There are no prerequisites for taking up this course. Basic knowledge of database, SQL and query language can help.
A Spark developer earns up to $239,000 annually, which is way higher than what other cities around the globe offer to the big data analysts. This clearly specifies that this city has lucrative career opportunities for aspirants who have sound knowledge in big data processing engines like Apache Spark and budding programming languages like Scala. Therefore anyone who wishes to kick-start his career as a big data professional in New York, now is the time. Intellipaat offers the industry-designed Spark certification training in New York City.
New York City has one of the strongest economic scenarios with strong technology sector standing at the forefront. The companies in this city are increasingly looking for faster big data technologies and strong programming language to process streaming data in real-time. This is possible through Apache Spark and Scala and hence aspirants who wish to become big data analysts should definitely enroll in this course.
With the influx of data it is imperative for the companies to deploy faster big data technologies like Apache Spark. Since Scala increases the throughput of Spark, an increasing number of companies are adopting these technologies together. Intellipaat’s Apache Spark and Scala Training Course lets you gain proficiency and expertise in these two technologies through lab sessions and real-time projects. This course also helps you prepare for Cloudera Spark and Hadoop Developer certification (CCA175) exam.
Introducing Scala and deployment of Scala for Big Data applications and Apache Spark analytics.
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.
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.
Learning about the Classes concept, understanding the constructor overloading, the various abstract classes, the hierarchy types in Scala, the concept of object equality, the val and var methods in Scala.
Understanding Sealed traits, wild, constructor, tuple, variable pattern, and constant pattern.
Understanding traits in Scala, the advantages of traits, linearization of traits, the Java equivalent and avoiding of boilerplate code.
Implementation of traits in Scala and Java, handling of multiple traits extending.
Introduction to Scala collections, classification of collections, the difference between Iterator, and Iterable in Scala, example of list sequence in Scala.
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.
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.
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,Spark Hadoop 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 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.
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.
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.
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.
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.
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 Architecture, Writing streaming programcoding, 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.
Introduction to various variables in Spark like shared variables, broadcast variables, learning about accumulators, the common performance issues and troubleshooting the performance problems.
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.
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.
Project 1: Movie Recommendation
Topics – This is a project wherein you will gain hands-on experience in deploying Apache Spark for movie recommendation. You 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.
Intellipaat is the pioneer of Hadoop training in India. So it pays to be with the market leader like Intellipaat to learn Spark and Scala and get the best jobs in top MNCs for top salaries. The Intellipaat training is the most comprehensive course that includes real time projects, assignments and designed by industry experts. The entire training course content is fully aligned towards clearing the exam for the Apache Spark component of the Cloudera Spark and Hadoop Developer certification (CCA175) exam.
Intellipaat offers lifetime access to videos, course materials, 24/7 Support, and course material upgrades to latest version at no extra fees. For Hadoop and Spark training you get the Intellipaat Proprietary Virtual Machine for Lifetime and free cloud access for 6 months for performing training exercises. Hence it is clearly a one-time investment.
This course is designed for clearing the learn Spark and clear the Apache Spark component of the Cloudera Spark and Hadoop Developer certification (CCA175) exam. Check our Hadoop training course for gaining proficiency in the Hadoop component of the CCA175 exam.The complete course is created by industry experts for professionals to get the top jobs in the best organizations. The entire training includes real world projects and case studies that are highly valuable.
Upon completion of the training you will have quizzes that will help you prepare for the CCA175 certification exam and score top marks.
The Intellipaat Certification is awarded upon successfully completing the project work and after reviewing by experts. The Intellipaat certification is recognized in some of the biggest companies like Cisco, Cognizant, Mu Sigma, TCS, Genpact, Hexaware, Sony, Ericsson among others.
This course is designed for clearing the learn Spark and clear the Apache Spark component of the Cloudera Spark and Hadoop Developer certification (CCA175) exam. Check our Hadoop training course for gaining proficiency in the Hadoop component of the CCA175 exam.The complete course is created by industry experts for professionals to get the top jobs in the best organizations. The entire training includes real world projects and case studies that are highly valuable.Intellipaat enjoys strong relationship with multiple staffing companies in US, UK and have +80 clients across the globe. If you are looking out for exploring job opportunities, you can pass your resumes once you complete the course and we will help you with job assistance. We don’t charge any extra fees for passing the resume to our partners and clients
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