- Introduction 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 and Spark in Hadoop Ecosystem.
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Apache Spark, Scala, and Storm Training
Learn Apache backwards and forwards then hit the jackpot by latching onto the perfect opportunity as a programmer with the certification course by Intellipaat.
Online
₹ 13,110
Quick facts
particular | details | ||
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Medium of instructions
English
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Mode of learning
Self study, Virtual Classroom
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Mode of Delivery
Video and Text Based
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Frequency of Classes
Weekends
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Course overview
The Apache Spark, Scala, and Storm Training are designed with the purpose of providing masters in data analytics and high-speed processing. The course consists of practical real-life projects with theoretical knowledge for the overall development of the candidate. Apache has developed itself into a popular cross-platform web server software. It is responsible for accepting directory (HTTP) requests from Internet users and then sending them their desired information in the form of files and Web pages. With the enhancement in technology, the scope of programming has widened.
The course is designed for people who are interested in programming and want to make their career in the same. The Apache Spark, Scala, and Storm Training certification course by Intellipaat brings the programming language and the skills required for mastering the programming language handy at the click of a button. Candidates can avail the course on their laptops at a convenient time slot. The Apache Spark, Scala, and Storm Training are 40 hours of self-paced videos and 80 hours of project and exercise that ensures full development and deep understanding of the concept. The course also ensures mentor support and job assistance. After completion of the course, the candidate securing 60% marks and completing the projects shall receive the Apache Spark, Scala, and Storm Training certification by Intellipaat.
The highlights
- 100% Online course
- Certification
- 40 hours self-paced videos
- 80 hours practical assessment
- Job assistance
- Flexible schedule
- Lifetime upgrade
- Mentor support
Program offerings
- Online course
- 40 hours instructor-led training course
- 80 hours project learning
- Convenient learning
- Video demonstration
- Assessments
- Certification
- Job assistance.
Course and certificate fees
Fees information
The Apache Spark, Scala, and Storm Training certification fee depend upon the learning mode chosen by the candidate. The candidate should pay a one-time fee at the time of registration. The details of the fee are mentioned below in the table.
Fee structure for Apache Spark, Scala, and Storm Training
Course name | Fee in INR |
Apache Spark, Scala, and Storm Training self-paced learning | Rs.13,110 |
Apache Spark, Scala, and Storm Training Online classroom | Rs. 30,039 |
Apache Spark, Scala, and Storm corporate learning | - |
certificate availability
certificate providing authority
Eligibility criteria
Eligibility
The candidate must have a basic knowledge of Java for taking up the Apache Spark, Scala, and Storm Training classes.
Certification Qualifying Details
The course gives multiple learning options for the learners. The candidate has two options to choose from, self-paced learning, online classroom learning. There is another option available for Corporate learning which can be subscribed to the organization for inculcating the skills in their candidates as per the market requirements. The candidate is required to pass assessments and complete the project associated with the program of Apache Spark, Scala, and Storm Training certification syllabus from Intellipaat. After the project completion and passing the internal assessments with 60% marks the candidate will receive qualifying certification by Intellipaat..
What you will learn
Apache Storm and Apache Spark both are platforms used for Big Data processing that work with real-time data streams. The basic difference between the two is Storm parallelizes task computation while Spark parallelizes data computations. The third technology of Spark is an open-source, processing system. It is used for big data workloads. The tool utilizes in-memory caching, and then optimized query execution for fast analytic queries. The Apache Spark, Scala, and Storm Training online course shall inculcate the skills required to operate the tools effectively. Moreover, the real-life projects shall help the candidate to learn and adapt more concepts. After the completion of the course, the learner will be able to perform the following tasks perfectly:
- Understanding the Spark and programming in Scala
- Comparing between Spark and Hadoop
- Highspeed processing on Big Data analytics
- Apache Spark’s cluster deployment
- Deploying the application of Python, Java, and scala in Apache spark
- Distributed processing and storm architecture
- Storm topology, the logic dynamics, and the components
- Trident filter, spouts, and functions
- Using Storm for real-time analytics
- Types of analyses including batch analysis
Who it is for
The Apache Spark, Scala, and Storm Training certification benefit the programmers or the people who are looking forward to making their career in data analysis through programming. It shall open the gates of opportunities for Big Data analysts. The course shall also help the candidate in the overall development and also help them by providing the working scenario in the corporates. While working on real-life projects and taking up the feature of job assistance, the candidate will become proficient in the domain.
Admission details
To get admission in Apache Spark, Scala, and Storm Training follow the steps mentioned below:
Step 1: Visit the Intellipaat portal or click on the link https://intellipaat.com/apache-storm-spark-scala-training/.
Step 2: Click on the ‘Enroll Now’ Tab and select the learning mode.
