Apache Spark and Scala Certification Training

6.2K Learners

Spark and Scala Certification Course Overview

This Spark certification training helps you master the essential skills of the Apache Spark open-source framework and Scala programming language, including Spark Streaming, Spark SQL, machine learning programming, GraphX programming, and Shell Scripting Spark. You will also understand the role of Spark in overcoming the limitations of MapReduce.

Skills Covered

  • Scala programming language
  • Resilient Distributed Datasets RDD
  • Spark Streaming features
  • GraphX programming
  • Spark installation process
  • SparkSQL
  • Spark ML programming
  • Scala programming language
  • Spark installation process
  • Resilient Distributed Datasets RDD
  • SparkSQL
  • Spark Streaming features
  • Spark ML programming
  • GraphX programming
  • Scala programming language
  • Spark installation process
  • Resilient Distributed Datasets RDD
  • SparkSQL
  • Spark Streaming features
  • Spark ML programming
  • GraphX programming

Benefits

The global Apache Spark market revenue may grow to $4.2 billion by 2020, with a cumulative market value of $9.2 billion and a growth rate of 67 percent. Professionals with Apache Spark skills find job opportunities in sectors like retail, manufacturing, and healthcare.

  • Designation
  • Annual Salary
  • Hiring Companies
  • Big Data Architect
    Annual Salary
    $93KMin
    $124KAverage
    $165KMax
    Source: Glassdoor
    Hiring Companies
    Amazon
    Hewlett-Packard
    Wipro
    Cognizant
    Spotify
    Source: Indeed
  • Big Data Engineer
    Annual Salary
    $81KMin
    $117KAverage
    $160KMax
    Source: Glassdoor
    Hiring Companies
    Amazon
    Hewlett-Packard
    Facebook
    KPMG
    Verizon
    Source: Indeed
  • Big Data Developer
    Annual Salary
    $58KMin
    $88.5KAverage
    $128KMax
    Source: Glassdoor
    Hiring Companies
    Cisco
    Target Corp
    GE
    IBM
    Source: Indeed

Spark and Scala Course Curriculum

Eligibility

This Spark certification training course is ideal for 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, and students who want to gain a thorough understanding of Apache Spark.
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Pre-requisites

Those wishing to take the Apache Spark certification training course should have a fundamental knowledge of any programming language and a basic understanding of any database, SQL, and query language for databases. Working knowledge of Linux- or Unix-based systems is also beneficial.
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Course Content

  • Apache Spark & Scala

    Preview

    • Lesson 00 - Course Overview

      04:11Preview
      • 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

      25:17Preview
      • 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

      37:15Preview
      • 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

      50:27Preview
      • 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

      39:26Preview
      • 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

      34:48Preview
      • 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

      39:50Preview
      • 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

      46:16Preview
      • 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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    Banking

    Help a banking institution to perform the marketing analysis of the data generated by a marketing campaign to convince potential customers to invest in a bank term deposit scheme.

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Apache Scala and Spark Exam & Certification

Apache Spark and Scala Certificate

  • Who provides the certification?

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

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

    Online Classroom:
    • Attend one complete batch of Apache Scala and Spark certification training
    • 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

  • How long is the Apache Scala and Spark certificate from Simplilearn valid for?

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

  • What is the cost of CCA175 Spark and Hadoop Developer exam?

    The  CCA175 Spark and Hadoop Developer exam costs USD $295.

  • What is the passing score of the CCA175 exam?

    There are 8–12 performance-based (hands-on) tasks and the passing score is 70%.

  • I have passed the Spark and Hadoop Developer exam, how long will it take to receive my CCA175 certificate?

    If you have passed the exam, you will receive your digital certificate and license number within 5 business days of taking the exam.

  • 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.

  • How many attempts do I have to pass the CCA175 exam?

    You may take the exam as many times as you want until you pass, however, you must pay for each attempt; Retakes are not allowed after the successful completion of a test.

  • How do I become a Spark Developer?

