Course description

  • Why learn Apache Spark and Scala?

    Why learn Apache Spark and Scala

  • What are the course objectives?

    Simplilearn’s Apache Spark and Scala certification training are designed to:
    • Advance your expertise in the Big Data Hadoop Ecosystem
    • Help you master essential Apache and Spark skills, such as Spark Streaming, Spark SQL, machine learning programming, GraphX programming and Shell Scripting Spark
    •  Help you land a Hadoop developer job requiring Apache Spark expertise by giving you  a real-life industry project coupled with 30 demos

  • What skills will you learn?

    By completing this Apache Spark and Scala course you will be able to:
    • Understand the limitations of MapReduce and the role of Spark in overcoming these limitations
    • Understand the fundamentals of the Scala programming language and its features
    • Explain and master the process of installing Spark as a standalone cluster
    • Develop expertise in using Resilient Distributed Datasets (RDD) for creating applications in Spark
    • Master Structured Query Language (SQL) using SparkSQL
    • Gain a thorough understanding of Spark streaming features
    • Master and describe the features of Spark ML programming and GraphX programming

  • Who should take this Scala course?

    • Professionals aspiring for a career in the field of real-time big data analytics
    • Analytics professionals
    • Research professionals
    • IT developers and testers
    • Data scientists
    • BI and reporting professionals
    • Students who wish to gain a thorough understanding of Apache Spark

  • What projects are included in this Spark training course?

    This Apache Spark and Scala training course has one project. In this project scenario, a U.S.based university has collected datasets which represent reviews of movies from multiple reviewers. To gain in-depth insights from the research data collected, you must perform a series of tasks in Spark on the dataset provided.

