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Apache Spark and Scala Certification Training

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Apache Spark and Scala Certification Training

In this era of Artificial intelligence, machine learning, and data science, algorithms that run on Distributed Iterative computation make the task of distributing and computing huge volumes of data easy. Spark is a lightning fast, in-memory, cluster computing framework that can be used for a variety of purposes. This JVM based open source framework can be used for processing and analyzing huge volumes of data and at the same time can be used to distribute data over a cluster of machines. It is designed in such a way that it can perform batch and stream processing and hence is known as a cluster computing platform. Scala is the language in which Spark is developed. Scala is a powerful and dynamic programming language that doesn’t compromise on type safety.

Why Should You Choose This Certification?

  • Understand Big Data, its components and the frameworks, Hadoop Cluster architecture and its modes.
  • Understand Scala programming, its implementation, basic constructs required for Apache Spark.
  • Gain an understanding of the concepts of Apache Spark and learn how to develop Spark applications.
  • Master the concepts of the Apache Spark framework and its associated deployment methodologies.
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Apache Spark and Scala - Instructor Led Training

15 th  October
Sat&Sun (4 Weeks) Weekends Batches
Timings: 07:00 AM - 11:30AM(IST)
Sold Out
25 th  October
Sat&Sun (4 Weeks) Weekends Batches
Timings: 07:00 AM - 11:30AM(IST)
Filling Fast
1 st  November
Sat&Sun (4 Weeks) Weekends Batches
Timings: 07:00 AM - 11:30AM(IST)


