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Big Data Hadoop Course Overview
Welcome to our Big Data Hadoop Training! This comprehensive program is designed to equip you with the knowledge and skills necessary to navigate the vast landscape of Big Data and Hadoop technologies.
To apply for the Big Data Hadoop Training, you need to either:
- To learn big data Analytics tools you need to know at least one programming language like Java, Python or R.
- You must also have basic knowledge on databases like SQL to retrieve and manipulate data.
- You need to have knowledge on basic statistics like progression, distribution, etc. and mathematical skills like linear algebra and calculus.
Big Data Hadoop Course Content
Our Big data Hadoop course content has been designed based on the industry needs. This course content covers end to end concepts to make the aspirants knowledgeable. The following are Big Data Hadoop Syllabus modules that we are going to cover in this module.
- Introduction to Hadoop
- Hadoop Architecture overview
- Overview of high availability and federation
- Different shell commands available in Hadoop
- Procedure to set up a production cluster
- Overview of configuration files in Hadoop
- Single node cluster installation
- Understanding Spark, Flume, Pig, Scala and Sqoop.
Learning outcome: Upon the completion of this module, you will gain hands-on experience in Hadoop Installation, shell commands cluster installation, etc.
- Overview of Big data Hadoop
- Big data and the role of Hadoop
- Components of Hadoop ecosystem
- Distributed File System Replications
- Secondary Name node, Block Size, and High Availability. Y
- ARN- Node and Resource manager
Learning Outcome: Upon the completion of this chapter you will gain knowledge of data replication process, HDFS working mechanism, deciding the size of a block, gain knowledge of data node and name node.
- Introduction to MapReduce
- Learning the working procedure of MapReduce
- Understanding Map and reduce concepts
- Stages in MapReduce
- The terminology used in MR such as Shuffle, Sort, Combiners, Partitions, Output Format, Input Format and Output Format.
Learning Outcome: Upon the completion of this chapter you learn the procedure to write a word count program, knowledge of MapReduce Combiner, writing a custom practitioner, deploying unit tests, how to use a local job runner, what is a tool runner, data set joining etc.
- Overview of Hadoop Hive
- Understanding the architecture of Hadoop
- Comparison between Hive, RDBMS, and Pig
- Creation of database
- working with Hive Query Language
- Different Hive Tables
- Group by and other clauses,
- Storing the Hive Results,
- HCatalog, and Hive tables,
- Hive partitioning, and Buckets
Learning outcome: By the completion of this module you will learn the process to create a database in Hive, Hive table creation, Database dropping and customization to a table, Writing Hive queries to pull data, Hive Table Partitioning and Group by clause.
- The index in Hive
- Hive Map side join
- User-defined functions in Hive
- Working with complex data types
- overview of Impala
- Difference between Impala and Hive
- Architecture of Impala
Learning Outcome: This chapter will give you complete knowledge of Hive queries, joining table, sequence table deployment, writing indexes, data storage in a different table.
- Introduction to Apache Pig
- Pig features
- Schema and various data types in Hive
- Tuples and Fields
- Available functions in Pig, and Hive Bags
Learning outcome: By the completion of this chapter you will gain knowledge to work with Pig, loading of data, storing the data into files, restricting data to 4 rows, working with Filter By, Group By, Split, Distinct, Cross in Hive.
- Introduction to Apache Sqoop
- Importing and exporting data
- Sqoop Limitations
- Performance improvement with Sqoop
- Flume overview
- Flume Architecture
- What is CAP theorem and Hbase
Learning Outcome: Upon the completion of this module you will be able to generate sequence numbers, Consume twitter data using Sqoop, Hive table creation with AVRO, Table creation in HBase, AVRO with Pig, Scan and enable table, Deploying disable.
- Introduction to Spark
- Procedure to write Spark applications with Scala
- Overview of object-oriented programming
- A detailed study of Scala
- Scala Uses
- Executing Scala code
- Multiple classes of Scala such as Getters, Extending Objects, Abstract, Constructors,
- Setters, Overriding Methods.
- Scala and Java interoperability
- Bobsrockets package
- Anonymous functions, and functional programming
- comparison between Mutable and immutable collections
- control Structures in Scala
- Scala REPL, Lazy Values
- Directed Acyclic Graph (DAG),
- Spark in Hadoop ecosystem and Spark UI
- Developing Spark application using SBT/Eclipse
Learning Outcome: Upon the completion of this module you will gain knowledge to write Spark applications using Scala, Scala ability for Spark real-time analytics operation.
- Introduction to Apache Spark
- Features of Spark
- Spark components Comparison
- between Spark and Hadoop
- Introduction t Scala and RDD
- Integrating HDFS with Spark
Learning Outcome: Upon the completion of this chapter, you will learn the importance of RDD in Spark and how it makes big data processes faster.
- Introduction to Spark SQL
- Importance of SQL in Spark
- Spark SQL JSON support
- Structured data processing
- Working with parquet files and XML data
- Procedure to read JDBC file
- Writing Data frame to HIve
- Hive context creation
- Role of Spark Dataframe
- Overview of schema manual inferring,
- JDBC table reading
- working with CSV files
- Data transformation from DataFrame to JDBC
- Shared accumulators, and variables.
- User-defined functions in Spark SQL
- Query and Transform data in data frames
- Configuration of Hive on Spark as an execution engine
- Dataframe benefits
Learning Outcome: After finishing this chapter you will gain knowledge to use data frames to query and transform data and get an overview of advantages that arise out of using data frames.
