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IT training»IT courses»Bigdata/Hadoop training»Hadoop Spark training

Apache Hadoop Spark Training and Certification

“Hadoop Spark Courses – you will learn with hands on practice, in-class seminars, training and certification from the list of World’s finest trainers”. Below listed Education Institutes provides you course materials, tutorial curriculum, demo videos, sample questions, books, tips and tricks. Get Hadoop Spark lectures by experts and practical lessons by instructors to improve your technical skills. Students will benefit with job placements and visa assistance in the United States.

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Key Highlights

  • Live or virtual instructor-led classes
  • Quality course material provided
  • Become a certified expert on the subject
  • Instant access to reference material
  • Get high-pay jobs offers post-training

Upcoming Instructor-Led Hadoop Spark Class Date & Time as on November 06, 2025

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Big Data Hadoop Spark Training
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Nov 7 2025
Dec 7 2025
Pro Career has the capabilities and expertise to manage online IT training programs that subsequently leads to certification and placement support.
Big Data Hadoop Spark Training
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$0.0000
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About Hadoop Spark training

  • Hadoop Spark

    Apache Spark is an open-source general engine for running large-scale data analytics applications across clustered computers. It can handle both batch and real-time analytics and data processing workloads. It can write applications in Java, Python, R, and Scala. Spark can offer eighty high level operators which makes parallel app building easy. Apache Spark can run programs 100x faster than Hadoop and MapReduce.

    Apache Spark can process data from a variety of data repositories, including the Hadoop Distributed File System (HDFS), NoSQL databases and relational data stores, such as Apache Hive. Spark supports in-memory processing to boost the performance of big data analytics applications, but it can also perform conventional disk-based processing when data sets are too large to fit into the available system memory. Spark was written in Scala, which is considered the primary language for interacting with the Spark, Core engine.

    Our training will help you to understand the Spark framework, understand the interactive analytics, how the deployments are done in the cloud. You will also learn how to access diverse data sources including HDFS, Cassandra, HBase, and S3.If you are aspiring to take a course in data science, data analytics and programming in Big Data analytics, we will help you to gain expertise and step into the Big Data world.

  • Course overview

    Our training course will help you to gain knowledge in spark development and programming. This course will help you to learn about open a Spark Shell, use of some ML algorithms, explore data sets loaded from HDFS, etc., Spark Streaming, Shark, GraphX, and MLlib.

    Our training course will help you to master the essential skills such as Spark Streaming, Spark SQL, Machine Learning Programming, and Shell Scripting Spark. You will also understand about the limitations of MapReduce and role of spark in overcoming these limitations. You will learn to install Spark as a standalone cluster. Our training will help you to understand Spark streaming features.

    Our course will help you to prepare for the various Spark certifications that will endorse your skills and will give market ready acceptance.

  • Pre-requisites

    To learn Apache Spark an individual should have a good understanding of Java basics and concepts of programming.

    For that matter, your knowledge of other programming languages like C, C++, Python and Big data analytics will be beneficial to interpret the topics better

    This course is useful for:

    • ETL Developers and Data Engineers
    • Software Professionals and  Architects
    • Data Scientists 
    • Programmers in Java, C++, Python
    • Big Data Analysts aspiring to learn latest and efficient programming language to process big data in a fast and easy way.

Hadoop Spark syllabus

  • Introduction to Spark

    Introduction to Spark, drawbacks of working MapReduce advantage of working with Spark, understanding in-memory MapReduce, Spark Hadoop YARN, HDFS Revision, YARN Revision, the overview of Spark and how it is better Hadoop, deploying Spark without Hadoop.

  • Spark Basics

    Spark installation guide, Installation as a stand-alone cluster configuration, History of Spark, working with Spark Shell, the concept of Resilient Distributed Datasets (RDD), learning to do functional programming in Spark, the architecture of Spark, Batch VS Real-time processing.

  • Working with RDDs in Spark

    Deep dive into Spark RDDs, the RDD general operations, a read-only partitioned collection of records, using the concept of RDD for faster and efficient data processing.

  • Aggregating Data with Pair RDDs

    Understanding the concept of Key-Value pair in RDDs, learning how Spark makes MapReduce operations faster, various operations of RDD.

  • Writing and Deploying Spark Applications

    Comparing the Spark applications with Spark Shell, creating a Spark application using Scala or Java, deploying a Spark application, the web user interface of Spark application, a real-world example of Spark and configuring of Spark.

  • Parallel Processing

    Learning about Spark parallel processing, deploying on a cluster, introduction to Spark partitions, file-based partitioning of RDDs, understanding of HDFS and data locality, mastering the technique of parallel operations.

  • Spark RDD Persistence

    Understanding the RDD persistence overview, distributed persistence, RDD lineage, Features of RDD, Referencing an external data set, Storage level of RDD persistence

  • Basic Spark Streaming

    Understanding the Spark streaming, creating a Spark stream application, processing of Spark stream, streaming request count, and DStreams.

  • Advanced Spark Streaming

    Spark multi-batch operations, state operations, sliding window operations and advanced data sources.

  • Common Patterns in Spark Data Processing

    Learning about the Spark common use cases, the concept of the iterative algorithm in Spark, analyzing with Spark graph processing, introduction to K-Means and machine learning.

