
- Apache HBase Overview and Architecture
- Apache HBase Commands
- HBase and Hive Integration module
- Hbase execution in shell/HUE
Apache Spark Basics
- What is Apache Spark?
- Starting the Spark Shell
- Using the Spark Shell
- Getting Started with Datasets and Data Frames
- Data Frame Operations
- Apache Spark Overview and Architecture
- RDD Overview
- RDD Data Sources
- Creating and Saving RDDs
- RDD Operations
- Transformations and Actions
- Converting Between RDDs and Data Frames
- Key-Value Pair RDDs
- Map-Reduce operations
- Other Pair RDD Operations
Working with Data Frames, Schemas and Datasets
- Creating Data Frames from Data Sources
- Saving Data Frames to Data Sources
- Data Frame Schemas
- Eager and Lazy Execution
- Querying Data Frames Using Column Expressions
- Grouping and Aggregation Queries
- Joining Data Frames
- Querying Tables, Files, Views in Spark Using SQL
- Comparing Spark SQL and Apache Hive-on-Spark
- Creating Datasets
- Loading and Saving Datasets
- Dataset Operations
Running Apache Spark Applications
- Writing a Spark Application
- Building and Running an Application
- Application Deployment Mode
- The Spark Application Web UI
- Configuring Application Properties
Apache Flume
- Introduction to Flume & features
- Flume topology & core concepts
- Flume Agents: Sources, Channels and Sinks
- Property file parameters logic
Apache Kafka
- Installation
- Overview and Architecture
- Consumer and Producer
- Deploying Kafka in real world business scenarios
- Integration with Spark for Spark Streaming
Apache Zookeeper
- Introduction to zookeeper concepts
- Overview and Architecture of Zookeeper
- Zookeeper principles & usage in Hadoop framework
- Use of Zookeeper in Hbase and Kafka
Apache Oozie
- Oozie Fundamentals
- Oozie workflow creations
- Concepts of Coordinates and Bundles
Bigdata Hadoop Development Course Content
Introduction to Bigdata and the Hadoop Ecosystem
- Why we need Bigdata
- Real time use cases of Bigdata Overview
- Introduction to Apache Hadoop and the Hadoop Ecosystem
- Apache Hadoop Overview
- Data Ingestion and Storage
- Data Processing
- Data Analysis and Exploration
- Other Ecosystem Tools
Apache Hadoop File Storage (HDFS)
- Why we need HDFS
- Apache Hadoop Cluster Components
- HDFS Architecture
- Failures of HDFS 1.0
- High Availability and Scaling
- Pros and Cons of HDFS
- Basics File system Operations
- Hadoop FS or HDFS DFS – The Command-Line Interface
- Decommission methods for Data Nodes.
- Exercise and small use case on HDFS.
MapReduce
- Overview and Architecture of Map Reduce
- Components of MapReduce
- How MapReduce works
- Flow and Difference of MapReduce Version
- YARN Architecture
- Working with YARN
- Types of Input formats & Output Formats
- Examples of MapReduce Tasks
HDFS Info graphic
- Reading Data from HDFS
- Writing Data from HDFS
- Replica Placement Strategy
- Fault tolerance
Apache Hive
- Installation on Ubuntu 14.04 With MySQL Database Metastore
- Overview and Architecture
- Command execution in shell and HUE
- Hive Data Loading methods, Partition and Bucketing
- External and Managed tables in Hive
- File formats in Hive
- Hive Joins
- Serde in Hive
- Functions in Hive
- String Manipulation in Hive
- Date Manipulation in Hive
- Row level transformations in Hive
- Indexes and Views in Hive
- Hive Query Optimizers
- Windowing Functions in Hive
Apache Sqoop
- Overview and Architecture
- Import and Export
- Sqoop Incremental load
- Sqoop Eval
- Managing Directories
- File Formats
- Compression Algorithm
- Boundary Query and Split-by
- Transformations and filtering
- Delimiter and Handling Nulls
- Sqoop import all tables
- Column Mapping in Sqoop Export
- Apache Pig Overview and Architecture
- MapReduce Vs Pig
- Data types of Pig
- Pig Data loading methods
- Pig Operators and execution modes
- Load and Store Operators
- Diagnostic Operators
- Grouping and Joining
- Combining and Splitting
- Filtering and Sorting
- Built-in Functions
- Pig script execution in shell/HUE
Apache Hbase
- Introduction to NoSQL/CAP theorem concepts
- Apache HBase Overview and Architecture
- Apache HBase Commands
- HBase and Hive Integration module
- Hbase execution in shell/HUE
Introduction to Scala
- Functional Programing Vs Object Orient Programing
- Scala Overview
- Configuring Apache Spark with Scala
- Variable Declaration
- Operations on variables
- Conditional Expressions
- Pattern Matching
- Iteration
Deep Dive into Scala
- Scala Functions and Oops Concept
- Scala Abstract Class & Traits
- Access Modifier, Array and String
- Exceptions, Collections and Tuples
- File handling and Multithreading
- Spark Ecosystem
Apache Spark Basics
- What is Apache Spark?
