Attend a Demo Session | Meet the Expert Who Can Kickstart Your Career in BigData Hadoop
|Course Duration||30 hrs|
|Next Batch||5 December, 2020|
The Architecture Of Hadoop 2.0 Cluster
What Is High Availability And Federation
How To Setup A Production Cluster
Various Shell Commands In Hadoop
Understanding Configuration Files In Hadoop 2.0
Installing Single Node Cluster With Cloudera Manager And Understanding Spark, Scala, Sqoop, Pig And Flume
Introducing Big Data and Hadoop
What is Big Data and where does Hadoop fit in
Two important Hadoop ecosystem components, namely, MapReduce and HDFS
In-depth Hadoop Distributed File System – Replications, Block Size, Secondary Name node
High Availability and in-depth YARN – resource manager and node manager
Learning the working mechanism of MapReduce
Understanding the mapping and reducing stages in MR
Various terminologies in MR like Input Format
Output Format, Partitioners, Combiners, Shuffle and Sort
Introducing Hadoop Hive, detailed architecture of Hive
Comparing Hive with Pig and RDBMS
Working with Hive Query Language
Creation of database, table
Group by and other clauses
Various types of Hive tables, HCatalog, storing the Hive Results, Hive partitioning and Buckets
Indexing in Hive, the Map Side Join in Hive, working with complex data types, the Hive User-defined Functions
Introduction to Impala
Comparing Hive with Impala
The detailed architecture of Impala
Apache Pig introduction
Its various features, various data types and schema in Hive
The available functions in Pig, Hive Bags, Tuples and Fields
Apache Sqoop introduction
Importing and exporting data
performance improvement with Sqoop, Sqoop limitations
Introduction to Flume and understanding the architecture of Flume and what is HBase and the CAP theorem
Using Scala for writing Apache Spark applications
Detailed study of Scala
The need for Scala, the concept of object-oriented programming, executing the Scala code, various classes in Scala like Getters, Setters, Constructors, Abstract, Extending Objects, Overriding Methods, the Java and Scala interoperability
The concept of functional programming and anonymous functions
Bobsrockets package and comparing the mutable and immutable collections
Scala REPL, Lazy Values
Control Structures in Scala
Directed Acyclic Graph (DAG)
First Spark application using SBT/Eclipse
Spark Web UI
Spark in Hadoop ecosystem.
Detailed Apache Spark, its various features
Comparing with Hadoop
Various Spark components
Combining HDFS with Spark
Introduction to Scala and importance of Scala and RDD
Understanding the Spark RDD operations
Comparison of Spark with MapReduce
What is a Spark transformation
Loading data in Spark
Types of RDD operations viz. transformation and action and what is a Key/Value pair
The detailed Spark SQL
The significance of SQL in Spark for working with structured data processing
Spark SQL JSON support
Working with XML data and parquet files
Creating Hive Context
Writing Data Frame to Hive
How to read a JDBC file, significance of a Spark Data Frame
How to create a Data Frame
What is schema manual inferring
How to work with CSV files, JDBC table reading
Data conversion from Data Frame to JDBC
Spark SQL user-defined functions
Shared variable and accumulators
How to query and transform data in Data Frames
How Data Frame provides the benefits of both Spark RDD and Spark SQL and deploying Hive on Spark as the execution engine
Introduction to Spark MLlib
Understanding various algorithms
What is Spark iterative algorithm
Spark graph processing analysis, introducing Machine Learning
Spark variables like shared and broadcast variables
What are accumulators, various ML algorithms supported by MLlib
Linear Regression, Logistic Regression, Decision Tree, Random Forest
K-means clustering techniques, building a Recommendation Engine
Why Kafka, what is Kafka, Kafka architecture, Kafka workflow, configuring Kafka cluster, basic operations, Kafka monitoring tools
Integrating Apache Flume and Apache Kafka
Introduction to Spark streaming
The architecture of Spark streaming
Working with the Spark streaming program
Processing data using Spark streaming
Requesting count and DStream
Multi-batch and sliding window operations and working with advanced data sources
Introduction to Spark Streaming, features of Spark Streaming, Spark Streaming workflow,
Initializing StreamingContext, Discretized Streams (DStreams), Input DStreams and Receivers, transformations on DStreams, Output Operations on DStreams
Windowed Operators and why it is useful, important Windowed Operators, Stateful Operators.
