There are no prerequisites for taking up this course. Basic knowledge of database, SQL and query language can help.
The average salary of a Spark Professional in Chicago is $104,356 per year.
Chicago is an emerging technology hub in USA, and its growing market is attracting top companies around the world to exploit the growth opportunities prevailing in this city. The availability of resources and the supporting technology environment of this city have encouraged Chicago-based firms to invest in Big Data tools like Spark and Scala. Hence, the number of job opportunities for Spark and Scala professionals is increasing in this market.
Chicago is one of the top ten cities in USA in terms of technology and businesses. With the increase in real-time analytics across the globe, the companies in Chicago have started collaborating smart Big Data tools like Apache Spark and Scala for faster and smarter operations. Therefore, trained Spark and Scala professionals are much in-demand in this city.
Apache Spark and Scala complement each other in performing Big Data operations. While the former lets you handle the streaming data with the feature of parallelism, the latter allows you to maximize the potential of your Big Data cluster. Intellipaat helps you master these two technologies with Apache Spark and Scala training course with the help of interactive classes and real-time projects. This course assists you in qualifying for Cloudera Spark and Hadoop Developer Certification (CCA175) exam.
Introducing Scala and deployment of Scala for Big Data applications and Apache Spark analytics, Scala REPL, Lazy Values, Control Structures in Scala, Directed Acyclic Graph (DAG), First Spark Application Using SBT/Eclipse, Spark Web UI, Spark in Hadoop Ecosystem.
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 and testing string equality in Scala, implicit classes in Scala, the concept of currying in Scala and various classes in Scala
Learning about the Classes concept, understanding the constructor overloading, various abstract classes, the hierarchy types in Scala, the concept of object equality and 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 and handling of multiple traits extending
Introduction to Scala collections, classification of collections, the difference between Iterator and Iterable in Scala and 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 and double-ended queue Deque, Stacks, Sets, Maps and 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 and examples 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 and 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 and the architecture of Spark
Spark RDD, creating RDDs, RDD partitioning, operations, and 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, collects map, save-as-text-files and 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 and coarse-grained update and 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 and 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 and coalesce and RDD actions
The execution flow in Spark, understanding the RDD persistence overview, Spark execution flow, and Spark terminology, distribution shared memory vs. RDD, RDD limitations, Spark shell arguments, distributed persistence, RDD lineage, Key-Value pair for sorting implicit conversions like CountByKey, ReduceByKey, SortByKey and AggregateByKey
Introduction to Machine Learning, types of Machine Learning, introduction to MLlib, various ML algorithms supported by MLlib, Linear Regression, Logistic Regression, Decision Tree, Random Forest, K-means clustering techniques, building a Recommendation Engine
Hands-on Exercise: Building a Recommendation Engine
Why Kafka, what is Kafka, Kafka architecture, Kafka workflow, configuring Kafka cluster, basic operations, Kafka monitoring tools, integrating Apache Flume and Apache Kafka
Hands-on Exercise: Configuring Single Node Single Broker Cluster, Configuring Single Node Multi Broker Cluster, Producing and consuming messages, Integrating Apache Flume and Apache Kafka.
Introduction to Spark Streaming, features of Spark Streaming, Spark Streaming workflow, initializing StreamingContext, Discretized Streams (DStreams), Input DStreams and Receivers, transformations on DStreams, Output Operations on DStreams, Windowed Operators and why it is useful, important Windowed Operators, Stateful Operators.
Hands-on Exercise: Twitter Sentiment Analysis, streaming using netcat server, Kafka-Spark Streaming and Spark-Flume Streaming
Introduction to various variables in Spark like shared variables and 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 Hive context, 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 variables and accumulators, learning to query and transform data in Data Frames, how Data Frame provides the benefit of both Spark RDD and Spark SQL and 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 and High Order Functions
Project 1: Movie Recommendation
Topics: This is a project wherein you will gain hands-on experience in deploying Apache Spark for the 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. You will understand how to deploy collaborative filtering, clustering, regression, and dimensionality reduction in MLlib. Upon the 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 Dataset
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 in 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 which is designed by industry experts. The entire 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 the 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 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 the 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 its review by experts. The Intellipaat certification is recognized in some of the biggest companies like Cisco, Cognizant, Mu Sigma, TCS, Genpact, Hexaware, Sony and Ericsson among others.
This course is designed for clearing 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 the US and the UK and has 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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