- Starting the Spark Shell
- Using the Spark Shell
- Getting Started with Datasets and Data Frames
- Data Frame Operations
- Apache Spark Overview and Architecture
- RDD Overview
- RDD Data Sources
- Creating and Saving RDDs
- RDD Operations
- Transformations and Actions
- Converting Between RDDs and Data Frames
- Key-Value Pair RDDs
- Map-Reduce operations
- Other Pair RDD Operations
Working with Data Frames, Schemas and Datasets
- Creating Data Frames from Data Sources
- Saving Data Frames to Data Sources
- Data Frame Schemas
- Eager and Lazy Execution
- Querying Data Frames Using Column Expressions
- Grouping and Aggregation Queries
- Joining Data Frames
- Querying Tables, Files, Views in Spark Using SQL
- Comparing Spark SQL and Apache Hive-on-Spark
- Creating Datasets
- Loading and Saving Datasets
- Dataset Operations
Apache Flume
- Introduction to Flume & features
- Flume topology & core concepts
- Flume Agents: Sources, Channels and Sinks
- Property file parameters logic
Apache Kafka
- Installation
- Overview and Architecture
- Consumer and Producer
- Deploying Kafka in real world business scenarios
- Integration with Spark for Spark Streaming
Apache Zookeeper
- Introduction to zookeeper concepts
- Overview and Architecture of Zookeeper
- Zookeeper principles & usage in Hadoop framework
- Use of Zookeeper in Hbase and Kafka
Apache Oozie
- Oozie Fundamentals
- Oozie workflow creations
- Concepts of Coordinates and Bundles
CCA Certification Course Content
Introduction to Apache Hadoop and the Hadoop Ecosystem
- Introduction to Apache Hadoop and the Hadoop Ecosystem
- Apache Hadoop Overview
- Data Ingestion and Storage
- Data Processing
- Data Analysis and Exploration
- Other Ecosystem Tools
Hadoop Ecosystem Installation
- Ubuntu 14.04 LTS Installation through VMware Player
- Installing Hadoop 2.7.1 on Ubuntu 14.04 LTS (Single-Node Cluster)
- Apache Hive Installation
- MySQL Installation
- Sqoop and Flume Installation
- Kafka and spark Installation
- Scala SBT Installation
Distributed Processing on an Apache Hadoop Cluster
- Overview and Architecture of Map Reduce
- Components of MapReduce
- How MapReduce works
- Flow and Difference of MapReduce Version
- YARN Architecture
- Working with YARN
HDFS Info graphic
- Reading Data from HDFS
- Writing Data from HDFS
- Replica Placement Strategy
- Fault tolerance – 1,2 and 3
Introduction to Scala
- Functional Programing Vs Object Orient Programing
- Scala Overview
- Configuring Apache Spark with Scala
Scala Basics
- Variable Declaration
- Operations on variables
- Conditional Expressions
- Pattern Matching
- Iteration
Deep Dive into Scala
- Functions and Oops Concept of scala
- Abstract Class & Traits
- Access Modifier, Array and String
- Exceptions, Collections and Tuples
- File handling and Multithreading
- Spark Ecosystem
Apache Hive
- Installation on Ubuntu 14.04 With MySQL Database Metastore
- Overview and Architecture
- Command execution in shell and HUE
- Data Loading methods, Partition and Bucketing
- External and Managed tables in Hive
- File formats in Hive
- Hive Joins
- Serde in Hive
Apache Sqoop
- Overview and Architecture
- MySQL Installation
- Sqoop Installation
- Import Examples and Export Example
Apache Spark Basics
- What is Apache Spark?
