Apache Spark and Scala Certification Training in London


Apache Spark and Scala Certification Training in London

Flexible Schedule

32 Hrs Project Work & Exercises

24 Hrs Instructor Led online Training

22 Hrs Self-paced Videos

24X7 Support

Certification and Job Assistance

Enquire Now

4.9 out of 1387+ Ratings
ZebLearn Spark training in London is a comprehensive course in a real-time analytical engine that includes the components of Spark Core, SQL, RDD and Machine Learning libraries (MLlib), along with the Scala programming language. In this training program, you will get to work on industry-chosen projects in Spark that give you hands-on experience. Get the best Spark and Scala course in London by the top Apache Spark Experts.


ZebLearn is one of the renowned online training providers who are offering the most competitive and industry-recognized Apache Spark and Scala course in London. After completing this online Spark certification course, learners will be having an in-depth knowledge about Spark’s parallel processing and how Scala is used to write applications in Spark.

In ZebLearn’s Apache Spark course, you will learn about:

  • Basic concepts of Spark and Scala
  • Difference between Spark and Hadoop
  • Writing applications on Spark using Java, Python and Scala
  • Spark RDDs and Dataframes
  • Scala-Java Interoperability
  • Developing codes in Scala
  • Data aggregation and performance improvement in Spark
Apache Spark training in London can be taken by:

  • Software Engineers looking to upgrade Big Data skills
  • Data Engineers and ETL Developers
  • Data Scientists and Analytics Professionals
  • Graduates looking to make a career in Big Data
There are no prerequisites for learning Spark online. Basic knowledge of database, SQL and query language can help.
The cost of the Spark Certification Exam in Bangalore is $295 USD and the duration of the CCA Spark and Hadoop Developer Exam (CCA175) is 120 minutes.
London has emerged as the commercial superpower lately. Due to its lucrative market, the top-notch companies from across the globe have called this city their home. This city has seen tremendous growth in terms of technology due to the application of Big Data technologies. Therefore, the demand for Spark and Scala are constantly rising in this city creating innumerable jobs for aspirants.
With the massive growth in the adoption of research and development, London-based companies have realized the importance of Big Data Analytics and hence are increasingly deploying Spark and Scala together to achieve the maximum benefits. Hence, for those candidates who see themselves as Big Data professionals, learning Spark and Scala will take their career to a whole new level.
Spark and Scala online training course seeks to give an in-depth knowledge to the learners through lab sessions, project works and assignments. This training is aligned with the Spark topics present in Cloudera Spark and Hadoop Developer Certification (CCA175) exam. Hence, enrolling to this course will help you score high in this exam.

What you will gain?

  • 1 to 1 Live Online Training
  • Dedicated 24 x 7 Support
  • Flexible Class Timing
  • Training Completion Certification
  • Direct Access to the Trainer
  • Lifetime Access of an LMS
  • Real-time Projects
  • Dedicated Placement Support


Self Paced Training

22 Hrs e-learning videos
Lifetime Free Upgrade
24×7 Lifetime Support & Access


Online Live One to One Training

Everything in self-paced, plus
24 Hrs of Instructor-led Training
1:1 Doubt Resolution Sessions
Attend as many batches for Lifetime


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 stack, 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:

  • Configuring Single Node Single Broker Cluster
  • Configuring Single Node Multi Broker Cluster
  • Producing and consuming messages
  • 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:

  • Twitter Sentiment analysis
  • Streaming using Netcat server
  • Kafka–Spark streaming
  • 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

Benefits of Online Training

  •  100% Satisfaction Ratio
  •  Dedicated Help In Global Examination
  •  Updated Syllabus & On-Demand Doubt Session
  •  Special Group & Corporate Discounts


ZebLearn is the pioneer in Hadoop training in India. So, it pays to be with the market leader like ZebLearn to learn Spark and Scala and get the best jobs in top MNCs for top salaries. The ZebLearn training is the most comprehensive course that includes real-time projects and assignments which are designed by industry experts. The entire course content is fully aligned towards clearing the exam for the Apache Spark component of the Cloudera Spark and Hadoop Developer Certification (CCA175) exam.