Course preview

    • Lesson 00 - Course Overview 04:11
      • 0.001 Introduction 00:12
      • 0.002 Course Objectives 00:28
      • 0.003 Course Overview 00:38
      • 0.004 Target Audience 00:31
      • 0.005 Course Prerequisites 00:21
      • 0.006 Value to the Professionals 00:48
      • 0.007 Value to the Professionals (contd.) 00:20
      • 0.008 Value to the Professionals (contd.) 00:21
      • 0.009 Lessons Covered 00:24
      • 0.010 Conclusion 00:08
    • Lesson 01 - Introduction to Spark 25:17
      • 1.001 Introduction 00:15
      • 1.002 Objectives 00:26
      • 1.3 Evolution of Distributed Systems
      • 1.004 Need of New Generation Distributed Systems 01:15
      • 1.005 Limitations of MapReduce in Hadoop 01:05
      • 1.006 Limitations of MapReduce in Hadoop (contd.) 01:06
      • 1.007 Batch vs. Real-Time Processing 01:09
      • 3.040 PairRDD Methods-Others 00:06
      • 1.009 Application of In-Memory Processing 01:47
      • 1.010 Introduction to Apache Spark 00:44
      • 1.11 Components of a Spark Project
      • 1.012 History of Spark 00:50
      • 1.013 Language Flexibility in Spark 00:54
      • 1.014 Spark Execution Architecture 01:13
      • 1.015 Automatic Parallelization of Complex Flows 00:58
      • 1.016 Automatic Parallelization of Complex Flows-Important Points 01:13
      • 1.017 APIs That Match User Goals 01:05
      • 1.018 Apache Spark-A Unified Platform of Big Data Apps 01:37
      • 1.019 More Benefits of Apache Spark 01:05
      • 1.020 Running Spark in Different Modes 00:40
      • 1.21 Installing Spark as a Standalone Cluster-Configurations
      • 1.022 Installing Spark as a Standalone Cluster-Configurations 00:07
      • 1.023 Demo-Install Apache Spark 00:07
      • 1.024 Demo-Install Apache Spark 02:40
      • 1.025 Overview of Spark on a Cluster 00:47
      • 1.026 Tasks of Spark on a Cluster 00:36
      • 1.027 Companies Using Spark-Use Cases 00:46
      • 1.028 Hadoop Ecosystem vs. Apache Spark 00:31
      • 1.029 Hadoop Ecosystem vs. Apache Spark (contd.) 00:42
      • 1.30 Quiz
      • 1.031 Summary 00:39
      • 1.032 Summary (contd.) 00:41
      • 1.033 Conclusion 00:13
    • Lesson 02 - Introduction to Programming in Scala 37:15
      • 2.001 Introduction 00:11
      • 2.002 Objectives 00:16
      • 2.003 Introduction to Scala 01:32
      • 2.4 Features of Scala
      • 2.005 Basic Data Types 00:24
      • 2.006 Basic Literals 00:34
      • 2.007 Basic Literals (contd.) 00:24
      • 2.008 Basic Literals (contd.) 00:21
      • 2.009 Introduction to Operators 00:31
      • 2.10 Types of Operators
      • 2.011 Use Basic Literals and the Arithmetic Operator 00:07
      • 2.012 Demo Use Basic Literals and the Arithmetic Operator 03:17
      • 2.013 Use the Logical Operator 00:07
      • 2.014 Demo Use the Logical Operator 01:40
      • 2.015 Introduction to Type Inference 00:33
      • 2.016 Type Inference for Recursive Methods 00:09
      • 2.017 Type Inference for Polymorphic Methods and Generic Classes 00:30
      • 2.018 Unreliability on Type Inference Mechanism 00:22
      • 2.019 Mutable Collection vs. Immutable Collection 01:13
      • 2.020 Functions 00:21
      • 2.021 Anonymous Functions 00:21
      • 2.022 Objects 01:07
      • 2.023 Classes 00:36
      • 2.024 Use Type Inference, Functions, Anonymous Function, and Class 00:09
      • 2.025 Demo Use Type Inference, Functions, Anonymous Function and Class 07:39
      • 2.026 Traits as Interfaces 00:57
      • 2.027 Traits-Example 00:08
      • 2.028 Collections 00:41
      • 2.029 Types of Collections 00:25
      • 2.030 Types of Collections (contd.) 00:26
      • 2.031 Lists 00:28
      • 2.032 Perform Operations on Lists 00:07
      • 2.033 Demo Use Data Structures 04:09
      • 2.034 Maps 00:45
      • 2.35 Maps-Operations
      • 2.036 Pattern Matching 00:33