Step 3: Fill in the required details and edit the cart.
Step 4: Pay theApache Spark, Scala and Storm Training certification fee.
Step 5: Start your Apache Spark, Scala and Storm training.
The syllabus
Scala Course Content
Introduction to Scala
Pattern Matching
- The importance of Scala,
- the concept of REPL (Read Evaluate Print Loop),
- deep dive into Scala pattern matching,
- Type of interface,
- Higher-order function,
- Currying,
- Traits,
- Application space and Scala for data analytics
Executing the Scala Code
- 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
Classes Concept 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
Case Classes and Pattern Matching
Understanding Sealed traits, wild, constructor, tuple, variable pattern, and constant pattern
Concepts of Traits with Example
- Understanding traits in Scala,
- The advantages of traits,
- Linearization of traits,
- The Java equivalent and avoiding of boilerplate code
Scala–Java Interoperability
Implementation of traits in Scala and Java and handling of multiple traits extending
Scala Collection
- Introduction to Scala collections,
- Classification of collections,
- The difference between Iterator and Iterable in Scala and an example of list sequence in Scala
Mutable Collections Vs. Immutable Collections
- 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 and 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
- Examples of Spark Split and Spark Scala
Spark Course Content
Introduction to 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
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 and the architecture of Spark
Working with RDDs in 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
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 and coarse-grained update and 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 and set operations, list, tuple, concatenating list,
- Creating applications 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 and coalesce and RDD actions
Spark RDD Persistence
- 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
Spark MLlib
- 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)
Integrating Apache Flume and Apache Kafka
- 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 Streaming
- 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)
Improving Spark Performance
- Introduction to various variables in Spark like shared variables and 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 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
- 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 Strome Course Content
Understanding the Architecture of Storm
- Big Data characteristics,
- Understanding Hadoop distributed computing,
- The Bayesian Law,
- Deploying Storm for real-time analytics,
- Apache Storm features,
- Comparing Storm with Hadoop,
- Storm execution and
- Learning about Tuple, Spout, and Bolt
Installation of Apache Storm
Installing Apache Storm and various types of run modes of Storm
Introduction to Apache Storm
Understanding Apache Storm and the data model
Apache Kafka Installation
- Installation of Apache Kafka and its configuration
Apache Storm Advanced
- Understanding advanced Storm topics like Spouts, Bolts, Stream Groupings
- Topology and its life cycle
- Learning about guaranteed message processing
Storm Topology
- Various grouping types in Storm,
- Reliable and unreliable messages,
- Bolt structure and life cycle,
- Understanding Trident topology for failure handling,
- Process and call log analysis topology for analyzing call logs for calls made from one number to another
Overview of Trident
- Understanding of Trident spouts and their different types,
- Various Trident spout interface and components,
- Familiarizing with Trident filter,
- Aggregator and functions
- A practical and hands-on use case on solving call log problem using Storm Trident
Storm Components and Classes
- Various components,
- Classes and interfaces in Storm like,
- Base Rich Bolt Class,
- I RichBolt Interface,
- I RichSpout Interface and Base Rich Spout class
- The various methodologies of working with them
Cassandra Introduction
Understanding Cassandra, its core concepts and its strengths and deployment
Boot Stripping
- Twitter Boot Stripping,
- Detailed understanding of Boot Stripping,
- Concepts of Storm and Storm development environment
How it helps
The Apache Spark, Scala, and Storm Training certifications are structured to provide basic to advance knowledge of Apache Spark, Scala, and Storm. The objective of this course is to train the learner regarding programming skills. The practical knowledge is given to the candidate for preparing them for real-life projects. The course also provides job assistance to land upon the dream job. Besides this, the program also provides the additional feature of peer learning. This shall help the candidates by solving their doubts and also keep them aware of hackathons and other technical events. The feature also provides information on projects, and assists candidates in many ways. Intellipaat also offers full development of the candidate with job assistance, that can help the candidate to apply in the big corporates
FAQs
Intellipaat issues the certificate after the candidate completes practicals and scores 60% marks in the qualifying quiz. The certificate is recognized in over 80 top MNC companies.
The course has the feature of 24*7 support by the mentors.
The peer learning feature shall allow the candidate to put up their doubts or solve others’ doubts. It further allows candidates to chat with juniors or seniors. In addition to this peer groups are the place to share information on Hackathons and other technical events.
All the courses that are associated with the course are mentioned below:
- Movie recommendation
- Twitter API Integration for tweet Analysis
- Data Exploration using Spark SQL
- Call log analysis using Trident
- Twitter data analysis using Trident
- The US presidential election result analysis using Trident DRPC query
The course offers instructor-led online training and self-paced training.