Apache Kafka,Spark Scala and Storm Training

SKU: 8801
54 Lesson
|
102 Hours
igmGuru brings to you an all-in-one Apache Kafka, Spark, Scala and Storm Training to give you a 360-degree overview to make the most of real-time processing of unbound data streams with Apache Storm. You will also learn to create apps in Spark with Scala programming. This course covers everything that you need to become job ready by giving you the opportunity to learn from industry experts with 10+ years of experience.
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Scala and Storm Training

Apache Spark Tutorial Overview

Spark Scala Training course has been crafted by subject-matter experts to help you gain expertise in real-time data analytics and open paths to leading organizations. The trainees will work in the real global projects in the Apache Kafka Spark Training by igmGuru, like Spark RDD, Scala programming, Storm topology, Logic Dynamics, Trident Filters, and Spouts. You will also gain expertise in Big Data processing by learning the ideal execution of Apache Storm and Apache Spark with the help of igmGuru's Scala training, which is aligned with the Apache Spark Scala Certification Exam. With the help of this training, you furnish your skills for the challenges that you are going to face in the Big Data Hadoop ecosystem. The Storm Training course is inclusive of the Apache Spark processing engine and also comprises the general-purpose language, Scala. igmGuru's Spark Scala Online Training provides deeper learning and understanding of the Apache Storm computation system.

What is Kafka?

Kafka is an open source software which provides a framework for reading, analyzing, streaming, and storing data. It is available for free to use. It boasts a large network of developers and users who contribute towards new features and offers support for new users and updates.

What is Kafka Architecture?

Kafka consists of Topics, Records, Producers, Consumers, Logs, Brokers, Partitions, and Clusters. Records can have value, key, and timestamp.

What will you learn in this Spark Training?

On completing Hadoop Scala Training, you would be able to have hands-on skills to help you pass the Spark certification exam:

  1. Learn about Spark and programming in Scala
  2. Learn to differentiate between Spark and Hadoop
  3. Learn high-speed installation on Big Data
  4. Cluster distribution of Apache Spark
  5. Install Python, Java, and Scala applications in Apache Spark
  6. Gain ideas of dispensed processing and Storm Architecture, Storm Topology, Logic Dynamics, and features
  7. Know and understand about Trident Filter, Spouts, and roles
  8. Utilizing Storm for real-time analytics with the help of storm training
  9. Analysis types including batch examination

Who Should Enroll for this Scala Course Online?

  1. Big Data professionals
  2. Software Engineers
  3. Data Scientists
  4. Data Analysts
  5. Project Managers
  6. ETL Developers

Apache Spark Training is beneficial for all the people who are looking to develop a career in Big Data.

What are the requirements for taking the Hadoop Spark Training Course?

Basic knowledge of Java would be beneficial. Apart from this, there are no special requirements for the course.

What are the Benefits of choosing this Hadoop Apache Kafka Course?

As the workload is increasing day by day in Big Data, there is an ever-increasing requirement for highly skilled professionals who can work in this domain. Learning Scala can be very helpful for trainees to take some of the best industries jobs in the current times.