      • 2.037 Implicits 00:36
      • 2.038 Implicits (contd.) 00:17
      • 2.039 Streams 00:21
      • 2.040 Use Data Structures 00:07
      • 2.041 Demo Perform Operations on Lists 03:24
      • 2.42 Quiz
      • 2.043 Summary 00:37
      • 2.044 Summary (contd.) 00:36
      • 2.045 Conclusion 00:14
    • Lesson 03 - Using RDD for Creating Applications in Spark 50:27
      • 3.001 Introduction 00:11
      • 3.002 Objectives 00:22
      • 3.003 RDDs API 01:39
      • 3.4 Features of RDDs
      • 3.005 Creating RDDs 00:36
      • 3.006 Creating RDDs-Referencing an External Dataset 00:18
      • 3.007 Referencing an External Dataset-Text Files 00:51
      • 3.008 Referencing an External Dataset-Text Files (contd.) 00:49
      • 3.009 Referencing an External Dataset-Sequence Files 00:32
      • 3.010 Referencing an External Dataset-Other Hadoop Input Formats 00:46
      • 3.011 Creating RDDs-Important Points 01:08
      • 3.012 RDD Operations 00:37
      • 3.013 RDD Operations-Transformations 00:47
      • 3.014 Features of RDD Persistence 00:56
      • 3.015 Storage Levels Of RDD Persistence 00:19
      • 3.16 Choosing The Correct RDD Persistence Storage Level
      • 3.017 Invoking the Spark Shell 00:22
      • 3.018 Importing Spark Classes 00:14
      • 3.019 Creating the SparkContext 00:25
      • 3.020 Loading a File in Shell 00:10
      • 3.021 Performing Some Basic Operations on Files in Spark Shell RDDs 00:20
      • 3.022 Packaging a Spark Project with SBT 00:50
      • 3.023 Running a Spark Project With SBT 00:31
      • 3.024 Demo-Build a Scala Project 00:06
      • 3.025 Build a Scala Project 06:50
      • 3.026 Demo-Build a Spark Java Project 00:07
      • 3.027 Build a Spark Java Project 04:31
      • 3.028 Shared Variables-Broadcast 01:20
      • 3.029 Shared Variables-Accumulators 00:51
      • 3.030 Writing a Scala Application 00:20
      • 3.031 Demo-Run a Scala Application 00:07
      • 3.032 Run a Scala Application 01:43
      • 3.033 Demo-Write a Scala Application Reading the Hadoop Data 00:07
      • 3.034 Write a Scala Application Reading the Hadoop Data 01:22
      • 3.035 Demo-Run a Scala Application Reading the Hadoop Data 00:07
      • 3.036 Run a Scala Application Reading the Hadoop Data 02:21
      • 3.37 Scala RDD Extensions
      • 3.038 DoubleRDD Methods 00:08
      • 3.039 PairRDD Methods-Join 00:46
      • 3.040 PairRDD Methods-Others 00:06
      • 3.041 Java PairRDD Methods 00:09
      • 3.042 Java PairRDD Methods (contd.) 00:05
      • 3.043 General RDD Methods 00:05
      • 3.044 General RDD Methods (contd.) 00:05
      • 3.045 Java RDD Methods 00:07
      • 3.046 Java RDD Methods (contd.) 00:06
      • 3.047 Common Java RDD Methods 00:09
      • 3.048 Spark Java Function Classes 00:12
      • 3.049 Method for Combining JavaPairRDD Functions 00:41
      • 3.050 Transformations in RDD 00:33
      • 3.051 Other Methods 00:07
      • 3.052 Actions in RDD 00:08
      • 3.053 Key-Value Pair RDD in Scala 00:31
      • 3.054 Key-Value Pair RDD in Java 00:43
      • 3.055 Using MapReduce and Pair RDD Operations 00:24
      • 3.056 Reading Text File from HDFS 00:16
      • 3.057 Reading Sequence File from HDFS 00:21
      • 3.058 Writing Text Data to HDFS 00:18
      • 3.059 Writing Sequence File to HDFS 00:12
      • 3.060 Using GroupBy 00:07
      • 3.061 Using GroupBy (contd.) 00:05
      • 3.062 Demo-Run a Scala Application Performing GroupBy Operation 00:07
      • 3.063 Run a Scala Application Performing GroupBy Operation 03:12
      • 3.064 Demo-Run a Scala Application Using the Scala Shell 00:06
      • 3.065 Run a Scala Application Using the Scala Shell 04:02
      • 3.066 Demo-Write and Run a Java Application 00:06
      • 3.067 Write and Run a Java Application 01:48
      • 3.68 Quiz
      • 3.069 Summary 00:53
      • 3.070 Summary (contd.) 00:59
