Scala and Spark Training – What is Scala?
Scala and spark Training – Scala is a modern multi-paradigm programming language designed to express common programming patterns in a concise, elegant, and type-safe way. Scala, the word came from “Scalable Language”, is a hybrid functional programming language which smoothly integrates the features of objected oriented and functional programming languages and it is compiled to run on the Java Virtual Machine. Scala has been created by Martin Odersky and released in 2003.
There are the following reasons that encourages Scala learning.
Many existing companies, who depend on Java for business critical applications, are turning to Scala to boost their development productivity, applications scalability and overall reliability.
Scala is a type-safe JVM language that incorporates both object oriented and functional programming features into an extremely concise, logical, simple and extremely powerful language.
Scala creates a “better Java” alternative by remaining its syntax very close to the Java language syntax, so that to minimize the learning difficulty.
Scala was created specifically with the goal of creating a better language, in contrast with those restrictive, overly tedious, or frustrating features of Java.
Scala is a much cleaner and well organized language that is ultimately easier to use and increases productivity.
What is Spark?
Spark is a fast cluster computing technology, designed for fast computation in Hadoop clusters. It is based on Hadoop MapReduce programming and it extends the MapReduce model to efficiently use it for more types of computations, like interactive queries and stream processing. Spark uses Hadoop in two different ways – one is storage and another one is processing. As Spark is having its own cluster management computation, it uses Hadoop for storage purpose only.
Spark is one of Hadoop’s sub project developed in 2009 in UC Berkeley’s AMPLab by Matei Zaharia. It was Open Sourced in 2010 under a BSD license. It was donated to Apache software foundation in 2013, and now Apache Spark has become a top level Apache project from Feb-2014.
Spark was introduced by Apache Software Foundation for speeding up the Hadoop software computing process.
The main feature of Spark is its in-memory cluster computing that highly increases the speed of an application processing.
Spark is designed to cover a wide range of workloads such as batch applications, iterative algorithms, interactive queries and streaming applications by reducing the management burden of maintaining separate tools.
Apache Spark also have the following features.
- Speed− Spark helps to run an application in Hadoop cluster, up to 100 times faster in memory and 10 times faster when running on disk by reducing number of read/write operations to disk and by storing the intermediate processing data in memory.
- Supports multiple languages− Spark comes up with 80 high-level operators for interactive querying and provides application development with built-in APIs in different languages in Java, Scala, or Python.
- Advanced Analytics− Spark not only supports ‘Map’ and ‘reduce’ programming but it also supports SQL queries, Streaming data, Machine learning (ML), and Graph algorithms.
The following topics will be covered in our Scala and Spark Training:
Scala and Spark Training – Introduction to Scala
Scala and spark Training – Overview of Scala
IDE for Scala
Variables & Methods
Ways of Executing a Scala Program
Expressions and Loops
Usage of ‘yield’ keyword in For Expression
Exception handling with Try Expression
Functions in Scala
First class Function
Higher Order Methods
Partially Applied Function
Call-by-name Vs call-by-value
Repeated Parameter passing mechanism
Named Parameter mechanism
Default parameter mechanism
OOPs in Scala
Classes & Objects
Defining a Constructor
Constructor Parameter Vs Class Parameter
Uniform Access Principle
Extending a Class
Namespace in Scala
Calling a superclass Constructor
Dynamic Binding in Scala
Final Member in Scala Class
Scala Class Hierarchy
Object Equality in Scala
Factory Design Pattern in Scala
Introduction to Traits
Inheritance in Traits
Mixing a Trait
Trait Vs Class
Example of Ordered Trait
Stackable Modification behaviour of Trait
Example of Stackable Modification
Rules of mixing of multiple traits
Scala Programming Packaging
Different form of Scala Package
Different form of Import
Case Class & Pattern Matching
Introduction to Case Class
Introduction to Pattern Matching
Example of Pattern Matching
Option Data Type
Usage of Option Data Type
Case Class and Partial Function
Usage of Pattern in For Expression
Immutable and Mutable collection
Constructing object of Array, Set, List, Tuple,Map
