• Learning Objectives: In this Scala course module, you will learn about object-oriented programming and functional programming techniques in Scala.
  
  
  Topics:
  
• Functional Programming
  
• Higher Order Functions
  
• Anonymous Functions
  
• Class in Scala
  
• Getters and Setters
  
• Custom Getters and Setters
  
• Properties with only Getters
  
• Auxiliary Constructor and Primary Constructor
  
• Singletons
  
• Extending a Class
  
  
• Overriding Methods
  
• Traits as Interfaces and Layered Traits
  
  Hands-on:
  OOPs Concepts
  Functional Programming
  

• Learning Objectives: Understand Apache Spark and learn how to develop Spark applications. At the end, you will learn how to perform data ingestion using Sqoop.
  
  
  Topics:
  
• Spark’s Place in Hadoop Ecosystem
  
• Spark Components & its Architecture
  
  
• Spark Deployment Modes
  
• Introduction to Spark Shell
  
• Writing your first Spark Job Using SBT
  
• Submitting Spark Job
  
• Spark Web UI
  
• Data Ingestion using Sqoop
  
  Hands-on:
  
• Building and Running Spark Application
  
• Spark Application Web UI
  
• Configuring Spark Properties
  
• Data ingestion using Sqoop
  

Learning Objectives: Get an insight of Spark - RDDs and other RDD related manipulations for implementing business logics (Transformations, Actions, and Functions performed on RDD).


Topics:
Challenges in Existing Computing Methods
Probable Solution & How RDD Solves the Problem
What is RDD, It’s Operations, Transformations & Actions

Data Loading and Saving Through RDDs

Key-Value Pair RDDs
Other Pair RDDs, Two Pair RDDs
RDD Lineage
RDD Persistence
WordCount Program Using RDD Concepts
RDD Partitioning & How It Helps Achieve Parallelization
Passing Functions to Spark

Need for Spark SQL
What is Spark SQL?
Spark SQL Architecture
SQL Context in Spark SQL
User Defined Functions
Data Frames & Datasets
Interoperating with RDDs
JSON and Parquet File Formats
Loading Data through Different Sources
Spark – Hive Integration

Hands-on:
Loading data in RDDs
Saving data through RDDs
RDD Transformations
RDD Actions and Functions
RDD Partitions
WordCount through RDDs

Spark SQL – Creating Data Frames
Loading and Transforming Data through Different Sources
Stock Market Analysis
Spark-Hive Integration

Learning Objectives: Understand Kafka and its Architecture. Also, learn about Kafka Cluster, how to configure different types of Kafka Cluster. Get introduced to Apache Flume, its architecture and how it is integrated with Apache Kafka for event processing. In the end, learn how to ingest streaming data using flume.


Topics:
Need for Kafka
What is Kafka?

Core Concepts of Kafka
Kafka Architecture
Where is Kafka Used?
Understanding the Components of Kafka Cluster
Configuring Kafka Cluster
Kafka Producer and Consumer Java API
Need of Apache Flume
What is Apache Flume?
Basic Flume Architecture
Flume Sources
Flume Sinks
Flume Channels
Flume Configuration

Integrating Apache Flume and Apache Kafka

Hands-on:
Configuring Single Node Single Broker Cluster
Configuring Single Node Multi Broker Cluster
Producing and consuming messages
Flume Commands
Setting up Flume Agent
Streaming Twitter Data into HDFS

Your access to the Support Team is for lifetime and will be available 24/7. The team will help you in resolving queries, during and after the spark course.

Post-enrolment, the LMS access will be instantly provided to you and will be available for lifetime. You will be able to access the complete set of previous class recordings, PPTs, PDFs, assignments. Moreover the access to our 24x7 support team will be granted instantly as well. You can start learning right away.

Yes, the access to the course material will be available for lifetime once you have enrolled into the Apache Spark online course.

We have limited number of participants in a live session to maintain the Quality Standards. So, unfortunately participation in a live class without enrollment is not possible. However, you can go through the sample class recording and it would give you a clear insight about how are the classes conducted, quality of instructors and the level of interaction in a class.

Apache Spark is one of the leading Big Data frameworks that is in demand today. Spark is the next evolutionary change in big data processing environments as it provides batch as well as streaming capabilities. This makes it the ideal framework for anyone looking for speed data analysis. With companies showing eagerness to adopt Spark in their system, learning this framework can help you climb up your career ladder as well.

Scala stands for Scalable languages. Dean Institute Spark and Scala training programs are what you need if you are looking to master Spark with Scala. Our course module starts from the beginning and covers every module necessary. With our instructor-led sessions and a 24x7 support system, we make sure that you achieve your learning objectives.

Dean Institute's vast repository of guides, tutorials, and full-fledged courses will not only help you in understanding Spark but also in mastering it. You can check out our blogs to get started with Spark and have basic foundational knowledge. Our tutorials will then help you in taking a deeper dive and understanding the underlying concepts. After this, our Spark and Scala training will help you in truly mastering the technology with instructor-led sessions and real-world hands-on.

Dean institute's Spark and Scala training is a 6 weeks structured training program aimed at helping our learners master Spark with Scala. In these 6 weeks, you will be attending classes for the live instructor led sessions and also working on various assignments and projects that will help you to have strong understanding of the Spark ecosystem.

Dean institute Spark and Scala Certification Training offers variable batch schedule to suit everyone’s needs. The weekend batches run for 6 weeks of live instructor led sessions. Which is then followed by real-time project for better hands-on. The accelerated program or the weekday batches can be completed in much shorter time with rigorous training sessions and live project to work-on at the end.

Learning pedagogy has evolved with the advent of technology. Online training adds convenience and quality to the training module. With our 24x7 support system, our online learners will have someone to help them all the time even after the class ends. This is one of the driving factors to make sure that people achieve their end learning objective. We also provide life-time access of our updated course material to all our learners.

Big data as technology is dominating the job market. For complete beginners, we have compiled an extensive list of blogs and tutorials on our blogging and Youtube channel which can definitely be a great help if you are looking to start out. Once, you are clear with the basic concepts, you can think about taking up Dean Institute Apache Spark and Scala Certification Training to truly master the technology.

• Followings are the top 5 certification:
  
  
• Cloudera Spark and Hadoop Developer
  
• HDP Certified Apache Spark Developer
  
• MapR Certified Spark Developer
  
• Databricks Apache Spark Certifications
  
• O’Reilly Developer Apache Spark Certifications

If you are thinking about a career in big data, this is the first step to getting the spark certification. This certification will give you a boost in your career. Once you are certified by spark, you will have the validation of your Spark skills. This certification is highly sought-after by almost all companies.

It is easy to obtain Spark certification preparation. There are many ways to get certified. The best reason to get certified is that it will give you an edge over your peers. Because there is fierce competition outside.

Databricks Certified Associate developer for Apache Spark 3.0 certification tests your understanding of Spark DataFrame API. It also assesses your ability to use the Spark DataFrame API in order to perform basic data manipulation tasks within a Spark session. These tasks include manipulating, filtering, dropping and sorting columns, handling missing data, and combining, reading and writing DataFrames with schemas. They also involve working with UDFs or Spark SQL functions. The exam will also assess fundamental aspects of Spark architecture such as execution/deployment mode, execution hierarchy, fault tolerance and garbage collection.