      • 3.071 Conclusion 00:15
    • Lesson 04 - Running SQL Queries Using Spark SQL 39:26
      • 4.001 Introduction 00:11
      • 4.002 Objectives 00:17
      • 4.003 Importance of Spark SQL 01:01
      • 4.004 Benefits of Spark SQL 00:47
      • 4.005 DataFrames 00:50
      • 4.006 SQLContext 00:50
      • 4.007 SQLContext (contd.) 01:12
      • 4.008 Creating a DataFrame 00:10
      • 4.009 Using DataFrame Operations 00:21
      • 4.010 Using DataFrame Operations (contd.) 00:05
      • 4.011 Demo-Run SparkSQL with a Dataframe 00:06
      • 4.012 Run SparkSQL with a Dataframe 08:52
      • 4.13 Interoperating with RDDs
      • 4.014 Using the Reflection-Based Approach 00:38
      • 4.015 Using the Reflection-Based Approach (contd.) 00:08
      • 4.016 Using the Programmatic Approach 00:44
      • 4.017 Using the Programmatic Approach (contd.) 00:06
      • 4.018 Demo-Run Spark SQL Programmatically 00:08
      • 4.019 Run Spark SQL Programmatically 09:20
      • 4.20 Data Sources
      • 4.021 Save Modes 00:31
      • 4.022 Saving to Persistent Tables 00:45
      • 4.023 Parquet Files 00:18
      • 4.024 Partition Discovery 00:37
      • 4.025 Schema Merging 00:28
      • 4.026 JSON Data 00:34
      • 4.027 Hive Table 00:45
      • 4.028 DML Operation-Hive Queries 00:27
      • 4.029 Demo-Run Hive Queries Using Spark SQL 00:06
      • 4.030 Run Hive Queries Using Spark SQL 04:58
      • 4.031 JDBC to Other Databases 00:49
      • 4.032 Supported Hive Features 00:38
      • 4.033 Supported Hive Features (contd.) 00:22
      • 4.034 Supported Hive Data Types 00:13
      • 4.035 Case Classes 00:14
      • 4.036 Case Classes (contd.) 00:07
      • 4.37 Quiz
      • 4.038 Summary 00:48
      • 4.039 Summary (contd.) 00:48
      • 4.040 Conclusion 00:12
    • Lesson 05 - Spark Streaming 34:48
      • 5.001 Introduction 00:11
      • 5.002 Objectives 00:14
      • 5.003 Introduction to Spark Streaming 00:49
      • 5.004 Working of Spark Streaming 00:19
      • 5.5 Features of Spark Streaming
      • 5.006 Streaming Word Count 01:34
      • 5.007 Micro Batch 00:19
      • 5.008 DStreams 00:34
      • 5.009 DStreams (contd.) 00:38
      • 5.010 Input DStreams and Receivers 01:19
      • 5.011 Input DStreams and Receivers (contd.) 00:54
      • 5.012 Basic Sources 01:14
      • 5.013 Advanced Sources 00:49
      • 5.14 Advanced Sources-Twitter
      • 5.015 Transformations on DStreams 00:15
      • 5.016 Transformations on Dstreams (contd.) 00:06
      • 5.017 Output Operations on DStreams 00:29
      • 5.018 Design Patterns for Using ForeachRDD 01:14
      • 5.019 DataFrame and SQL Operations 00:25
      • 5.020 DataFrame and SQL Operations (contd.) 00:20
      • 5.021 Checkpointing 01:25
      • 5.022 Enabling Checkpointing 00:37
      • 5.023 Socket Stream 00:59
      • 5.024 File Stream 00:11
      • 5.025 Stateful Operations 00:28
      • 5.026 Window Operations 01:22
      • 5.027 Types of Window Operations 00:11
      • 5.028 Types of Window Operations Types (contd.) 00:05
      • 5.029 Join Operations-Stream-Dataset Joins 00:20
      • 5.030 Join Operations-Stream-Stream Joins 00:33
      • 5.031 Monitoring Spark Streaming Application 01:18
      • 5.032 Performance Tuning-High Level 00:20
      • 5.33 Performance Tuning-Detail Level
      • 5.034 Demo-Capture and Process the Netcat Data 00:07
      • 5.035 Capture and Process the Netcat Data 05:00
      • 5.036 Demo-Capture and Process the Flume Data 00:07
      • 5.037 Capture and Process the Flume Data 05:08
      • 5.038 Demo-Capture the Twitter Data 00:06
      • 5.039 Capture the Twitter Data 02:33
      • 5.40 Quiz
      • 5.041 Summary 01:00
      • 5.042 Summary (contd.) 01:04
      • 5.043 Conclusion 00:11
    • Lesson 06 - Spark ML Programming 39:50
      • 6.001 Introduction 00:11
      • 6.002 Objectives 00:19