  • Introduction
  • Evolution of Distributed Systems
  • Need of New Generation Distributed Systems
  • Limitations of MapReduce in Hadoop
  • Batch vs. Real-Time Processing
  • PairRDD Methods-Others
  • Application of In-Memory Processing
  • Introduction to Apache Spark
  • Components of a Spark Project
  • History of Spark
  • Language Flexibility in Spark
  • Spark Execution Architecture
  • Automatic Parallelization of Complex Flows
  • Automatic Parallelization of Complex Flows-Important Points
  • APIs That Match User Goals
  • Installing Spark as a Standalone Cluster-Configurations
  • Demo-Install Apache Spark
  • Tasks of Spark on a Cluster
  • Companies Using Spark-Use Cases
  • Hadoop Ecosystem vs. Apache Spark
  • Introduction to Scala
  • Features of Scala
  • Basic Data Types
  • Basic Literals
  • Introduction to Operators
  • Types of Operators
  • Use Basic Literals and the Arithmetic Operator
  • Demo Use Basic Literals and the Arithmetic Operator
  • Use the Logical Operator
  • Demo Use the Logical Operator
  • Introduction to Type Inference
  • Type Inference for Recursive Methods
  • Mutable Collection vs. Immutable Collection
  • Functions
  • Objects
  • Classes
  • Traits as Interfaces
  • Collections
  • Types of Collections
  • Perform Operations on Lists
  • Demo Use Data Structures
  • Maps-Operations
  • Pattern Matching
  • Use Data Structures
  • RDDs API
  • Features of RDDs
  • Creating RDDs
  • Creating RDDs-Referencing an External Dataset
  • Referencing an External Dataset-Text Files
  • Referencing an External Dataset-Sequence Files
  • Creating RDDs-Important Points
  • RDD Operations
  • RDD Operations-Transformations
  • Invoking the Spark Shell
  • Importing Spark Classes
  • Demo-Build a Scala Project
  • Build a Scala Project
  • Build a Spark Java Project
  • Shared Variables-Broadcast
  • Shared Variables-Accumulators
  • Writing a Scala Application
  • Demo-Run a Scala Application
  • Scala RDD Extensions
  • DoubleRDD Methods
  • PairRDD Methods-Join
  • Method for Combining JavaPairRDD Functions
  • Importance of Spark SQL
  • Benefits of Spark SQL
  • DataFrames
  • SQLContext
  • Creating a DataFrame
  • Using DataFrame Operations
  • Demo-Run SparkSQL with a Dataframe
  • Run SparkSQL with a Dataframe
  • Interoperating with RDDs
  • Using the Reflection-Based Approach
  • Using the Programmatic Approach
  • Demo-Run Spark SQL Programmatically
  • Data Sources
  • Save Modes
  • Saving to Persistent Tables
  • Parquet Files
  • Partition Discovery
  • JSON Data
  • Hive Table
  • DML Operation-Hive Queries
  • JDBC to Other Databases
  • Supported Hive Data Types
  • Case Classes
  • Introduction to Spark Streaming
  • Working of Spark Streaming
  • Features of Spark Streaming
  • Micro Batch
  • DStreams
  • Input DStreams and Receivers
  • Basic Sources
  • Advanced Sources
  • Transformations on DStreams
  • Output Operations on DStreams
  • Design Patterns for Using ForeachRDD
  • DataFrame and SQL Operations
  • Checkpointing
  • Enabling Checkpointing
  • Socket Stream
  • File Stream
  • Window Operations
  • Types of Window Operations
  • Join Operations-Stream-Dataset Joins
  • Join Operations-Stream-Stream Joins
  • Monitoring Spark Streaming Application
  • Performance Tuning-High Level
  • Demo-Capture and Process the Netcat Data
  • Capture and Process the Netcat Data
  • Capture and Process the Flume Data
  • Introduction to Machine Learning
  • Common Terminologies in Machine Learning
  • Applications of Machine Learning
  • Machine Learning in Spark
  • Spark ML API
  • DataFrames
  • Transformers and Estimators
  • Pipeline
  • Working of a Pipeline
  • DAG Pipelines
  • Runtime Checking
  • Parameter Passing
  • General Machine Learning Pipeline-Example
  • Model Selection via Cross-Validation
  • Supported Types, Algorithms, and Utilities
  • Data Types
  • Feature Extraction and Basic Statistics
  • Clustering
  • K-Means
  • Perform Clustering Using K-Means
  • Gaussian Mixture
  • Power Iteration Clustering (PIC)
  • Latent Dirichlet Allocation (LDA)
  • Collaborative Filtering
  • Classification
  • Regression
  • Perform Classification Using Linear Regression
  • Perform Recommendation Using Collaborative Filtering
  • Introduction to Graph-Parallel System
  • Limitations of Graph-Parallel System
  • Introduction to GraphX
  • Importing GraphX
  • The Property Graph
  • Features of the Property Graph
  • Creating a Graph
  • Demo-Create a Graph Using GraphX
  • Create a Graph Using GraphX
  • Triplet View
  • List of Operators
  • Property Operators
  • Structural Operators
  • Subgraphs
  • Join Operators
  • Demo-Perform Graph Operations Using GraphX
  • Perform Graph Operations Using GraphX
  • Demo-Perform Subgraph Operations
  • Perform Subgraph Operations
  • Neighborhood Aggregation
  • mapReduceTriplets
  • Demo-Perform MapReduce Operations
  • Counting Degree of Vertex
  • Collecting Neighbors
  • Caching and Uncaching
  • Graph Builders
  • Vertex and Edge RDDs
  • Graph System Optimizations

About Apache Spark and Scala Training

  • 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
  • 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
  • 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

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.

Frequently Asked Question's

  • Spark can be integrated well with Hadoop and that’s a great advantage for those who are familiar with the latter.
  • According to technology forecasts, Spark is the future of worldwide Big Data Processing. The standards of Big Data Analytics are rising immensely with Spark, driven by high-speed data processing and real time results.
  • The number of companies that are using Spark or are planning the same has exploded over the last year. There is a massive surge in the popularity of Spark, the reason being its matured open-source components and an expanding community of users.
  • There is a huge demand for Spark Professionals and the demand for spark professionals is increasing.
  • 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

Your system must fulfill the following requirements:-

  • 64-bit Operating System
  • 8GB RAM

Please send us an email to, and we will answer any queries you may have!