Learn to Become a Skilled Hadoop Developer with Spark Course

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Hadoop Developer with Spark Course Overview


The Hadoop Developer with Spark Course is designed to help users learn to develop big data applications using the Apache Hadoop and Apache Spark frameworks. It covers the fundamentals of Hadoop and Spark and their architectures, programming models, and operations, while also providing hands-on activities and demonstrations.
It combines theoretical concepts with practical applications, allowing users to explore the different components of a distributed in-memory computing framework and understand how to construct and deploy applications in the cloud. On completion of the course, users will be able to understand how to set up and utilize a Hadoop and Spark cluster on their own computers. They will also be able to write Spark applications with Scala and create and deploy machine learning algorithms to analyze large datasets.
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The 1-on-1 Advantage

Get 1-on-1 session with our expert trainers at a date & time of your convenience.
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Flexible Dates

Start your session at a date of your choice-weekend & evening slots included, and reschedule if necessary.
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4-Hour Sessions

Training never been so convenient- attend training sessions 4-hour long for easy learning.
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Destination Training

Attend trainings at some of the most loved cities such as Dubai, London, Delhi(India), Goa, Singapore, New York and Sydney.

You will learn:

Module 1: Introduction to Apache Hadoop and the Hadoop Ecosystem
  • Apache Hadoop Overview
  • Data Ingestion and Storage
  • Data Processing
  • Data Analysis and Exploration
  • Other Ecosystem Tools
  • Introduction to the Hands-On Exercises
  • Apache Hadoop Cluster Components
  • HDFS Architecture
  • Using HDFS
  • What is Apache Spark?
  • Starting the Spark Shell
  • Using the Spark Shell
  • Getting Started with Datasets and DataFrames
  • DataFrame Operations
  • Creating DataFrames from Data Sources
  • Saving DataFrames to Data Sources
  • DataFrame Schemas
  • Eager and Lazy Execution
  • Querying DataFrames Using Column Expressions
  • Grouping and Aggregation Queries
  • Joining DataFrames
  • RDD Overview
  • RDD Data Sources
  • Creating and Saving RDDs
  • RDD Operations
  • Writing and Passing Transformation Functions
  • Transformation Execution
  • Converting Between RDDs and DataFrames
  • Key-Value Pair RDDs
  • Map-Reduce
  • Other Pair RDD Operations
  • Querying Tables in Spark Using SQL
  • Querying Files and Views
  • The Catalog API
  • Comparing Spark SQL, Apache Impala, and Apache Hive-on-Spark
  • Datasets and DataFrames
  • Creating Datasets
  • Loading and Saving Datasets
  • Dataset Operations
  • 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
  • Common Apache Spark Use Cases
  • Iterative Algorithms in Apache Spark
  • Machine Learning
  • Example: k-means
  • Apache Spark Streaming Overview
  • Example: Streaming Request Count
  • DStreams
  • Developing Streaming Applications
  • Multi-Batch Operations
  • Time Slicing
  • State Operations
  • Sliding Window Operations
  • Preview: Structured Streaming
  • Streaming Data Source Overview
  • Apache Flume and Apache Kafka Data Sources
  • Example: Using a Kafka Direct Data Source
Live Online Training (Duration : 32 Hours)
We Offer :
  • 1-on-1 Public - Select your own start date. Other students can be merged.
  • 1-on-1 Private - Select your own start date. You will be the only student in the class.

1950 + If you accept merging of other students. Per Participant & excluding VAT/GST
4 Hours
8 Hours
Week Days
Weekend

Start Time : At any time

12 AM
12 PM

1-On-1 Training is Guaranteed to Run (GTR)
Group Training
1500 Per Participant & excluding VAT/GST
Online
09 - 12 Oct
09:00 AM - 05:00 PM CST
(8 Hours/Day)
Online
06 - 09 Nov
09:00 AM - 05:00 PM CST
(8 Hours/Day)
Course Prerequisites

• Good knowledge of basic computer fundamentals
• Knowledge of any programming language such as Java, C#, etc.
• Good knowledge of Database such as SQL, Oracle, etc.
• Familiarity with Hadoop platform
• Knowledge of Linux/Unix operating system and commands
• Experience in any Big Data Frameworks like HBase, Hive, Pig, etc.

Target Audience


The target audience for a Hadoop Developer with Spark training program would be experienced software developers or IT professionals who have a firm understanding of programming languages, data structures, and databases
Familiarity with distributed computing and web development frameworks would be a plus
Additionally, the course should be tailored towards those who are interested in developing distributed computing solutions using Hadoop and Spark
Since the course will focus on the development side of distributed computing, knowledge of other distributed computing and data wrangling technologies such as HDFS and MapReduce is beneficial
Participants should also possess a curiosity for discovering solutions to problems in computational infrastructure
With this course, participants are able to create distributed applications for data analytics, big data management and real-time analytics