      • 6.003 Introduction to Machine Learning 01:35
      • 6.4 Common Terminologies in Machine Learning
      • 6.005 Applications of Machine Learning 00:21
      • 6.006 Machine Learning in Spark 00:33
      • 6.7 Spark ML API
      • 6.008 DataFrames 00:32
      • 6.009 Transformers and Estimators 00:59
      • 6.010 Pipeline 00:48
      • 6.011 Working of a Pipeline 01:41
      • 6.012 Working of a Pipeline (contd.) 00:44
      • 6.013 DAG Pipelines 00:33
      • 6.014 Runtime Checking 00:20
      • 6.015 Parameter Passing 00:59
      • 6.016 General Machine Learning Pipeline-Example 00:06
      • 6.17 General Machine Learning Pipeline-Example (contd.)
      • 6.018 Model Selection via Cross-Validation 01:15
      • 6.019 Supported Types, Algorithms, and Utilities 00:30
      • 6.020 Data Types 01:25
      • 6.021 Feature Extraction and Basic Statistics 00:42
      • 6.022 Clustering 00:37
      • 6.023 K-Means 00:55
      • 6.024 K-Means (contd.) 00:05
      • 6.025 Demo-Perform Clustering Using K-Means 00:07
      • 6.026 Perform Clustering Using K-Means 04:41
      • 6.027 Gaussian Mixture 00:57
      • 6.028 Power Iteration Clustering (PIC) 01:16
      • 6.029 Latent Dirichlet Allocation (LDA) 00:34
      • 6.030 Latent Dirichlet Allocation (LDA) (contd.) 01:45
      • 6.031 Collaborative Filtering 01:13
      • 6.032 Classification 00:16
      • 6.033 Classification (contd.) 00:06
      • 6.034 Regression 00:41
      • 6.035 Example of Regression 00:56
      • 6.036 Demo-Perform Classification Using Linear Regression 00:07
      • 6.037 Perform Classification Using Linear Regression 02:00
      • 6.038 Demo-Run Linear Regression 00:06
      • 6.039 Run Linear Regression 02:14
      • 6.040 Demo-Perform Recommendation Using Collaborative Filtering 00:05
      • 6.041 Perform Recommendation Using Collaborative Filtering 02:23
      • 6.042 Demo-Run Recommendation System 00:06
      • 6.043 Run Recommendation System 02:45
      • 6.44 Quiz
      • 6.045 Summary 01:14
      • 6.046 Summary (contd.) 00:57
      • 6.047 Conclusion 00:11
    • Lesson 07 - Spark GraphX Programming 46:16
      • 7.001 Introduction 00:13
      • 7.002 Objectives 00:17
      • 7.003 Introduction to Graph-Parallel System 01:13
      • 7.004 Limitations of Graph-Parallel System 00:49
      • 7.005 Introduction to GraphX 01:21
      • 7.006 Introduction to GraphX (contd.) 00:06
      • 7.007 Importing GraphX 00:10
      • 7.008 The Property Graph 01:25
      • 7.009 The Property Graph (contd.) 00:06
      • 7.010 Features of the Property Graph
      • 7.011 Creating a Graph 00:13
      • 7.012 Demo-Create a Graph Using GraphX 00:07
      • 7.013 Create a Graph Using GraphX 10:08
      • 7.014 Triplet View 00:30
      • 7.015 Graph Operators 00:50
      • 7.016 List of Operators 00:23
      • 7.017 List of Operators (contd.) 00:05
      • 7.018 Property Operators 00:18
      • 7.019 Structural Operators 01:02
      • 7.020 Subgraphs 00:21
      • 7.021 Join Operators 01:09
      • 7.022 Demo-Perform Graph Operations Using GraphX 00:07
      • 7.023 Perform Graph Operations Using GraphX 05:46
      • 7.024 Demo-Perform Subgraph Operations 00:07
      • 7.025 Perform Subgraph Operations 01:36
      • 7.026 Neighborhood Aggregation 00:43
      • 7.027 mapReduceTriplets 00:41
      • 7.028 Demo-Perform MapReduce Operations 00:08
      • 7.029 Perform MapReduce Operations 09:17
      • 7.030 Counting Degree of Vertex 00:32
      • 7.031 Collecting Neighbors 00:28
      • 7.032 Caching and Uncaching 01:09
      • 7.033 Graph Builders
      • 7.034 Vertex and Edge RDDs 01:17
      • 7.035 Graph System Optimizations 01:21
      • 7.036 Built-in Algorithms
      • 7.037 Quiz
      • 7.038 Summary 01:12
      • 7.039 Summary (contd.) 00:55
      • 7.040 Conclusion 00:11
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Exam & certification

