Spark and scalla Training in Pune

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.
Do you know the secret behind Uber’s flawless map functioning? Here’s a hint, the images gathered by the Map Data Collection Team are accessed by the downstream Apache Spark team and are assessed by operators responsible for map edits. A number of file formats are supported by Apache Spark which allows multiple records to be stored in a single file.

1. Big Data Introduction
Understand Big Data, its components and the frameworks, Hadoop Cluster architecture and its modes.
2. Introduction on Scala
Understand Scala programming, its implementation, basic constructs required for Apache Spark.
3. Spark Introduction
Gain an understanding of the concepts of Apache Spark and learn how to develop Spark applications.
4. Spark Framework & Methodologies
Master the concepts of the Apache Spark framework and its associated deployment methodologies.
5. Spark Data Structure
Learn Spark Internals RDD and use of Spark’s API and Scala functions to create and transform RDDs.
6. Spark Ecosystem
Master the RDD and various Combiners, SparkSQL, Spark Context, Spark Streaming, MLlib, and GraphX.

WHO SHOULD ATTEND THE APACHE SPARK COURSE?
• Data Scientists
• Data Engineers
• Data Analysts
• BI Professionals
• Research professionals
• Software Architects
• Software Developers
• Testing Professionals
• Anyone who is looking to upgrade Big Data skills
Syllabus

1Introduction to Big Data Hadoop and Spark
Learning Objectives: Understand Big Data and its components such as HDFS. You will learn about the Hadoop Cluster Architecture. You will also get an introduction to Spark and the difference between batch processing and real-time processing.

Topics:
• What is Big Data?
• Big Data Customer Scenarios
• What is Hadoop?
• Hadoop’s Key Characteristics
• Hadoop Ecosystem and HDFS
• Hadoop Core Components
• Rack Awareness and Block Replication
• YARN and its Advantage
• Hadoop Cluster and its Architecture
• Hadoop: Different Cluster Modes
• Big Data Analytics with Batch & Real-time Processing
• Why Spark is needed?
• What is Spark?
• How Spark differs from other frameworks?
2Introduction to Scala
Learning Objectives: Learn the basics of Scala that are required for programming Spark applications. Also learn about the basic constructs of Scala such as variable types, control structures, collections such as Array, ArrayBuffer, Map, Lists, and many more.

Topics:
• What is Scala?
• Why Scala for Spark?
• Scala in other Frameworks
• Introduction to Scala REPL
• Basic Scala Operations
• Variable Types in Scala
• Control Structures in Scala
• Foreach loop, Functions and Procedures
• Collections in Scala- Array
3 Object Oriented Scala and Functional Programming Concepts
Learning Objectives: Learn about object-oriented programming and functional programming techniques in Scala.

Topics
• Variables in Scala
• Methods, classes, and objects in Scala
• Packages and package objects
• Traits and trait linearization
• Java Interoperability
• Introduction to functional programming
• Functional Scala for the data scientists
• Why functional programming and Scala are important for learning Spark?
• Pure functions and higher-order functions
• Using higher-order functions
• Error handling in functional Scala
• Functional programming and data mutability
4 Collection APIs
Learning Objectives: Learn about the Scala collection APIs, types and hierarchies. Also, learn about performance characteristics.

Topics
• Scala collection APIs
• Types and hierarchies
• Performance characteristics
• Java interoperability
• Using Scala implicits
5 Introduction to Spark
Learning Objectives: Understand Apache Spark and learn how to develop Spark applications.

Topics:
• Introduction to data analytics
• Introduction to big data
• Distributed computing using Apache Hadoop
• Introducing Apache Spark
• Apache Spark installation
• Spark Applications
• The back bone of Spark – RDD
• Loading Data
• What is Lambda
• Using the Spark shell
• Actions and Transformations
• Associative Property
• Implant on Data
• Persistence
• Caching
• Loading and Saving data
6 Operations of RDD
Learning Objectives: Get an insight of Spark – RDDs and other RDD related manipulations for implementing business logic (Transformations, Actions, and Functions performed on RDD).

Topics
• Challenges in Existing Computing Methods
• Probable Solution & How RDD Solves the Problem
• What is RDD, Its Operations, Transformations & Actions
• Data Loading and Saving Through RDDs
• Key-Value Pair RDDs
• Other Pair RDDs, Two Pair RDDs
• RDD Lineage
• RDD Persistence
• WordCount Program Using RDD Concepts
• RDD Partitioning & How It Helps Achieve Parallelization
• Passing Functions to Spark
7 DataFrames and Spark SQL
Learning Objectives: Learn about SparkSQL which is used to process structured data with SQL queries, data-frames and datasets in Spark SQL along with different kinds of SQL operations performed on the data-frames. Also, learn about the Spark and Hive integration.

Topics
• Need for Spark SQL
• What is Spark SQL?
• Spark SQL Architecture
• SQL Context in Spark SQL
• User Defined Functions
• Data Frames & Datasets
• Interoperating with RDDs
• JSON and Parquet File Formats
• Loading Data through Different Sources
• Spark – Hive Integration
8 Machine learning using MLlib
Learning Objectives: Learn why machine learning is needed, different Machine Learning techniques/algorithms, and SparK MLlib.

Topics
• Why Machine Learning?
• What is Machine Learning?
• Where Machine Learning is Used?
• Different Types of Machine Learning Techniques
• Introduction to MLlib
• Features of MLlib and MLlib Tools
• Various ML algorithms supported by MLlib
• Optimization Techniques
9 Using Spark MLlib
Learning Objectives: Implement various algorithms supported by MLlib such as Linear Regression, Decision Tree, Random Forest and so on

Topics
• Supervised Learning – Linear Regression, Logistic Regression, Decision Tree, Random Forest
• Unsupervised Learning – K-Means Clustering
10 Streaming with Kafka and Flume
Learning Objectives: Understand Kafka and its Architecture. Also, learn about Kafka Cluster, how to configure different types of Kafka Clusters. Get introduced to Apache Flume, its architecture and how it is integrated with Apache Kafka for event processing. At the end, learn how to ingest streaming data using flume.

Topics
• Need for Kafka
• What is Kafka?
• Core Concepts of Kafka
• Kafka Architecture
• Where is Kafka Used?
• Understanding the Components of Kafka Cluster
• Configuring Kafka Cluster
• Kafka Producer and Consumer Java API
• Need of Apache Flume
• What is Apache Flume?
• Basic Flume Architecture
• Flume Sources
• Flume Sinks
• Flume Channels
• Flume Configuration
• Integrating Apache Flume and Apache Kafka
11 Apache Spark Streaming
Learning Objectives: Learn about the different streaming data sources such as Kafka and Flume. Also, learn to create a Spark streaming application.

Topics
• Apache Spark Streaming: Data Sources
• Streaming Data Source Overview
• Apache Flume and Apache Kafka Data Sources
12 Spark GraphX Programming
Learning Objectives: Learn the key concepts of Spark GraphX programming and operations along with different GraphX algorithms and their implementations.

Topics
• A brief introduction to graph theory
• GraphX
• VertexRDD and EdgeRDD
• Graph operators
• Pregel API
• PageRank