- Overview of Spark MLlib
- Introduction to different algorithms
- Graph processing analysis in Spark
- Understanding Spark interactive algorithm
- ML algorithms supported by MLlib,
- Introduction to Machine learning
- Introduction to accumulators,
- Overview of Decision Tree, Logistic Regression,
- Linear Regression. Building a Recommendation Engine
- K-means clustering techniques
Learning Outcome: Upon the completion of this module you will gain hands-on experience in building a recommendation engine.
- Introduction to Kafka
- Use of Kafka
- Kafka workflow,
- Kafka architecture
- Basic operations,
- Configuring Kafka cluster
- Integration of Apache
- Kafka and Apache Flume Producing and consuming messages
- Kafka monitoring tools.
Learning Outcome: Upon the completion of this module, you will gain hands-on exposure in the configuration of Single Node Multi Broker Cluster, Single Node Single Broker Cluster, and integration of Apache Flume and Kafka.
- Introduction to Spark Streaming
- Working with Spark streaming
- Spark Streaming Architecture
- Data processing using Spark streaming
- Requesting count and DStream
- Features of Spark Streaming
- Working with advanced data sources
- Sliding window and multi-batch operations Spark Streaming features Discretized Streams (DStreams),
- Spark Streaming workflow
- Output Operations on DStreams,
- important Windowed Operators
- Windowed Operators and their use
- Stateful Operators.important
Windowed Operators Learning Outcome: After finishing this module you will learn to execute Twitter sentiment analysis, Kafka-Spark Streaming, streaming using Netcat server, and Spark-Flume Streaming.
- Setting up 4 node cluster
- Running MapReduce code
- Running MapReduce jobs
- Working with cloud manager setup
Learning Outcome: By the completion of this chapter you will gain hands-on expertise in building a multi-node Hadoop cluster and working knowledge of cloud managers.
- Introduction to Hadoop configuration
- Various parameters to be followed in the configuration process
- Importance of Hadoop configuration file
- Hadoop environment setup
- MapReduce parameters
- HDFS parameters The process to include and exclude
- Data node directory structures
- Overview of the File system image Understanding Edit log
Learning Outcome: In this chapter, you will gain hands-on exposure in executing performance tuning in MapReduce.
- Basics of checkpoint procedure
- Failure of Name node
- Procedure to recover failed node
- Metadata and Data backup,
- Safe Mode, Different problems and solutions Adding and removing nodes
Learning Outcome: Upon the completion of this chapter, you will learn the process to recover the MapReduce File system, Hadoop cluster monitoring, Usage of job scheduler to schedule jobs, Fair Scheduler and process to its configuration, FIFO schedule and MapReduce job submission flow.
Big Data Hadoop projects
We at HKR not only provide you with theoretical training but also make you practically knowledgeable by making you work with real-.....world projects and case studies. Every course we offer includes two real-time projects which provide you with real-time experience. The practical knowledge improves your domain expertise and helps you in clearing the certifications with ease. Read more
Big Data Hadoop Training Reviews
Technical Lead - Service Now
Big Data Hadoop Training Objectives
This Big Data Hadoop Training has been designed based on the current industry needs and provides the aspirants with all the skills to handle real-world tasks. This course will make you practically knowledgeable by making you work with live projects. You will also gain knowledge to clear the Cloudera CCA175 Big Data certification exam. Get the best Big Data Hadoop online training by joining HKR training.
Following are the areas where you gain full knowledge in this course
- Basic knowledge of Hadoop and Yarn and able to write an application on your own using them
- Configuration of multi-node and pseudo-node clusters on Amazon EC2
- ZooKeeper, Flume, Sqoop, Oozie, Pig, Hive, MapReduce, HDFS, and HBase.
- GraphX, RDD, Data Frame, Streaming, Spark SQL, Spark, and writing Spark applications.
- Hadoop cluster management tasks such as administration, cluster management, monitoring and troubleshooting.
- Gain knowledge of Avro data formats. Configuration of ETL tools such as Talend/Pentaho to execute tasks of Hive, MapReduce, Pig etc.
- Testing applications of Hadoop using MRUnit and various automation tools. Working with real-life industry projects. Big Data Hadoop Certification preparation.
Following are the job roles and candidates who get benefited from learning this Big data Hadoop course:
- Programming Developers Project Managers
- System Administrators
- Architects Testing Professionals
- Mainframe Professionals
- Analytics and Business Intelligence Professionals
- Big Data Hadoop Developers who wish to excel in analytics, testing and administration.
- Graduates or freshers who wish to build their career in the data world.
As such there are no mandatory prerequisites to join this Big data Hadoop training but having knowledge of Java, Unix and SQL would be an added advantage for you.
In order to get the Big Data Hadoop module training, first, you need to search for the best training center that delivers sound knowledge in the Big Data Hadoop module. Also, take suggestions or pick ideas from already learned or experienced candidates in the subject. HKR Trainings, with a team of industry experts, are ready to enhance your professional career and help you to get your dream job.
Once you complete the entire course along with real-time projects and assignments, HKR delivers the course completion certification. This certification helps to get a job in any company very quickly.
Our trainers are highly qualified and certified with many years of industry experience and technology background in Big Data Hadoop.
Certification differentiates you from the non-certified peers, and you can demand the best salary in the leading companies.
We, at HKR, provide complete guidance to reach your dream job. But your job will be based on your performance in the interview panel and the recruiter requirements.
Big Data Hadoop Training FAQ's
Each and every class is recorded so if you missed any class you can review the recordings and clarify any doubts with the trainer in next class.
Yes, we don’t assure 100% placement assistance. We are tied up with some corporate companies so when they have a requirement we send your profiles to them.
Yes, we provide demo before starting any training in which you can clear all your doubts before starting training.
Our trainers are real time experts who are presently working on particular platform on which they are providing training.
You can call our customer care 24/7
Max of the students get satisfied with our training, if you are not then we provide a specialised training in return.