Course Description

  • Why learn Apache Spark Certification?

    Being a cluster computing framework that can handle and process a huge amount of data of almost all kinds in quick time. Apache Spark has risen as the Big Data engine that can be used for all kinds of data processing. This is why more businesses and organizations are looking at implementing Apache Spark for their Big Data needs, which has led to increased demand for the Apache Spark professionals. Here is why you should learn Apache Spark:

    • Due to its increasing popularity, there are ample opportunities for you to start your career in Apache Spark.
    • It interacts and supports Hadoop MapReduce. If you know Hadoop then it is easier for you to learn and understand this Big Data platform.
    • It supports multiple programming languages, including Java, Python, Scala, R, and others, which keeps it away from all kind of limitations.
    • It can handle a huge amount of data easily and that too very quickly, unlike other Big Data platforms.
    • It is the fastest growing Big Data platform with organizations, like NASA, NVIDIA, and Adobe already implementing it. This provides you with a plethora of opportunities to grow in the Big Data industry.
    • You can earn big money if you choose to have a career in Apache Spark.

  • 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.

  • Do you provide any practice tests as part of this course?

    Yes, we provide 1 practice test as part of our course to help you prepare for the actual certification exam. You can try this Spark and Scala Exam Questions - Free Practice Test to understand the type of tests that are part of the course curriculum.

  • Do you provide any practice tests as part of this course?

    Yes, we provide 1 practice test as part of our course to help you prepare for the actual certification exam. You can try this Spark and Scala Exam Questions - Free Practice Test to understand the type of tests that are part of the course curriculum.

Reviews

Peter Dao
Peter Dao Senior Technical Analyst at Sutter Health, Sacramento

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

Amit Pradhan
Amit Pradhan Assistant Manager at HT Media, Bangalore

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, Bangalore

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., Prague

Great course! I really recommend it!

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

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, Perth

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, Bangalore

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, Bangalore

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, Bangalore

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.

Course Advisor

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

Named by Onalytica as one of the three most influential people in Big Data, Ronald is also an author of 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.

    Our San Francisco Correspondence / Mailing address

    Simplilearn Americas, Inc, 201 Spear Street, Suite 1100, San Francisco, CA 94105, United States of America, Call us at: +1-844-532-7688

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