Apache Spark Certification Training

Big Data is the most highly valued technology, this stream is known to be the best career choice. Big data is a wide domain that uses different technologies and frameworks. However, this particular course at Hatigen – Apache Spark Certification Training Course will enhance your skills in the hottest technology of big data. Furthermore, Spark is widely used by most organizations to extract meaningful information from massive data sets. Therefore, this will be the best course to explore your career in the field of big data.


Apache Spark Online Course – Overview

Apache Spark Training Course online enables you to acquire hands-on experience in creating Spark applications with the use of Scala programming. With this course, you will get the clear difference between the two Big Data frameworks – Apache and Spark. Therefore, you’ll learn the technicalities of the Spark framework and use it to increase the performance of the application, enabling high-speed data processing. Furthermore, our expert trainers from the Big Data Analytics domain will train you with their unique teaching methodologies and help you to perfectly handle big data sets and data processing.

Apache Spark Training Course – Key Features

  • Trusted content.
  • Re-learn for free anytime in a year.
  • Rigorous assignments and assessments.
  • Learn at your own pace.
  • Mandatory feedback sessions.
  • Mock-interviews.
  • Hands-on real-time experience.
  • Free mentorship.
  • Live chat for instant solutions.
  • Job ready employees post-training.
  • End-to-end training.
  • Download the certificate after the course.

Apache Spark Course Online – Benefits

The global market of Apache Spark is known to rise high during the period 2019 to 2026 with a CAGR of 32.8%. As the processing speed of Apache Spark is higher than Hadoop, it is highly demandable in the world of Big Data.

Annual Salary
Hiring Companies
Job Wise Benefits
Apache Spark Scala Developer

Hiring Companies

Apache Spark Course Online – Training Options

Self-Paced Learning

£ 1200

  • 1-year access to the Blockchain course content
  • 1 capstone project
  • Multiple assessments
  • Continuous feedback sessions
  • Access to the class recordings
  • Assistance and support
  • Download certification
  • Free mentorship

Online Boot Camp

£ 1000

  • Everything in Self-paced learning +
  • On-spot doubt clarification
  • Interactive training sessions
  • Sessions on the capstone project
  • Live, online classroom training
  • Mock-interviews

Corporate Training

Customized to your team's needs

  • 1-year access to the Blockchain course content
  • 1 capstone project
  • Multiple assessments
  • Continuous feedback sessions
  • Class recordings
  • Assistance and support
  • Certification after the course

Apache Spark Course Online – Curriculum


Graduates who are willing to make their career in the Big Data domain and software engineers who are planning to expand their skills in big data. Also, Data Scientists, Data Engineers, Analytics Professionals, and ETL developers who want to explore and advance their skills can join this course.


If you are willing to learn Spark and Big Data, then basic knowledge about SQL, database, and query language can help you to better grasp the skills. However, as the curriculum of this Apache Spark Certification Training course is designed from the basics, anyone can join this course.