  • Improving Spark Performance

    Variables in Spark - shared variables, broadcast variables, learning about accumulators, the common performance issues and troubleshooting the performance problems.

  • Spark SQL and Data Frames

    Learning about Spark SQ, the context of SQL in Spark for providing structured data processing, understanding the Data Frames in Spark, learning to query and transform data in Data Frames, benefit of Data Frame to both Spark RDD and Spark SQL, deploy Hive on Spark as the execution engine.

  • Scheduling/ Partitioning

    Understand about the scheduling and partitioning in Spark, scheduling within and around applications, static partitioning, dynamic sharing, fair scheduling, Spark master high availability, standby Masters with Zookeeper, Single Node Recovery with Local File System, High Order Functions.

  • Capacity planning in Spark

    Understanding how to design capacity planning in Spark, creation of Maps, Transformations, the concept of concurrency in Java and Scala

  • Introduction to programming in Scala

    Introduction, basic data types, features of Scala, Running a scala application, Basic literals, Introduction to operators, types of operators, introduction to type inference, Mutable collection VS Immutable collection, functions, objects, classes lists, Maps, Maps operators, Running a scala application reading the Hadoop data, Java pair RDD methods, Transformations in RDD

  • Log analysis

    Understanding of log analysis with Spark, first log analyzers in Spark, working with various buffers like array, compact and protocol buffer.

FAQ's on Hadoop Spark training & certification

Certification of Spark

Apart from your practical knowledge of Spark, companies prefer certified candidates to hire. You will get certified after successful completion of the project and submission through LMS. You also have to clear the online exam with 80% marks.

There are plenty of Apache Spark Certifications available online. Our training course will help you to clear those exams too. You can do any of them to become eligible for Spark related jobs. Here, we will discuss some good Apache Spark Certifications.  

Once you have decided to kick start your career in the big data world, take the first step to getting certified as a spark developer. This certification will validate your skills and will give you a distinct edge over your peers.

Some of the best certifications available for Spark certifications are:

1. Cloudera Spark and Hadoop Developer – Cost of the exam 295$ - 2 hours duration

2. HDP Certified Apache spark developer – cost of exam 250$ - 2 hours duration

3. MapR Certified Spark Developer – cost of exam 250$ - 2 hours duration

4. O’Reilly Developer Apache Spark Certifications - Cost of the exam 300$ - duration 1hr 30 mins.

5. Databricks Apache Spark Certifications - Cost of the exam 300$ - duration 1hr 30 mins.

Job and Placement

Apache Spark is used in big data analytics. Spark certified professionals are considered asData Engineers, Java/big data developers, senior big data engineers etc. Spark professionals have a high demand for big data boom. They are taken in various roles and designations depending on their experience and expertise.

The average salary for "apache spark developer" ranges from approximately $108,845 per year for Developer to $157,659 per year.

About our Hadoop Spark Course

Welcome to our interactive and user-friendly Hadoop Spark course, designed specifically for the USA audience. This course is tailored to suit both beginners and professionals looking to upgrade their skills. With us, you will dive into the world of Hadoop Spark, a powerful open-source data processing engine.

Overview of our Hadoop Spark Training

Our Hadoop Spark training is a comprehensive course that covers the essential aspects of Hadoop and Spark. You'll learn about Big Data, the Hadoop ecosystem, and how to use Spark for data processing tasks. This course will equip you with the skills necessary to harness the power of Big Data using Hadoop and Spark.

Objectives of our Hadoop Spark Training Course

This course aims to provide students with a strong foundation in Hadoop and Spark. You'll learn how to process and analyze large datasets, implement data processing algorithms, and use Spark's machine learning library. By the end of this course, you will be able to confidently work with Hadoop and Spark in real-world scenarios.

What You Will Learn in Our Hadoop Spark Classes

  • Understand the basics of Hadoop and its ecosystem.
  • Learn how to use Spark for processing large datasets.
  • Implement data processing algorithms using Spark.
  • Use Spark's machine learning library for predictive analytics.
  • Analyze data using Spark SQL.
  • Work with real-world datasets and scenarios.

Course Details

The course duration is 40 hours, and it's suitable for both beginners and professionals. There are no prerequisites for this course. After course completion, you will receive a certification that validates your skills in Hadoop and Spark.

Course Syllabus

The Hadoop Spark Training course includes the following modules:

  • Introduction to Big Data and Hadoop: Understanding Big Data, Introduction to Hadoop, Hadoop ecosystem, and Hadoop Distributed File System (HDFS).
  • Introduction to Spark: Understanding Spark, Spark architecture, and Spark's Resilient Distributed Datasets (RDD).
  • Data Processing with Spark: Transformations and actions, Key-Value Pair RDD, and Spark SQL.
  • Machine Learning with Spark: Introduction to Spark MLlib, regression, classification, and clustering.

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Hadoop Spark interview questions

  • Mention any one of the important benefits of using Spark over MapReduce?
  • Is Apache Spark is faster than MapReduce?
  • In order to run Apache Spark on Yarn, should the Spark need to be installed in every cluster of Yarn?
  • What is RDD?
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