Spark Scala Tutorial Key Feature

Apache Kafka Tutorial Modules

1. Hadoop 2.x Cluster Architecture
2. Federation and High Availability
3. A Typical Production Cluster setup
4. Hadoop Cluster Modes
5. Common Hadoop Shell Commands
6. Hadoop 2.x Configuration Files
7. Cloudera Single node cluster
8. Hive, Pig, Sqoop, Flume, Scala and Spark
1. Introducing Big Data & Hadoop
2. What is Big Data and where does Hadoop fits in
3. Two important Hadoop ecosystem componentsnamely Map Reduce and HDFS
4. in-depth Hadoop Distributed File System Replications, Block Size, Secondary Name node, High Availability
5. In-depth YARN Resource Manager, Node Manager
1. Detailed understanding of the working of MapReduce
2. The mapping and reducing process
3. the working of Driver, Combiners, Partitioners, Input Formats, Output Formats, Shuffle and Sor
1. Introducing Hadoop Hive
2. Detailed architecture of Hive
3. Comparing Hive with Pig and RDBMS
4. Working with Hive Query Language
5. Creation of database, table, Group by and other clauses, the various types of Hive tables, Hcatalog, storing the Hive Results, Hive partitioning and Buckets
1. The indexing in Hive
2. Map side Join in Hive
3. Working with complex data types
4. Hive User-defined Functions
5. Introduction to Impala
6. Comparing Hive with Impala
7. The detailed architecture of Impala
1. Apache Pig introduction
2. Its various features, the various data types and schema in Hive, the available functions in Pig, Hive Bags, Tuples and Fields
1. Introduction to Apache Sqoop
2. Sqoop overview
3. Basic imports and exports
4. How to improve Sqoop performance
5. The limitation of Sqoop
6. Introduction to Flume and its Architecture
7. Introduction to HBase
8. The CAP theorem
1. Using Scala for writing Apache Spark applications
2. Detailed study of Scala
3. The need for Scala
4. The concept of object oriented programming
5. Executing the Scala code
6. The various classes in Scala like Getters,Setters, Constructors, Abstract ,Extending Objects, Overriding Methods
7. Java and Scala interoperability
8. The concept of functional programming and anonymous functions
9. Bobsrockets package
10. Comparing the mutable and immutable collections
1. Detailed Apache Spark
2. Its various features, comparing with Hadoop
3. The various Spark components
4. Combining HDFS with Spark, Scalding
5. Introduction to Scala
6. Importance of Scala and RDD
1. The RDD operation in Spark
2. Spark transformations, actions, data loading
3. Comparing with MapReduce
4. Key Value Pair
1. The detailed Spark SQL
2. The significance of SQL in Spark for working with structured data processing
3. Spark SQL JSON support
4. working with XML data, and parquet files
5. Creating HiveContext
6. Writing Data Frame to Hive, reading of JDBC files, the importance of Data Frames in Spark, creating Data Frames, schema manual inferring, working with CSV files, reading of JDBC tables
7. Converting from Data Frame to JDBC, the user-defined functions in Spark SQL, shared variable and accumulators
8. How to query and transform data in Data Frames, how Data Frame provides the benefits of both Spark RDD and Spark SQL, deploying Hive on Spark as the execution engine
1. Different Algorithms, the concept of iterative algorithm in Spark, analyzing with Spark graph processing
2. Introduction to K-Means and machine learning, various variables in Spark like shared variables, broadcast variables, learning about accumulators
1. Introduction to Spark streaming
2. The architecture of Spark Streaming
3. Working with the Spark streaming program
4. Processing data using Spark streaming
5. Processing data using Spark streaming
6. Multi-batch and sliding window operations and working with advanced data sources
1. Create a four node Hadoop cluster setup
2. Running the MapReduce Jobs on the Hadoop cluster
3. Successfully running the MapReduce code
4. Working with the Cloudera Manager setup
1. The overview of Hadoop configuration
2. The importance of Hadoop configuration file
3. The various parameters and values of configuration
4. The HDFS parameters and MapReduce parameters
5. Setting up the Hadoop environment
6. The Include and Exclude configuration files
7. The administration and maintenance of Name node
8. Data node directory structures and files
9. File system image and Edit log
1. Introduction to the Checkpoint Procedure
2. Name node failure and how to ensure the recovery procedure
3. Safe Mode, Metadata and Data backup
4. The various potential problems and solutions, what to look for, how to add and remove nodes
5. Metadata and Data backup
1. How ETL tools work in Big data Industry
2. Introduction to ETL and Data warehousing
3. Working with prominent use cases of Big data in ETL industry
4. End to End ETL PoC showing big data integration with ETL tool
1. Introducing Scala and deployment of Scala for Big Data applications and Apache Spark analytics.
1. The importance of Scala
2. The concept of REPL (Read Evaluate Print Loop)
3. Deep dive into Scala pattern matching, type interface, higher order function, currying, traits
4. Application space and Scala for data analysis
1. Learning about the Scala Interpreter
2. Static object timer in Scala
3. Testing String equality in Scala
4. Implicit classes in Scala
5. The concept of currying in Scala
6. various classes in Scala
1. Learning about the Classes concept
2. Understanding the constructor overloading
3. The various abstract classes
4. The hierarchy types in Scala
5. The concept of object equality
6. The val and var methods in Scala
1. Understanding Sealed traits, wild, constructor, tuple, variable pattern, and constant pattern.
1. Understanding traits in Scala
2. The advantages of traits
3. Linearization of traits
4. The Java equivalent and avoiding of boilerplate code
1. Implementation of traits in Scala and Java
2. Handling of multiple traits extending
1. Introduction to Scala collections
2. Classification of collections
3. The difference between Iterator, and Iterable in Scala
4. Example of list sequence in Scala
1. The two types of collections in Scala, Mutable and Immutable collections
2. Understanding lists and arrays in Scala
3. The list buffer and array buffer
4. Queue in Scala, double-ended queue Deque, Stacks, Sets, Maps, Tuples in Scala
1. Introduction to Scala packages and imports