Course Content

  • 1.1 Introducing Scala
  • 1.2 Deployment of Scala for Big Data applications and Apache Spark analytics
  • 1.3 Scala REPL, lazy values, and control structures in Scala
  • 1.4 Directed Acyclic Graph (DAG)
  • 1.5 First Spark application using SBT/Eclipse
  • 1.6 Spark Web UI
  • 1.7 Spark in the Hadoop ecosystem.
  • 2.1 The importance of Scala
  • 2.2 The concept of REPL (Read Evaluate Print Loop)
  • 2.3 Deep dive into Scala pattern matching
  • 2.4 Type interface, higher-order function, currying, traits, application space and Scala for data analysis
  • 3.1 Learning about the Scala Interpreter
  • 3.2 Static object timer in Scala and testing string equality in Scala
  • 3.3 Implicit classes in Scala
  • 3.4 The concept of currying in Scala
  • 3.5 Various classes in Scala
  • 4.1 Learning about the Classes concept
  • 4.2 Understanding the constructor overloading
  • 4.3 Various abstract classes
  • 4.4 The hierarchy types in Scala
  • 4.5 The concept of object equality
  • 4.6 The val and var methods in Scala
  • 5.1 Understanding sealed traits, wild, constructor, tuple, variable pattern, and constant pattern
  • 6.1 Understanding traits in Scala
  • 6.2 The advantages of traits
  • 6.3 Linearization of traits
  • 6.4 The Java equivalent
  • 6.5 Avoiding of boilerplate code
  • 7.1 Implementation of traits in Scala and Java
  • 7.2 Handling of multiple traits extending
  • 8.1 Introduction to Scala collections
  • 8.2 Classification of collections
  • 8.3 The difference between iterator and iterable in Scala
  • 8.4 Example of list sequence in Scala
  • 9.1 The two types of collections in Scala
  • 9.2 Mutable and immutable collections
  • 9.3 Understanding lists and arrays in Scala
  • 9.4 The list buffer and array buffer
  • 9.6 Queue in Scala
  • 9.7 Double-ended queue Deque, Stacks, Sets, Maps, and Tuples in Scala
  • 10.1 Introduction to Scala packages and imports
  • 10.2 The selective imports
  • 10.3 The Scala test classes
  • 10.4 Introduction to JUnit test class
  • 10.5 JUnit interface via JUnit 3 suite for Scala test
  • 10.6 Packaging of Scala applications in the directory structure
  • 10.7 Examples of Spark Split and Spark Scala
  • 11.1 Introduction to Spark
  • 11.2 Spark overcomes the drawbacks of working on MapReduce
  • 11.3 Understanding in-memory MapReduce
  • 11.4 Interactive operations on MapReduce
  • 11.5 Spark stack, fine vs. coarse-grained update,, Spark Hadoop YARN, HDFS Revision, and YARN Revision
  • 11.6 The overview of Spark and how it is better than Hadoop
  • 11.7 Deploying Spark without Hadoop
  • 11.8 Spark history server and Cloudera distribution
  • 12.1 Spark installation guide
  • 12.2 Spark configuration
  • 12.3 Memory management
  • 12.4 Executor memory vs. driver memory
  • 12.5 Working with Spark Shell
  • 12.6 The concept of resilient distributed datasets (RDD)
  • 12.7 Learning to do functional programming in Spark
  • 12.8 The architecture of Spark
  • 13.1 Spark RDD
  • 13.2 Creating RDDs
  • 13.3 RDD partitioning
  • 13.4 Operations and transformation in RDD
  • 13.5 Deep dive into Spark RDDs
  • 13.6 The RDD general operations
  • 13.7 Read-only partitioned collection of records
  • 13.8 Using the concept of RDD for faster and efficient data processing
  • 13.9 RDD action for the collect, count, collects map, save-as-text-files, and pair RDD functions
  • 14.1 Understanding the concept of key-value pair in RDDs
  • 14.2 Learning how Spark makes MapReduce operations faster
  • 14.3 Various operations of RDD
  • 14.4 MapReduce interactive operations
  • 14.5 Fine and coarse-grained update
  • 14.6 Spark stack
  • 15.1 Comparing the Spark applications with Spark Shell
  • 15.2 Creating a Spark application using Scala or Java
  • 15.3 Deploying a Spark application
  • 15.4 Scala built application
  • 15.5 Creation of the mutable list, set and set operations, list, tuple, and concatenating list
  • 15.6 Creating an application using SBT
  • 15.7 Deploying an application using Maven
  • 15.8 The web user interface of Spark application
  • 15.9 A real-world example of Spark
  • 15.10 Configuring of Spark
  • 16.1 Learning about Spark parallel processing
  • 16.2 Deploying on a cluster
  • 16.3 Introduction to Spark partitions
  • 16.4 File-based partitioning of RDDs
  • 16.5 Understanding of HDFS and data locality
  • 16.6 Mastering the technique of parallel operations
  • 16.7 Comparing repartition and coalesce
  • 16.8 RDD actions
  • 17.1 The execution flow in Spark
  • 17.2 Understanding the RDD persistence overview
  • 17.3 Spark execution flow, and Spark terminology
  • 17.4 Distribution shared memory vs. RDD
  • 17.5 RDD limitations
  • 17.6 Spark shell arguments
  • 17.7 Distributed persistence
  • 17.8 RDD lineage
  • 17.9 Key-value pair for sorting implicit conversions like CountByKey, ReduceByKey, SortByKey, and AggregateByKey
  • 18.1 Introduction to Machine Learning
  • 18.2 Types of Machine Learning
  • 18.3 Introduction to MLlib
  • 18.4 Various ML algorithms supported by MLlib
  • 18.5 Linear regression, logistic regression, decision tree, random forest, and K-means clustering techniques
  • Hands-on Exercise:
  • 1. Building a Recommendation Engine
  • 19.1 Why Kafka and what is Kafka?
  • 19.2 Kafka architecture
  • 19.3 Kafka workflow
  • 19.4 Configuring Kafka cluster
  • 19.5 Operations
  • 19.6 Kafka monitoring tools
  • 19.7 Integrating Apache Flume and Apache Kafka
  • Hands-on Exercise:
  • 1. Configuring Single Node Single Broker Cluster
  • 2. Configuring Single Node Multi Broker Cluster
  • 3. Producing and consuming messages
  • 4. Integrating Apache Flume and Apache Kafka
  • 20.1 Introduction to Spark Streaming
  • 20.2 Features of Spark Streaming
  • 20.3 Spark Streaming workflow
  • 20.4 Initializing StreamingContext, discretized Streams (DStreams), input DStreams and Receivers
  • 20.5 Transformations on DStreams, `,output operations on DStreams, windowed operators and why it is useful
  • 20.6 Important windowed operators and stateful operators
  • Hands-on Exercise:
  • 1. Twitter Sentiment analysis
  • 2. Streaming using Netcat server
  • 3. Kafka–Spark streaming
  • 4. Spark–Flume streaming
  • 21.1 Introduction to various variables in Spark like shared variables and broadcast variables
  • 21.2 Learning about accumulators
  • 21.3 The common performance issues
  • 21.4 Troubleshooting the performance problems
  • 22.1 Learning about Spark SQL
  • 22.2 The context of SQL in Spark for providing structured data processing
  • 22.3 JSON support in Spark SQL
  • 22.4 Working with XML data
  • 22.5 Parquet files
  • 22.6 Creating Hive context
  • 22.7 Writing data frame to Hive
  • 22.8 Reading JDBC files
  • 22.9 Understanding the data frames in Spark
  • 22.10 Creating Data Frames
  • 22.11 Manual inferring of schema
  • 22.12 Working with CSV files
  • 22.13 Reading JDBC tables
  • 22.14 Data frame to JDBC
  • 22.15 User-defined functions in Spark SQL
  • 22.16 Shared variables and accumulators
  • 22.17 Learning to query and transform data in data frames
  • 22.18 Data frame provides the benefit of both Spark RDD and Spark SQL
  • 22.19 Deploying Hive on Spark as the execution engine
  • 23.1 Learning about the scheduling and partitioning in Spark
  • 23.2 Hash partition
  • 23.3 Range partition
  • 23.4 Scheduling within and around applications
  • 23.5 Static partitioning, dynamic sharing, and fair scheduling
  • 23.6 Map partition with index, the Zip, and GroupByKey
  • 23.7 Spark master high availability, standby masters with ZooKeeper, single-node recovery with the local file system and high order functions