    To become a Spark developer, one needs to be well versed in using Spark for cleaning, transforming, and analyzing huge amounts of raw data. With our Spark and Scala certification course, you would learn how to handle this responsibility satisfactorily.

Spark and Scala Course 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..

  • 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.

  • 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.

  • 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.

  • 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.

  • 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.

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Why Join this Program

  • Develop skills for real career growthCutting-edge curriculum designed in guidance with industry and academia to develop job-ready skills
  • Learn from experts active in their field, not out-of-touch trainersLeading practitioners who bring current best practices and case studies to sessions that fit into your work schedule.
  • Learn by working on real-world problemsCapstone projects involving real world data sets with virtual labs for hands-on learning
  • Structured guidance ensuring learning never stops24x7 Learning support from mentors and a community of like-minded peers to resolve any conceptual doubts

Spark and Scala Training FAQs

  • What is Apache Spark?

    Apache Spark is an open-source data processing framework that can easily perform processing tasks on very large data sets, and also distribute data processing tasks across multiple computers, either on its own or with other distributed computing tools.
     

  • What is Scala?

    Scala is a general purpose programming language which is compiler based and a multi-paradigm programming language. It is a combination of functional programming and object oriented programming languages.

  • Scala vs Spark

    Spark is a unified analytics engine that is used for processing Big Data. It is a cluster computing framework that developers use for processing tasks rapidly with extensive datasets. Scala is an object-oriented programming language in which Spark is written. With Scala, developers can dive into Spark’s source code to get access to the framework’s newest features.

  • Can I learn Apache Spark and Scala online?

    The rapid evolution of learning methodologies, thanks to the influx of technology, has increased the ease and efficiency of online learning, making it possible to learn at your own pace. Simplilearn's Apache Spark and Scala provide eLearning content and access to study materials from anywhere and at any time. Our extensive (and growing) collection of blogs, tutorials, and YouTube videos will help you get up to speed on the main concepts. Even after your class ends, we provide a 24/7 support system to help you with any questions or concerns you may have.

  • Will this Apache Spark course help me to crack the CCA175 Spark and Hadoop certification exam?

    Simplilearn’s Apache Spark certification course gives you a step by step guide for learning all the relevant concepts. It makes you industry-ready by covering all the topics that are included in the CCA175 certification exam.

  • I also have basic knowledge in Python, do you offer both Python and Spark courses together?

    You can take Simplearn’s PySpark certification training course to learn how to use Python in the Spark ecosystem.

  • What are the system requirements?

    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 Apache Spark real-time projects completed and how do I get certified?

    Everything you need to complete your Apache Spark real-time projects, 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 Apache Spark certification training, you will submit your finished project to the trainer for evaluation. Upon successful evaluation of the project and completion of the online simulation 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 Scala and Spark certified, with more than 15 years of experience in training and working professionally in the Big Data domain. 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 Apache Scala and Spark certification 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 Apache Scala and Spark course remotely from your desktop or laptop via video conferencing. This format improves productivity and decreases the 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 Apache Scala and Spark training classes after the session is conducted, so you can catch-up on training before the next session.
     

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

    Yes, you can cancel your enrollment. 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 Apache Scala and Spark certification training?

    You can enroll for this Apache Scala and Spark certification 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 Spark certification course. 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 Apache Spark and Scala 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.

  • Do you recommend any Apache Spark books to refer before taking this course?

    Before taking this Apache Spark certification training course, you can learn about Apache Spark by reading these books: Spark:

    • The Definitive Guide: Big Data Processing Made Simple By Bill Chambers
    • Apache Spark 2.x Machine Learning Cookbook By Siamak Amirghodsi
    • Spark Cookbook from Rishi Yadav

  • What is the recommended learning path after completing Apache Spark and Scala certification course?

    You can either enroll in our Big Data Engineer certification training or if you are looking to get the University certificate, you can enroll in the Professional Certificate Program in Data Engineering.

Related Programs

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

This course has been upgraded to

Professional Certificate Program in Data Engineering

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  • Program completion certificate from Purdue University Online and Simplilearn
  • Access to Purdue’s Alumni Association membership on program completion