Learning Objectives of Hadoop Developer with Spark


1. Understand the fundamentals and internal working of Apache Hadoop and its ecosystem
2. Gain hands-on experience in configuring and managing Hadoop clusters
3. Learn the architectural concepts of Hadoop Storage and Computing
4. Master the fundamental theories and implementation details of Apache Spark
5. Learn to develop Hadoop applications with Java, Scala and other programming languages
6.Explore real time streaming and ETL processing in Apache Spark
7. Learn to use Flume and Sqoop to transfer data to/from Hadoop cluster
8. Understand Big Data deployment, including MapReduce, YARN
9. Analyze big data using Machine Learning and other algorithms
10.Grasp the concepts of Apache Ignite and Kafka for real-time data analysis.
Student Name Feedback
Mohammed Ramzi F Hejazi
United States
A1. Kratika Sharma has given me an incredible experience with the Hadoop Developer with Spark course. Her teaching skills were excellent, she understood the subject at an extremely high level, and was incredibly supportive with any questions that I had. Any doubts I had were immediately resolved, and the layout of the course gave me the ability to use spark, hadoop, and machine learning through data bricks hands on.
Mazoon Mahfoud Al Jabri
United States
A2. great
Ujadan Salim Ali Al Harrasi
United States
A2. excellent
Ibtehal Hamood Khamis Albusaidi
United States
A2. Great
Asma Said Al Hatmi
United States
A2. Where you can improve your skills
Maather Ahmed Al Busaidi
United States
A1. Thank you for your time and your teaching
Amira Rashed Mohammed Alriyami
United States
A2. Good
Shahira Mahmood Al Zaraouni
United States
A1. She explains in fast way and sometimes she skips how to solve the questions.
Maryam Khamis Al Khatri
United States
A1. perfect, perfect, perfect.
Ameera Ali Al Saadi
United States
A2. good
Fatimah Malallah Khamis Al Jabri
United States
A1. Have to be teach the material in an organised way and not faster and try to solve all questions in a good way because not all students are know about material like me this is the first time and am confused till now
Amalabdullah Al Rahbi
United States
A2. GOOD COURSES ,
Maysa Khamis Salem Al-hosni
United States
A2. good
Abeer Saif Said Alsaidi
United States
A1. Good
Moza Rashid Al Ashkhari
United States
A2. nice to deal with smart and wonderful Koeing

FAQ's


You will receive the letter of course attendance post training completion via learning enhancement tool after registration.
Yes you can.
You can request a refund if you do not wish to enroll in the course.
Yes, we do offer corporate training More details
To receive an acknowledgment of your online payment, you should have a valid email address. At the point when you enter your name, Visa, and other data, you have the option of entering your email address. Would it be a good idea for you to decide to enter your email address, confirmation of your payment will be emailed to you.
Yes you can request your customer experience manager for the same.
Yes, course requiring practical include hands-on labs.
Yes, if we have an office in your city.
Yes, we do. For details go to flexi
You can pay through debit/credit card or bank wire transfer.
1-on-1 Public - Select your start date. Other students can be merged.
1-on-1 Private - Select your start date. You will be the only student in the class.
We do accept all major credit cards from Visa, Mastercard, American Express, and Discover.
Yes, Koenig follows a BYOL(Bring Your Own Laptop) policy.
It is recommended but not mandatory. Being acquainted with the basic course material will enable you and the trainer to move at a desired pace during classes.You can access courseware for most vendors.
Buy-Now. Pay-Later option is available using credit card in USA and India only.
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After you submit your payment, you will land on the payment confirmation screen.It contains your payment confirmation message. You will likewise get a confirmation email after your transaction is submitted.
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We use the best standards in Internet security. Any data retained is not shared with third parties.
You can buy online from the page by clicking on "Buy Now". You can view alternate payment method on payment options page.
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Yes, we do accept partial payments, you may use one payment method for part of the transaction and another payment method for other parts of the transaction.
Yes, we do.
Yes, we also offer weekend classes.
Yes, you can pay from the course page and flexi page.
Yes, fee excludes local taxes.
Yes, we do.
The Fee includes:
  • Testing Via Qubits
  • Remote Labs
Yes, Koenig Solutions is a Cloudera Learning Partner
Schedule for Group Training is decided by Koenig. Schedule for 1-on-1 is decided by you.
In 1 on 1 Public you can select your own schedule, other students can be merged. Choose 1-on-1 if published schedule doesn't meet your requirement. If you want a private session, opt for 1-on-1 Private.
Duration of Ultra-Fast Track is 50% of the duration of the Standard Track. Yes(course content is same).
Hadoop is an open source, Java-based programming framework that supports the processing and storage of large data sets in a distributed computing environment. It can quite easily expand from single servers to thousands of machines, each providing computation and storage.
Apache Spark is a fast, in-memory data processing engine with elegant and expressive development APIs. Spark can efficiently execute streaming, machine learning or SQL workloads that require fast iterative access to datasets. Data Scientists commonly use machine learning to decipher large and complex unorganized data and Spark can help them in their need for iterative data processing.
Rescheduling requests should come in at least 24 hours prior to your scheduled appointment. Rescheduling less than 24 hours prior to your appointment will result in forfeiture of your exam fees. All exams are non-refundable and non-transferable. All exam purchases are valid for one year from date of purchase.
Your job prospect will improve on the successful completion of the Hadoop Developer certification. There is a huge demand for Big Data and Data Analytics professionals in the industry and the remuneration they get is also good.
There are no prerequisites for taking this certification. However, this course is best suited for developers and engineers who have prior programming experience. Knowledge of Java is also strongly recommended.
A score report will be emailed to you after you take the exam, typically within a few hours of taking the exam. If you end up successfully clearing the exam, you will receive your digital certificate in three days.
If you pass the exam a certificate in PDF format will be e-mailed to you.
Candidates who are not able to pass the exam must wait for a period of thirty calendar days, beginning the day after the failed attempt, before they may retake the same exam.

Prices & Payments

Yes of course.
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Travel and Visa

Yes we do after your registration for course.

Food and Beverages

Yes.

Others

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