2. The selective imports, the Scala test classes
3. Introduction to JUnit test class, JUnit interface via JUnit 3 suite for Scala test
4. Packaging of Scala applications in Directory Structure
5. Example of Spark Split and Spark Scala
1. Introduction to Spark
2. How Spark overcomes the drawbacks of working MapReduce
3. Understanding in-memory MapReduce,interactive operations on MapReduce, Spark stack, fine vs. coarse grained update, Spark stack,
4. 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
1. Spark installation guide,Spark configuration, memory management, executor memory vs. driver memory
2. Working with Spark Shell, the concept of Resilient Distributed Datasets (RDD), learning to do functional programming in Spark, the architecture of Spark.
1. The RDD general operations, a read-only partitioned collection of records
2. Using the concept of RDD for faster and efficient data processing,RDD action for Collect, Count, Collectsmap, Saveastextfiles, pair RDD functions
1. Understanding the concept of Key-Value pair in RDDs
2. Learning how Spark makes MapReduce operations faster
3. Various operations of RDD
4. MapReduce interactive operations
5. Fine & coarse grained update
6. Spark stack
7. Deploying a Spark application,Scala built application,creation of mutable list, set & set operations, list, tuple, concatenating list
1. Comparing the Spark applications with Spark Shell
2. Creating a Spark application using Scala or Java
3. Creating application using SBT
4. Deploying application using Maven,the web user interface of Spark application, a real world example of Spark and configuring of Spark
1. Learning about Spark parallel processing, deploying on a cluster, introduction to Spark partitions
2. File-based partitioning of RDDs, understanding of HDFS and data locality, mastering the technique of parallel operations,comparing repartition & coalesce, RDD actions
1. The execution flow in Spark
2. Understanding the RDD persistence overview,Spark execution flow & Spark terminology
3. Distribution shared memory vs. RDD, RDD limitations, Spark shell arguments,distributed persistence
4. RDD lineage,Key/Value pair for sorting implicit conversion like CountByKey, ReduceByKey, SortByKey, AggregataeByKey
1. Spark Streaming Architecture, Writing streaming programcoding, processing of spark stream,processing Spark Discretized Stream (DStream)
2. The context of Spark Streaming, streaming transformation, Flume Spark streaming, request count and Dstream
3. Multi batch operation, sliding window operations and advanced data sources. Different Algorithms, the concept of iterative algorithm in Spark, analyzing with Spark graph processing
4. Introduction to K-Means and machine learning, various variables in Spark like shared variables, broadcast variables, learning about accumulators
1. Introduction to various variables in Spark like shared variables, broadcast variables
2. Learning about accumulators, the common performance issues and troubleshooting the performance problems
1. Learning about Spark SQL, the context of SQL in Spark for providing structured data processing
2. JSON support in Spark SQL, working with XML data, parquet files, creating HiveContext, writing Data Frame to Hive
3. 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
4. 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
1. Learning about the scheduling and partitioning in Spark,hash partition, range partition
2. Scheduling within and around applications, static partitioning, dynamic sharing, fair scheduling
3. 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
1. Big Data characteristics
2. Understanding Hadoop distributed computing, the Bayesian Law, deploying Storm for real time analytics
3. The Apache Storm features, comparing Storm with Hadoop, Storm execution, learning about Tuple, Spout, Bolt
1. Installing the Apache Storm
2. Various types of run modes of Storm
1. Understanding Apache Storm and the data model.
1. Installation of Apache Kakfa and its configuration.
1. Understanding of advanced Storm topics like Spouts, Bolts, Stream Groupings, Topology and its Life cycle, learning about Guaranteed Message Processing.
1. Various Grouping types in Storm, reliable and unreliable messages, Bolt structure and life cycle
2. Understanding Trident topology for failure handling, process, Call Log Analysis Topology for analyzing call logs for calls made from one number to another
1. Understanding of Trident Spouts and its different types
2. The various Trident Spout interface and components
3. Familiarizing with Trident Filter, Aggregator and Functions
1. Various components, classes and interfaces in storm like – Base Rich Bolt Class, i RichBolt Interface, i RichSpout Interface, Base Rich Spout class and the various methodology of working with them.
1. Understanding Cassandra, its core concepts, its strengths and deployment.
1. Twitter Boot Stripping, detailed understanding of Boot Stripping, concepts of Storm, Storm Development Environment.
1. Understanding what is Apache Kafka
2. The various components and use cases of Kafka
3. Implementing Kafka on a single node
1. Learning about the Kafka terminology, deploying single node Kafka with independent Zookeeper
2. Adding replication in Kafka, working with Partitioning and Brokers
3. Understanding Kafka consumers, the Kafka Writes terminology, various failure handling scenarios in Kafka
1. Introduction to multi node cluster setup in Kafka, the various administration commands
2. Leadership balancing and partition rebalancing, graceful shutdown of kafka Brokers and tasks
3. Working with the Partition Reassignment Tool, cluster expending, assigning Custom Partition
4. Removing of a Broker and improving Replication Factor of Partitions
1. Understanding the need for Kafka Integration
2. Successfully integrating it with Apache Flume
3. Steps in integration of Flume with Kafka as a Source
1. Detailed understanding of the Kafka and Flume Integration
2. Deploying Kafka as a Sink and as a Channel
3. Introduction to PyKafka API and setting up the PyKafka Environment
1. Connecting Kafka using PyKafka
2. Writing your own Kafka Producers and Consumers
3. Writing a random JSON Producer
4. Writing a Consumer to read the messages from a topic
5. Writing and working with a File Reader Producer
6. Writing a Consumer to store topics data into a file