  • How do I become a certified Apache Spark and Scala professional?

    To become a Certified Apache Spark and Scala professional it is mandatory to fulfill both of the following criteria:
    • You must complete a project given by Simplilearn that is evaluated by the lead trainer. Your project may be submitted through the learning management system (LMS). If you have any questions or difficulties while working on the project, you may get assistance and clarification from our experts at SimpliTalk. If you have any further issues you may look to our Online Classroom Training, where you may attend any of the ongoing batches of Apache Spark and Scala Certification Training classes to get help with your project.
    •  A minimum score of 80 percent is required to pass the online examination. If you don’t pass the online exam on the first attempt, you are allowed to retake the exam once.
    • At the end of the Scala course, you will receive an experience certificate stating that you have three months experience implementing Spark and Scala.

  • What are the prerequisites for the Scala course?

    The prerequisites for the Apache Spark and Scala course are:
    • Fundamental knowledge of any programming language
    • Basic understanding of any database, SQL and query language for databases
    • Working knowledge of Linux- or Unix-based systems (not mandatory)
    • Certification training as a Big Data Hadoop developer (recommended)

  • What do I need to do to unlock my Simplilearn certificate?

    Online Classroom:
    • Attend one complete batch
    • Complete one project and one simulation test with a minimum score of 60 percent
    Online Self-Learning:
    • Complete 85 percent of the course
    • Complete one project and one simulation test with a minimum score of 60 percent

  • Who provides the certification?

    After successful completion of the Apache Spark & Scala course, you will be awarded the course completion certificate from Simplilearn.

  • Is this course accredited?

    No, this course is not officially accredited.

  • What is the passing score for the Apache Spark & Scala exam?

    The Apache Spark & Scala certification exam is 120 minutes long and comprises  60 single or multiple choice questions. The passing score for the exam is 80% (i.e you must answer 48 questions correctly).

  • How many attempts do I have to pass the Apache & Scala exam?

    You have a maximum of two total attempts to pass the exam. You may re-attempt it immediately if you fail the first time. 

  • How long does it take to receive the Apache & Scala course certification?

    Upon successful completion of the Simplilearn’s Apache Scala & Spark online training, you will immediately receive the Apache & Scala course certificate.

  • How long is the Apache & Scala Certification valid for?

    The Apache Spark & Scala certification from Simplilearn has lifelong validity.

  • I have passed the Apache & Scala exam. When and how do I receive my certificate?

    Upon successful completion of this apache spark and scala course online and passing the exam, you will receive the certificate through our Learning Management System, which you can download or share via email or Linkedin.

  • Do you offer a money-back guarantee for the training program?

    Yes. We do offer a money-back guarantee for many of our training programs. Refer to our Refund Policy and submit refund requests via our Help and Support portal.

Course advisor

Ronald van Loon
Ronald van Loon Top 10 Big Data & Data Science Influencer, Director - Adversitement

Named by Onalytica as one of the three most influential people in Big Data, Ronald is also an author for a number of leading Big Data and Data Science websites, including Datafloq, Data Science Central, and The Guardian. He also regularly speaks at renowned events.

Reviews

Peter Dao
Peter Dao Senior Technical Analyst at Sutter Health

Instructor is very experienced in these topics. I like the examples given in the classes.

Amit Pradhan
Amit Pradhan Assistant Manager at HT Media

It was really a great learning experience. Big Data course has been instrumental in laying the foundation for the beginners, both in terms of conceptual content as well as the practical lab. Thanks to Simplilearn team that it was not less than a live classroom..Really Appreciate it..

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Aravinda Reddy
Aravinda Reddy Lead Software Engineer at Thomson Reuters

The training has been very good. Trainer was right on the targeted agenda with great technical skills. He covered all the topics with good number of examples and allowed us to do hands-on as well.