Detailed Discussion of various methods in List class and List Object
Basic Operations like head, tail, isEmpty on List
Example of using List Pattern
Categories of methods in List
First Order Methods in List
Higher Order Methods in List
Map vs flatMap
Filtering a List
Example of takeWhile, dropWhile, span, partition
Predicates over List
Folding Over List
FoldLeft Vs FoldRight
Scala and Spark Training – Introduction to Spark
Introduction to Big Data
Big Data Problem
Scale-Up Vs Scale-Out Architecture
Characteristics of Scale-Out
Introduction to Hadoop, Map-Reduce and HDFS
Hortonworks Data Platform (HDP) using Virtual box
Importing HDP VM image using Virtual box on local machine
Overview of Ambari and its components
Overview of services configuration using Ambari
Overview of Apache Zeppelin
Creating, importing and executing notebooks in Apache Zeppelin
IDEs for Spark Applications
SBT and its overview
Resolving dependencies for Spark applications
Overview of Spark architecture
Storage layers for Spark
Initialize a Spark Context and building applications
Submitting a Spark Application
Use of Spark History Server
Spark Driver Process
Spark Conf and Spark Context
Overview of spark-submit command
Overview of RDD
RDD and Partitions
Ways of Creating RDD
RDD transformations and Actions
RDD Lineage Graph (DAG)
Element wise transformations
Map Vs FlatMap Transformation
Overview of RDD persistence
Methods for persisting RDD
Persisting RDD with Storage option
Illustration of Caching on an RDD in DAG
Removal of Cached RDD
Overview of Key-Value Pair RDD
Ways of creating Pair RDDs
Transformations on Pair RDD
ReduceByKey(), FoldByKey(),MapValues(), FlatMapValues(),keys() and Values() Transformation
Grouping, Joining, Sorting on Pair RDD
ReduceByKey() Vs GroupByKey()
Pair RDD Action
Launching Spark on cluster
Configure and launch Spark Cluster on Google Cloud
Configure and launch Spark Cluster on Microsoft Azure
Logging and Debugging a Spark Application
Setting up a window environment for executing Spark Application using IDE
Steps of using slf4j logging mechanism in Spark Application
Attaching a debugger to Spark Application
Example of debugging a Spark application running inside a cluster
Spark Application Architecture
Spark Application Distributed Architecture
Spark Application submission Mode
Overview of Cluster Manager
Example of using Standalone Cluster Manager
Driver and its responsibilities
Overview of Job, Stage and Tasks
Spark Job Hierarchy
Spark-submit command and various submission options
Yarn Cluster Manager
Client and Cluster Deploy-mode
Advance concepts in Spark
Determining RDD partitioner
Introduction to SparkSQL
Creating SparkSession with Hive Support
Ways of Creating DataFrame
Registering a DataFrame as View
DataFrame Transformations API
DataFrame SQL statement
Limitation of DataFrame
Introduction to Dataset
Introduction to Encoder
Functional transformation on Dataset
Loading CSV, JSON, Parquet format file in SparkSQL
Loading and saving data from/in Hive, JDBC, HDFS, Cassandra
Introduction to User-Defined-Function (UDF)
Customizing a UDF
Usage of UDF in DataFrame Transformations API
Usage of UDF in Spark SQL statement
Introduction to Window Function
Steps of defining a window function
Illustration of Window function usage
Introduction to UDAF
Customizing a UDAF
Illustration of customized UDAF usage
Introduction to data streaming
Spark Streaming framework
Spark Streaming and Micro batch
Introduction of DStreams
DStreams and RDD
Word Count example using Socket Text Stream
Streaming with Twitter feeds
Setting up a Twitter App
Resolving Twitter dependency in Spark Streaming Application
Steps of creating Uber Jar
Example of extracting hashtags from tweet data
Troubleshooting Twitter Streaming issue in Spark Application
Steps of creating Spark Streaming Application
Architecture of Spark Streaming
Twitter Streaming examples using stateless transformation
Introduction to stateful Transformations
Window Duration and Slide Duration
Naive and inverse window reduce operation
Tracking State of an event using updateStateByKey operation
Interact directly with RDD using transform () operation
Example of HDFS file streaming
Example of Spark-Kafka interaction
Saving DStreams to external file system
Duration & Timings : USA
Duration – 30 Hours.
Training Type: Online Live Interactive Session.
Weekend Session – Sat – Sun 9:30 AM – 12:30 PM EST– 5 Weeks. March 30, 2019.
Weekend Session – Sat – Sun 9:30 AM – 12:30 PM EST– 5 Weeks. April 27, 2019..
Duration & Timings : INDIA
Duration – 30 Hours.
Training Type: Online Live Interactive Session.
Weekend Session – Sat – Sun 7:00 PM to 10:00 PM (IST) – 5 Weeks. March 30, 2019.
Weekend Session – Sat – Sun 7:00 PM to 10:00 PM (IST) – 5 Weeks. April 27, 2019..