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  • Duration : 102 Hrs
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Apache Spark Scala Certification Exam

This Spark Certification training course is developed to help with the successful clearing of the Apache Spark component of the Cloudera Spark and CCA175 exam, thereby qualifying you for the best positions in Top MNCs.
You can check our Scala Training for obtaining expertise in the Hadoop features of the CCA175 examination. While training, participants get to work on live projects and assignments that have enormous inference in the real world industries cases, helping to boost up your career without any problem.

By the end of this course, you will have worked on various quizzes that indicate the question types asked in the Spark certification exam. This will help you to attain better marks in the certification exam in 2024.

Number of Questions: 8–12 performance-based (hands-on) tasks on Cloudera Enterprise cluster.

Time Limit: 120 minutes

Passing Score: 70%

Language: English

Apache Spark Scala Certification Exam

Apache Spark Training Online FAQ

This IgmGuru all-in-one coaching course allows you to master the assorted process tools. This helps in figuring on massive knowledge like Apache Spark, Storm at the side of Scala programming and even what is Kafka. You'll gain full proficiency in • Process massive knowledge • Work on period analytics • Perform an execution, and • Increase the performance of Hadoop framework while getting the Hadoop certification. The coaching course content is totally in line with clearing the Spark element of Cloudera Spark and Hadoop Developer Certification (CCA175). This is a career-oriented course designed by trade consultants. Your educational program includes real-time comes piecemeal assignments to determine your progress and specially designed quizzes for clearing the required certification exams.

No, It is sad, but this is not the case for the time being.

We offer 24 x 7 Support through Emails/Chat and Calls. Our skilled Team can never let go Students and hence get satisfied with our set of skilled mentors, whenever you need their help apart from the normal spark training time!

Apache Kafka is used for storing the incoming messages before processing, whereas Apache Storm works on the real-time messaging system. Kafka is used to process real-time data while Apache Storm is utilized to transfer the data. Kafka derives its data from the original data sources but Apache Storm pulls the data from Kafka for further processes.

For a number of reasons, igmGuru is the best resource for learning Apache Spark, Storm, and Scala. First off, igmGuru provides extensive and sector-specific training created by subject-matter specialists. To stay current with the most recent developments, the courses are frequently updated. Second, igmGuru offers practical instruction using real-world projects so that students can obtain real-world experience. In addition, igmGuru provides a variety of flexible learning choices, including instructor-led live classes and self-paced study. A lively community and 24/7 assistance are also offered by igmGuru to encourage collaborative learning. And lastly, the cost-effectiveness of igmGuru's courses makes top-notch education available to everyone. Making the decision to study with igmGuru guarantees a strong foundation in Apache Spark, Storm, and Scala, enhancing career opportunities in big data processing and analytics.

Apache Spark and Apache Storm are both distributed processing frameworks, but they serve different purposes. Spark is primarily used for batch processing, real-time stream processing, and machine learning tasks. Storm, on the other hand, is designed specifically for real-time stream processing, providing low-latency and fault-tolerant data processing capabilities.

In simple terms, Apache Spark refers to a distributed data processing engine, while Apache Kafka refers to a stream processing engine. They are both offered by the Apache Software Foundation to help process data at a more rapid rate.

As of Apache 3.1.0, the largest and key module written in Scala is the ‘core’ one. The other module written in Scala is a Scala API module for Kafka Streams.

Use Kafka and Spark together by following these steps -

-Build a script to integrate Spark Streaming and Kafka

-Create an RDD -Extract and store Offsets

-Implement SSL Spark communication

-Compile everything & submit to Spark Console

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