Create a 4-node Hadoop cluster setup
Running the MapReduce Jobs on the Hadoop cluster
Successfully running the MapReduce code and working with the Cloudera Manager setup
The overview of Hadoop configuration
The importance of Hadoop configuration file
The various parameters and values of configuration
The HDFS parameters and MapReduce parameters
Setting up the Hadoop environment
The Include and Exclude configuration files
The administration and maintenance of name node
Data node directory structures and files
What is a File system image and understanding Edit log?
Introduction to the checkpoint procedure
name node failure and how to ensure the recovery procedure, Safe Mode, Metadata and Data Backup, various potential problems and solutions
What to look for and how to add and remove nodes
How ETL tools work in the Big Data industry
Introduction to ETL and data warehousing
Working with prominent use cases of Big Data in the ETL industry and end-to-end ETL PoC showing Big Data integration with the ETL tool
After the successful completion of Big Data Hadoop training at Tekslate, the participant will be able to
Master the fundamentals of Hadoop and Big Data and its features.
Gain knowledge on how to use HDFS, and MapReduce frameworks.
Gain knowledge of various tools of Hadoop ecosystem like Pig, Hive, Sqoop, Flume, Oozie, and HBase.
Work with Pig and Hive to perform ETL operations and data analytics.
Perform Partitioning, Bucketing, and Indexing in Hive.
Understand Apache Spark and its Ecosystem.
Implement real-world Big Data Analytics projects in various verticals.
The demand for Big Data Hadoop developers is increasing rapidly in the industry with high CTC being offered to them.
On average, a certified Big Data Hadoop developer is earning 123,000 USD per annum.
The following job roles will get benefited from learning this course:
Software Developers and Architects
Senior IT professionals
Testing and Mainframe Professionals
Data Management Professionals
Business Intelligence Professionals
Aspirants who are looking to build a career in Big Data analytics.
There are no specific prerequisites for learning this course. Anyone who is looking to build a career in this domain can join this training.
Having prior knowledge of Core Java, and SQL will be helpful but not mandatory.
We will provide two real-time projects under the guidance of a professional trainer, who will explain you on how to acquire in-depth knowledge on all the concepts involved in these projects.
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Have questions? We’ve got the answers. Get the details on how you can grow in this course.
We have a strong team of professions who are experts in their fields. Our trainers are highly supportive and render a friendly working environment to the students positively stimulating their growth.
We will share you the missed session from our recordings. We at Tekslate maintains a recorded copy of each live course you undergo.
Our Trainers will provide the student with the Server Access ensuring practical real-time experience and training with all the utilities required for the in-depth understanding of the course.
We provide all the training sessions LIVE using either GoToMeeting or WebEx, thus promoting one-on-one trainer student Interaction.
Live training uncovers distinct benefits as they are mighty to reach your desired audience converting your prospects into customers in less time. Pre-recorded videos offer plenty of advantages for entrepreneurs to educate entertain and inspire your audience as long as you want.
You can contact our Tekslate support team, or you can send an email to email@example.com for your queries.
Yes. We provide the course materials available after course completion.
There exist some discounts for weekend batches and group participants if the joiners are more than 2.
If you are enrolled in classes and have paid fees but want to cancel the registration for any reason, we will attain you in 48 hours will be processed within 30 days of prior request.
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Attend a Demo Session | Meet the Expert Who Can Kickstart Your Career in BigData Hadoop
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