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Martin Stufi
Martin Stufi C.E.O - Solutia, s.r.o.

Great course! I really recommend it!

Anjaneya Prasad Nidubrolu
Anjaneya Prasad Nidubrolu Assistant Consultant at Tata Consultancy Services

Well-structured course and the instructor is very good. He has a good grip on the subject and clears our doubts instantly and he makes sure that all the students understand things correctly.

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Olga Barrett
Olga Barrett Career Advisor @ CV Wizard of OZ

Great Class. Very interactive. Overview of HDFS and MapReduce was very helpful, it was smooth transition to the Apache Spark and Scala knowledge. The content is good. Overall, excellent training.

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Nagarjuna D N
Nagarjuna D N AT&T

Getting a high quality training from industry expert at your convenience, affordable with the resources you need to master what you are learning.

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Vinod JV
Vinod JV Lead Software Engineer at Thomson Reuters

The trainer has excellent knowledge on the subject and is very thorough in answering the doubts. I hope Simplilearn will always continue to give trainers like this.

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Arijit Chatterjee
Arijit Chatterjee Senior Consultant at Capgemini

It was really a wonderful experience to have such real-time project discussion during the training session. It helped to learn in depth.

FAQs

  • What are the system requirements for taking this course?

    Your system must fulfill  the following requirements:
    • 64-bit Operating System
    • 8GB RAM

  • How will the labs be conducted?

    We will help you set up a virtual machine with local access. The detailed installation guide is provided in the LMS.

  • How is the project completed and how do I get certified?

    Everything you need to complete your project, such as, problem statements and data points, are provided for you in the LMS. If you have other questions, you can contact us.
     
    After completing the Scala course, you will submit your finished project to the trainer for evaluation. Upon successful evaluation of the project and completion of the online exam, you will get certified as a Spark and Scala Professional.
     

  • Who are the instructors/trainers and how are they selected?

    All of our highly-qualified instructors are Apache Spark and Scala certified, with more than 15 years of experience in training and working professionally in the field. Each of them has gone through a rigorous selection process that includes profile screening, technical evaluation and live training demonstration before they are certified to train for us. We also ensure that only those trainers who maintain a high alumni rating continue to train for us.

  • What are the modes of training offered for this Scala Training course?

    We offer two modes of training:
     
    Live Virtual Classroom or Online Classroom: With instructor led online classroom training, you have the option to attend the course remotely from your desktop or laptop via video conferencing. This format improves productivity and decreases time spent away from work or home.
     
    Online Self-Learning: In this mode you will receive lecture videos which you can review at your own pace.

  • What if I miss a class?

    We provide recordings of the class after the session is conducted, so you can catch-up on training before the next session.
     

  • Can I cancel my enrolment? Do I get a refund?

    Yes, you can cancel your enrolment. We provide a complete refund after deducting the administration fee. To know more, please go through our Refund Policy.

  • How do I enroll for the online training?

    You can enroll for this Scala training on our website and make an online payment using any of the following options: 
    • Visa Credit or Debit Card
    • MasterCard
    • American Express
    • Diner’s Club
    • PayPal
    Once payment is received you will automatically receive a payment receipt and access information via email.

  • I’d like to learn more about this training program. Whom should I contact?

    Contact us using the form on the right of any page on the Simplilearn website, or select the Live Chat link. Our customer service representatives can provide you with more details.

  • What is Global Teaching Assistance?

    Our teaching assistants are a dedicated team of subject matter experts here to help you get certified in your first attempt. They engage students proactively to ensure the course path is being followed and help you enrich your learning experience, from class onboarding to project mentoring and job assistance. Teaching Assistance is available during business hours.

  • What is covered under the 24/7 Support promise?

    We offer 24/7 support through email, chat and calls. We also have a dedicated team that provides on-demand assistance through our community forum. What’s more, you will have lifetime access to the community forum, even after completion of your course with us.

    • Disclaimer
    • PMP, PMI, PMBOK, CAPM, PgMP, PfMP, ACP, PBA, RMP, SP, and OPM3 are registered marks of the Project Management Institute, Inc.