Spark and Scala

About Spark and Scala Training Course

Intellipaat is a prominent e-learning institute in Hyderabad widely known for its most sought-after Apache Spark and Scala Training Course. As part of this Spark training classes the learners will be getting complete knowledge about these two advanced technologies and their concepts like RDD, Spark SQL, fundamentals of Scala programming and much more.

Scala Course Content Introduction of Scala

Introducing Scala and deployment of Scala for Big Data applications and Apache Spark analytics

Pattern Matching

The importance of Scala, the concept of REPL (Read Evaluate Print Loop), deep dive into Scala pattern matching, type interface, higher order function, currying, traits, application space and Scala for data analysis.

Executing the Scala code

Learning about the Scala Interpreter, static object timer in Scala, testing String equality in Scala, Implicit classes in Scala, the concept of currying in Scala, various classes in Scala, the concept of currying in Scala, various classes in Scala. of multiple traits extending.

Scala collections

Introduction to Scala collections, classification of collections, the difference between Iterator, and Iterable in Scala, example of list sequence in Scala.

Mutable collections vs. Immutable collections

The two types of collections in Scala, Mutable and Immutable collections, understanding lists and arrays in Scala, the list buffer and array buffer, Queue in Scala, double-ended queue Deque, Stacks, Sets, Maps, Tuples in Scala.

Use Case bobsrockets package

Introduction to Scala packages and imports, the selective imports, the Scala test classes, introduction to JUnit test class, JUnit interface via JUnit 3 suite for Scala test, packaging of Scala applications in Directory Structure, example of Spark Split and Spark Scala.

Spark Course Content

Introduction to Spark

Introduction to Spark, how Spark overcomes the drawbacks of working MapReduce, understanding in-memory MapReduce,interactive operations on MapReduce, Spark stack, fine vs. coarse grained update, Spark stack,SparkHadoop YARN, HDFS Revision, YARN Revision, the overview of Spark and how it is better Hadoop, deploying Spark without Hadoop,Spark history server, Cloudera distribution.

Spark Basics

Spark installation guide,Spark configuration, memory management, executor memory vs. driver memory, working with Spark Shell, the concept of Resilient Distributed Datasets (RDD), learning to do functional programming in Spark, the architecture of Spark.

Working with RDDs in Spark

Spark RDD, creating RDDs, RDD partitioning, operations & transformation in RDD,Deep dive into Spark RDDs, the RDD general operations, a read-only partitioned collection of records, using the concept of RDD for faster and efficient data processing,RDD action for Collect, Count, Collectsmap, Saveastextfiles, pair RDD functions.

Aggregating Data with Pair RDDs

Understanding the concept of Key-Value pair in RDDs, learning how Spark makes MapReduce operations faster, various operations of RDD,MapReduce interactive operations, fine & coarse grained update, Spark stack.

Writing and Deploying Spark Applications

Comparing the Spark applications with Spark Shell, creating a Spark application using Scala or Java, deploying a Spark application,Scala built application,creation of mutable list, set & set operations, list, tuple, concatenating list, creating application using SBT,deploying application using Maven,the web user interface of Spark application, a real world example of Spark and configuring of Spark.

Parallel Processing

Learning about Spark parallel processing, deploying on a cluster, introduction to Spark partitions, file-based partitioning of RDDs, understanding of HDFS and data locality, mastering the technique of parallel operations,comparing repartition & coalesce, RDD actions.

Spark RDD Persistence

The execution flow in Spark, Understanding the RDD persistence overview,Spark execution flow & Spark terminology, distribution shared memory vs. RDD, RDD limitations, Spark shell arguments,distributed persistence, RDD lineage,Key/Value pair for sorting implicit conversion like CountByKey, ReduceByKey, SortByKey, AggregataeByKey.

Spark Streaming &Mlib

Spark Streaming Architecture, Writing streaming program coding, processing of spark stream,processing Spark Discretized Stream (DStream), the context of Spark Streaming, streaming transformation, Flume Spark streaming, request count and Dstream, multi batch operation, sliding window operations and advanced data sources. Different Algorithms, the concept of iterative algorithm in Spark, analyzing with Spark graph processing, introduction to K-Means and machine learning, various variables in Spark like shared variables, broadcast variables, learning about accumulators.

Improving Spark Performance

Introduction to various variables in Spark like shared variables, broadcast variables, learning about accumulators, the common performance issues and troubleshooting the performance problems.

Spark SQL and Data Frames

Learning about Spark SQL, the context of SQL in Spark for providing structured data processing, JSON support in Spark SQL, working with XML data, parquet files, creating HiveContext, writing Data Frame to Hive, reading JDBC files, understanding the Data Frames in Spark, creating Data Frames, manual inferring of schema, working with CSV files, reading JDBC tables, Data Frame to JDBC, user defined functions in Spark SQL, shared variable and accumulators, learning to query and transform data in Data Frames, how Data Frame provides the benefit of both Spark RDD and Spark SQL, deploying Hive on Spark as the execution engine.

Scheduling/ Partitioning

Learning about the scheduling and partitioning in Spark,hash partition, range partition, scheduling within and around applications, static partitioning, dynamic sharing, fair scheduling,Map partition with index, the Zip, GroupByKey, Spark master high availability, standby Masters with Zookeeper, Single Node Recovery With Local File System, High Order Functions.

Apache Spark – Scala Project

Project 1: Movie Recommendation

will be introduced to the Spark Machine Learning Library, a guide to MLlib algorithms and coding which is a machine learning library. Understand how to deploy collaborative filtering, clustering, regression, and dimensionality reduction in MLlib. Upon completion of the project you will gain experience in working with streaming data, sampling, testing and statistics.

Project 2: Twitter API Integration for tweet Analysis

Topics – With this project you will learn to integrate Twitter API for analyzing tweets. You will write codes on the server side using any of the scripting languages like PHP, Ruby or Python, for requesting the Twitter API and get the results in JSON format. You will then read the results and perform various operations like aggregation, filtering and parsing as per the need to come up with tweet analysis.

Project 3: Data Exploration Using Spark SQL – Wikipedia data set

Topics – This project lets you work with Spark SQL. You will gain experience in working with Spark SQL for combining it with ETL applications, real time analysis of data, performing batch analysis, deploying machine learning, creating visualizations and processing of graphs.