Apache Spark and Scala Training Course Online – FAQs

Hatigen provides the best training in Big Data, Hadoop, and Spark with its most experienced trainer’s team. With their knowledge and industry-based experience, they have developed the best teaching methodologies through which you can grasp concepts easily. Also, as the course curriculum is designed to include all the foundational concepts, graduates or freshers can join this course. Furthermore, Hatigen also offers year-around mentorship support through which you can clarify your doubts at any time and from any place. Clearly, it is the best place to learn Apache Spark and Scala.

There are multiple courses involved in the Big Data stream at Hatigen which enables you to choose according to your preference and advance your knowledge.

  • Big Data Hadoop and Spark Developer Course.
  • Big Data Certification Master Course
  • Splunk Dev & Admin Certification Training.
  • Apache HBase Certification Training.
  • MongoDB Certification Training.
  • Kafka Certification Training.

Hatigen does not guarantee you a job but it provides 100% job assistance i.e. it helps you to secure your dream job through the intimation of various competitive openings at multiple corporate companies. Hatigen also assists you in your resume and interview preparation through which you can perform well and get a job.

Scala is a high-level language that supports both functional and object-oriented programming. Furthermore, as Apache Spark is written in Scala, it is essential to adopt Scala while learning Apache Spark. Though it is not necessary to master Scala, you can use Spark effectively with intermediate knowledge.