Apache Spark and Scala Certification Training

234+ Learners

Apache Spark and Scala Certification Training is mainly designed for Big Data Developers to program in Apache Scala and to use RDD in Apache Spark for creating an application. This certification helps you to wrap your head around Apache Spark and Scala to become a specialist in Big Data and helps you to have hands-on experience on real-time projects.

Instructor Led Training

Flexible Schedule

LifeTime Free Upgrade

24x7 Support

Instructor Led Training

Flexible Schedule

LifeTime Free Upgrade

24x7 Support

Apache Spark And Scala Certification Training

Jul 25

base64:type251:e1NhdCxTdW59 (18 Weeks) Weekend Batch

07:00 AM 09:00 AM

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Course Price at

$ 589

About Course

Spark and Scala Training Overview:

StepLeaf’s Apache Spark and Scala Certification Training course helps you to gain skills to develop solutions for Apache Spark platform. It elaborates the effective use of memory and powerful programming model. You will learn more about Apache Spark Internals, Spark SQL, RDD, MLib and GraphX.

What will you learn in the Apache Spark and Scala Certification Training?

Master the concepts of Apache Spark and Scala frameworks
Understand and work on Spark Internals, RDD and Spark API’s.
Implementing Spark on a cluster
Write applications using Scala.
RDD and its operations
Understand Spark Streaming
Java-Scala interoperability
Work on real time projects to run Spark app

Who should take up this Apache Spark and Scala Certification Training?

Spark is the hottest technology in the current scenario. A Big Data professional or anyone from the software genre can take up this course.

What are the prerequisites for this Apache Spark and Scala Certification Training

There is no special qualification necessary to join the Apache Spark and Scala training but little SQL knowledge will help you to speed up your learning.

Why should you take up this Apache Spark and Scala Certification Training?

Apache Spark is a fascination platform for its ability to store data with high speed machine learning. It helps you to pace up with Growing Enterprise Adoption. Spark developers are in high demand, such that even companies are ready to bend their requirements.

Key Skills

scala, patternmatching, scalacodeexecution, classes, traits, scala–javainteroperability, scalacollections, mutablecollectionsvs.immutablecollections, bobsrocketspackage, spark, rddsinspark

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Course Contents

Scala

Introducing Scala, 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 and 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 Scala collections, classification of collections, the difference between Iterator and Iterable in Scala and example of list sequence in Scala

Spark

Introduction to Spark, how Spark overcomes the drawbacks of working on 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 than Hadoop, deploying Spark without Hadoop, Spark history server and Cloudera distribution

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

Why Kafka, what is Kafka, Kafka architecture, Kafka workflow, configuring Kafka cluster, basic operations, Kafka monitoring tools and 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 and integrating Apache Flume and Apache Kafka

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

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 and building a Recommendation Engine

Hands-on Exercise: Building a Recommendation Engine

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 and 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

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Projects

Project:#1

Problem Statement

In a retail store, to analyze the most purchased product we try to deploy Apache Spark. You will have practical experience in working with collaborative filtering, regression, clustering and dimensionality reduction in MLlib. At the end of the course you will be able to work with streaming data, testing and statistics.

Project:#2

Problem Statement

This project will help you to explore more about data using Spark SQL. You will be using Spark SQL with ETL application, batch analysis, analysis of data, deploying Machine Learning(ML) and processing of graphs.

Project:#3

Problem Statement

In this process you will be able to analyze each tweet on twitter. The data is available in JSON format and you have to aggregate, filter and parse to analyze the tweets.

Apache Spark Certification

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 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 StepLeaf certification is awarded upon successfully completing the project work and after its review by experts.

Certification given after completing the Online Professional Development Courses

FAQ

StepLeaf uses a blended learning technique which consists of auditory, visual, hands-on and much more technique at the same time. We assess both students and instructors to make sure that no one falls short of the course goal.

The fee of each training course varies according to the curriculum and the duration preferred by the student. For further information please look into the link of the preferred course.

Yes, we offer crash courses. You could get the overview of the whole course and can drive it within a short period of time.

Currently we don't offer demo class as the number of students who attend the live sessions are limited. You could see our recorded video of the class in each course description page to get the insight of the class and the quality of our instructors

Each student who joins StepLeaf will be allocated with a learning manager to whom you can contact anytime to clarify your queries

StepLeaf has a study repository where you can find the recorded video of each class and all other essential resources for the course.

Yes we have a centralized study repository, where students can jump in and explore all the latest materials of latest technologies.

Assessment is a continuous process in StepLeaf where a student's goal is clearly defined and identifies the learning outcome. We conduct weekly mock tests, so that students can find their shortfalls and improve them before the final certification exam.

StepLeaf offers a discussion board where students can react to content, share challenges, teach each other and experiment their new skills.

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