Data Analytics With Spark & Scala

Spark Big data

Expertise as a Data Scientist

Propel your Big Data analysis expertise to the next level with hands-on practice on Apache Spark and get insights with much faster and real-time analysis.

Here, you will find the most comprehensive course designed in order to elevate you as a professional Apache Spark Developer, Big Data Analyst or as a Data Scientist.

8 Modules with live data set

Our extensively framed training curriculum enables the participants to have a thorough understanding of what Big Data Analytics is, which furthers to discuss the core concepts of Stream Processing and In-memory Analytics using the Resilient Distributed Datasets in Spark. The training curriculum also provides the participants to have a hands-on experience with Scala and the PySpark Shell, Spark Streaming, Spark SQL, Graph Queries using GraphX and Machine Learning using MLLib.

To enhance the competency levels of participants, this course also includes quizzes, assignments and allows them to have a hands-on practice with live datasets like the GE Flights data set, data from twitter etc.

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Course Details

After the successful completion of this course, you will gain expertise in the following concepts:

  1. Determining the limitations of Hadoop and the role of Spark in overcoming these limitations
  2. Understanding the fundamentals of Scala programming language
  3. Applying Control Structures, Loops, Collection etc
  4. Learning Object Oriented and Functional programming in Scala
  5. Understanding the role of RDDs in Spark
  6. Implementing the Spark applications on YARN (Hadoop)
  7. Using the Spark SQL queries to perform several computations
  8. Streaming the data using Spark Streaming API
  9. Implementing the machine learning algorithms in Spark using MLLib API
  10. Understanding the GraphX API and implement graph algorithms
  11. Extending the capabilities of Spark using PySpark and SparkR

This course is extensively useful for the aspirants who have the passion to emerge as a professional Apache Spark Developer and also those who currently are Big Data Analytics professionals, Research professionals, IT Developers and Testers, Data Scientists, BI and Reporting professionals, Hadoop Developers and a lot more.

Instructor Led training 24 Hrs
Instructor Interaction Yes
Live Support Post Training 1 Year
Simulated Projects 2
Access to Virtual Machine Life Time
Access to Kovid Cluster Life Time
Capstone/Hands On/Real Time Project 1
Kovid Academy Big Data Administrator Certificate Yes
21 CEU/PDU certificate Yes

Module 1: Big Data and Spark

  • Overview of Big Data and Spark
  • MapReduce limitations
  • Spark History
  • Spark Architecture
  • Spark and Hadoop Advantages
  • Benefits of Spark + Hadoop
  • Introduction to Spark Eco-system
  • Practice Environment Set Up

Module 2: Scala Programming

  • Scala foundation
  • Features of Scala
  • Setup Spark and Scala on Ubuntu and Windows OS
  • Install IDE’s for Scala
  • Run Scala Codes on Scala Shell
  • Understanding Data types in Scala
  • Implementing Lazy Values
  • Control Structures
  • Looping Structures
  • Functions
  • Procedures
  • Collections
  • Arrays and Array Buffers
  • Maps, Tuples and Lists

Module 3: Object Oriented Programming with Scala

  • Implementing Classes
  • Implementing Getter & Setter
  • Object & Object Private Fields
  • Implementing Nested Classes
  • Using Auxiliary Constructor
  • Primary Constructor
  • Companion Object
  • Apply Method
  • Understanding Packages
  • Override Methods
  • Type Checking
  • Casting
  • Abstract Classes

Module 4: Functional Programming with Scala

  • Understanding Functional programming in Scala
  • Implementing Traits
  • Layered Traits
  • Rich Traits
  • Anonymous Functions
  • Higher Order Functions
  • Closures and Currying
  • Performing File Processing

Module 5: Spark Foundations

  • Spark Shell and PySpark
  • Basic operations on Shell
  • Spark Java projects
  • Spark Context and Spark Properties
  • Persistence in Spark
  • HDFS data from Spark
  • Implementing Server Log Analysis using Spark

Module 6: Resilient Distributed Datasets (RDD)

  • Understanding RDD
  • Loading data into RDD
  • Scala RDD, Paired RDD, Double RDD & General RDD Functions
  • Implementing HadoopRDD, Filtered RDD, Joined RDD
  • Transformations, Actions and Shared Variables
  • Spark Operations on YARN
  • Sequence File Processing
  • Partitioner and its role in Performance improvement

Module 7: Spark Streaming & Spark SQL

  • Introduction to Spark Streaming
  • Introduction to Spark SQL
  • Querying Files as Tables
  • Text file Format
  • JSON file Format
  • Parquet file Format
  • Hive and Spark SQL Architecture
  • Integrating Spark & Apache Hive
  • Spark SQL performance optimization
  • Implementing Data visualization in Spark

Module 8: GraphX & MLLib

  • Graph theory foundations
  • Property graphs
  • Graph traversal queries
  • MLLib data formats
  • Data Frames
  • MLLib library functions
  • Putting all the pieces together
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Why learn Spark?

In the recent times, Spark has gained much traction as the processing module of choice for most of the Big Data users. This is mainly because of its obvious performance and interoperability with the multiple APIs.

Who is the right candidate for the course on Spark?

This course is extensively useful for the aspirants who have the passion to emerge as a professional Apache Spark Developer and also those who have the designations including (but not limited to) – Big Data Analytics professionals, Research professionals, IT Developers and Testers, Data Scientists, BI and Reporting professionals, Hadoop Developers.

What are the prerequisites for learning Spark?

1)Learning Apache Spark is ideal for the participants who already have some exposure to the Big Data and Distributed Computing concepts.
2)Basic familiarity with Linux is required, as most of the deployments are carried on Linux environments.
3)Knowledge of scripting and programming languages are required in order to quickly grasp the core concepts of Spark.
4)For a beginner, it is recommended to start with the Big Data Developer course and later continue with the Apache Spark.

How will this certification help in building a sound Big Data Career?

This certification course will assist the participants to excel their current skills on Hadoop or Data Science to the next levels, which will further characterise them as a specialist in Spark and Scala. After successfully completing the course from Kovid Academy, we recommend the participants to enhance their Big Data skills in the field of Data Science using R or Python or Data Engineering by learning Kafka and Storm or NoSQL by learning Cassandra, MongoDB, Elastic or others (based on their career plan).

What projects will be involved with the course?

At Kovid Academy, we always prefer to train our participants with the real life datasets. Accordingly, the participants will be having a hands-on practice on the different projects dealing with – 10 years flight data, data from twitter, movie ratings data, data from medicare and other similar datasets.

What are the system requirements for participants?

It is recommended that the participants should have a i3 or higher range processor with virtualization support minimum of 4 GB RAM (8 GB RAM is recommended), 64-bit operating system and about 100 GB of free hard disk space.

What are the training materials provided?

For all the training modules that are covered in this course, adequate materials and good references will be provided to the participants. In the case of online interactive trainings, every session will be recorded and uploaded in the LMS, giving the participants a feasibility to recap their completed training sessions.

Is Certification offered and if so, how do you earn?

After the completion of training, the participants will undergo a certification examination. Based on their performance in the assignments, projects and the final examination, certificates will be issued to the participants.

How many hours, a student is expected to work?

This extensively depends upon the prior experience levels and the grasping nature of the participants. It means, the time period may vary from one participant to another. On an average, we have noticed that the participants are required to spend double the training hours. Let us consider, if the training is for 10 hours, then the participants are required to spend an additional of 20 hours more.