ZebLearn offers lifetime access to videos, course materials, 24/7 support and course material upgrades to the latest version at no extra fee. For Hadoop and Spark training, you get the ZebLearn Proprietary Virtual Machine for lifetime and free cloud access for 6 months for performing training exercises. Hence, it is clearly a one-time investment.

At ZebLearn, you can enroll in either the instructor-led online training or self-paced training. Apart from this, ZebLearn also offers corporate training for organizations to upskill their workforce. All trainers at ZebLearn have 12+ years of relevant industry experience, and they have been actively working as consultants in the same domain, which has made them subject matter experts. Go through the sample videos to check the quality of our trainers.
ZebLearn is offering the 24/7 query resolution, and you can raise a ticket with the dedicated support team at anytime. You can avail of the email support for all your queries. If your query does not get resolved through email, we can also arrange one-on-one sessions with our trainers.

You would be glad to know that you can contact ZebLearn support even after the completion of the training. We also do not put a limit on the number of tickets you can raise for query resolution and doubt clearance.

ZebLearn is offering you the most updated, relevant, and high-value real-world projects as part of the training program. This way, you can implement the learning that you have acquired in real-world industry setup. All training comes with multiple projects that thoroughly test your skills, learning, and practical knowledge, making you completely industry-ready.

You will work on highly exciting projects in the domains of high technology, ecommerce, marketing, sales, networking, banking, insurance, etc. After completing the projects successfully, your skills will be equal to 6 months of rigorous industry experience.

ZebLearn actively provides placement assistance to all learners who have successfully completed the training. For this, we are exclusively tied-up with over 80 top MNCs from around the world. This way, you can be placed in outstanding organizations such as Sony, Ericsson, TCS, Mu Sigma, Standard Chartered, Cognizant, and Cisco, among other equally great enterprises. We also help you with the job interview and résumé preparation as well.
You can definitely make the switch from self-paced training to online instructor-led training by simply paying the extra amount. You can join the very next batch, which will be duly notified to you.
Once you complete ZebLearn’s training program, working on real-world projects, quizzes, and assignments and scoring at least 60 percent marks in the qualifying exam, you will be awarded ZebLearn’s course completion certificate. This certificate is very well recognized in ZebLearn-affiliated organizations, including over 80 top MNCs from around the world and some of the Fortune 500companies.
Apparently, no. Our job assistance program is aimed at helping you land in your dream job. It offers a potential opportunity for you to explore various competitive openings in the corporate world and find a well-paid job, matching your profile. The final decision on hiring will always be based on your performance in the interview and the requirements of the recruiter.

Recently Trained Students

Jessica Biel

– Infosys

My instructor had sound Knowledge and used to puts a lot of effort that made the course as simple and easy as possible. I was aiming for with the help of the ZebLearn Online training imparted to me by this organization.

Richard Harris


I got my training from Gaurav sir, I would like to say that say he is one of the best trainers. He has not even trained me but also motivated me to explore more and the way he executed the project, in the end, was mind-blowing.

Job Opportunities

Apache Spark is a data processing framework that can quickly perform processing tasks on very large data sets, and can also distribute data processing tasks across multiple computers, either on its own or in tandem with other distributed computing tools.

Apache Spark alone is a very powerful tool. It is in high demand in the job market. If integrated with other tools of Big Data, it makes a strong portfolio. Today, Big Data market is booming and many individuals are making use of it.

Know more

Job Designation

  • Apache Spark
  • Scala Developer with spark
  • Apache Spark Application Developer
  • Apache Spark Data Architect
  • Big Data Engineer – Apache Spark

Placement Partner


Leave your details


Download Course Content