- Starting the Spark Shell
- Using the Spark Shell
- Getting Started with Datasets and DataFrames
- DataFrame Operations
Working with DataFrames and Schemas
- Creating DataFrames from Data Sources
- Saving DataFrames to Data Sources
- DataFrame Schemas
- Eager and Lazy Execution
Analyzing Data with DataFrame Queries
- Writing and Passing Transformation Functions
- Transformation Execution
- Converting Between RDDs and DataFrames
Aggregating Data with Pair RDDs
Querying Tables and Views with Apache Spark SQL
- Spark with Querying Tables Using SQL
- Querying Files and Views
- The Catalog API
- Comparing Spark SQL, Apache Impala, and Apache Hive-on-Spark
Working with Datasets in Scala
Writing, Configuring, and Running Apache Spark Applications
- Writing a Spark Application
- Building and Running an Application
- Application Deployment Mode
- The Spark Application Web UI
- Configuring Application Properties
- Review: Apache Spark on a Cluster
- RDD Partitions
- Example: Partitioning in Queries
- Stages and Tasks
- Job Execution Planning
- Example: Catalyst Execution Plan
- Example: RDD Execution Plan
- DataFrame and Dataset Persistence
- Persistence Storage Levels
- Viewing Persisted RDDs
- Difference between RDD, Dataframe and Dataset
Common Patterns in Apache Spark Data Processing
- Common Apache Spark Use Cases
- Iterative Algorithms in Apache Spark
- Machine Learning
- Example: k-means
Apache Spark Streaming: Introduction to DStreams
- Apache Spark Streaming Overview
- Example: Streaming Request Count
- Developing Streaming Applications
- Print and Saving tweets
- Explain how stateful operations work
- Explain window and join operations
Apache Flume
- Introduction to Flume & features
- Flume topology & core concepts
- Flume Agents: Sources, Channels and Sinks
- Property file parameters logic
Apache Kafka
- Installation
- Overview and Architecture
- Consumer and Producer
- Deploying Kafka in real world business scenarios
- Integration with Spark for Spark Streaming
Certificate List
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Infycle’s Big Data Training Institute In Chennai -What’s This Program About?
“Inspired by industry and driven by student success”
The program enables you to
- Build your resume to grab a recruiter attraction and leave an extraordinary initial introduction in any company you join in.
- Reclassifying your career objectives and molding your portfolio to assist you “TO STAND OUT IN A GROUP”.
- Pretending during a mock interview and Q&A rounds to intellectually set you up for progress.
Big Data Analytics Introduction:
- Big data Analytics offers further knowledge about the importance of data sets by recounting the story behind the data.
- This empowers partners to settle on progressively educated choices. This program furnishes students with an exceptional mix of hypothetical information and applied skills.
- Students will learn about how to gather, control, encode, and store data indexes so they can be analyzed and mined.
Career Opportunities of Big Data analytics training in Chennai:
Infycle’s training allows graduates to gather, sort out and associate data for a wide range of businesses including government, applied research, HR, social insurance, and marketing.
Utilizing earlier foundation, aptitudes, and experience, students and working employees might be utilized in jobs, for example, Data Analyst, Data Visualization Developer, BI Specialist, etc.
Top 12 Companies using Apache Hadoop
Hadoop is an open source platform that runs huge amounts of unstructured data in commodity hardware with great flexibility across the web. Here are some of the top companies which follow Hadoop clusters in their businesses:=
- Amazon web services
- Cloudera
- Sciencesoft
- Pivotal
- Hortonworks
- IBM
- Microsoft
- MapR
- Datameer
- Hadapt
- Adello
- Karmasphere
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