Apache Spark and Scala Certification Training Course

Learning Objectives: Learn the basics of Scala that are required for programming Spark applications. In this Apache Spark course module, you will also learn about the basic constructs of Scala such as variable types, control structures, collections such as Array, ArrayBuffer, Map, Lists, and many more.

Topics:
• What is Scala? Preview
• Why Scala for Spark?
• Scala in other Frameworks
• Introduction to Scala REPL
• Basic Scala Operations
• Variable Types in Scala
• Control Structures in Scala Preview
• Foreach loop, Functions and Procedures
• Collections in Scala- Array
• ArrayBuffer, Map, Tuples, Lists, and more

Hands-on:
• Scala REPL Detailed Demo

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 Preview
• Getters and Setters
• Custom Getters and Setters
• Properties with only Getters
• Auxiliary Constructor and Primary Constructor
• Singletons
• Extending a Class Preview
• 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 Preview
• Spark Deployment Modes
• Introduction to Spark Shell
• Writing your first Spark Job Using SBT
• Submitting Spark Job
• Spark Web UI
• Data Ingestion using Sqoop Preview

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 Preview
• Data Loading and Saving Through RDDs Preview
• 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

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

Learning Objectives: In this Apache Spark online training module, you will learn about SparkSQL which is used to process structured data with SQL queries, data-frames and datasets in Spark SQL along with different kind of SQL operations performed on the data-frames. You will also learn about Spark and Hive integration.

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

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

Learning Objectives: Learn why machine learning is needed, different Machine Learning techniques/algorithms, and SparK MLlib.

Topics:
• Why Machine Learning?
• What is Machine Learning? Preview
• Where Machine Learning is Used?
• Face Detection: USE CASE
• Different Types of Machine Learning Techniques Preview
• Introduction to MLlib
• Features of MLlib and MLlib Tools
• Various ML algorithms supported by MLlib

Learning Objectives: Implement various algorithms supported by MLlib such as Linear Regression, Decision Tree, Random Forest and many more.

Topics:
• Supervised Learning - Linear Regression, Logistic Regression, Decision Tree, Random Forest Preview
• Unsupervised Learning - K-Means Clustering & How It Works with MLlib Preview
• Analysis on US Election Data using MLlib (K-Means)

Hands-on:
• Machine Learning MLlib
• K- Means Clustering
• Linear Regression
• Logistic Regression
• Decision Tree
• Random Forest

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? Preview
• 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? Preview
• Basic Flume Architecture
• Flume Sources
• Flume Sinks
• Flume Channels
• Flume Configuration Preview
• 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

Learning Objectives: Work on Spark streaming which is used to build scalable fault-tolerant streaming applications. Also, learn about DStreams and various Transformations performed on the streaming data. You will get to know about commonly used streaming operators such as Sliding Window Operators and Stateful Operators.

Topics:
• Drawbacks in Existing Computing Methods
• Why Streaming is Necessary?
• What is Spark Streaming? Preview
• Spark Streaming Features
• Spark Streaming Workflow Preview
• How Uber Uses Streaming Data
• Streaming Context & DStreams
• Transformations on DStreams
• Describe Windowed Operators and Why it is Useful
• Important Windowed Operators
• Slice, Window and ReduceByWindow Operators
• Stateful Operators

Learning Objectives: In this Apache Spark and Scala training module, you will learn about the different streaming data sources such as Kafka and flume. At the end of the module, you will be able to create a spark streaming application.

Topics:
• Apache Spark Streaming: Data Sources
• Streaming Data Source Overview Preview
• Apache Flume and Apache Kafka Data Sources
• Example: Using a Kafka Direct Data Source
• Perform Twitter Sentimental Analysis Using Spark Streaming Preview

Hands-on:
• Different Streaming Data Sources

There are no specific prerequisites for our Spark Scala Certification Training. However, prior knowledge of Java Programming and SQL will be helpful but not mandatory.

The Spark Certification Training is designed by industry experts to make you a Certified Spark Developer. The Spark Scala Course offers:

• An overview of Big Data & Hadoop, including HDFS (Hadoop Distributed File System) and YARN (Yet Another Resource Negotiator).
• Comprehensive knowledge of various tools that fall in the Spark Ecosystem, such as Spark SQL, Spark MlLib, Sqoop, Kafka, Flume, and Spark Streaming.
• The capability to ingest data in HDFS using Sqoop & Flume and analyze those large datasets stored in the HDFS.
• The power to handle real-time data feeds through a publish-subscribe messaging system like Kafka.
• Exposure to many real-life industry-based projects that will be executed using Edureka’s CloudLab.
• Projects that are diverse in nature, covering banking, telecommunications, social media, and government domains.
• Rigorous involvement of an SME throughout the Spark Training to learn industry standards and best practices.

Spark is one of the fastest-growing and widely used tools for Big Data & Analytics. It has been adopted by multiple companies in various domains around the globe and, therefore, offers promising career opportunities. To take part in these kinds of opportunities, you need structured training that is aligned with Cloudera Hadoop and Spark Developer Certification (CCA175) and current industry requirements and best practices. Besides strong theoretical understanding, it is essential to have strong hands-on experience. Hence, during Edureka’s Spark and Scala course, you will be working on various industry-based use cases and projects incorporating big data and Spark tools as part of the solution strategy. Additionally, all your doubts will be addressed by industry professionals currently working on real-life big data and analytics projects.

Edureka’s Spark Training is designed to help you become a successful Spark developer. During this course, our expert instructors will train you to:

The market for Big Data Analytics is growing tremendously across the world, and the strong growth pattern followed by market demand is a great opportunity for all IT Professionals. Here are a few professional IT groups who are continuously enjoying the benefits and perks of moving into the Big Data domain:

The statistics provided below will give you a glimpse of the growing popularity and adoption rate of Big Data tools like Spark in the current and upcoming years:

As you know, nowadays many organizations are showing interest in Big Data and adopting Spark as part of their solution strategy. The demand for jobs in Big Data and Spark is rising rapidly. So, it is high time to pursue a career in the field of Big Data & Analytics with our Spark and Scala Certification Training Course.

You will execute all your Spark and Scala course assignments and case studies in the Cloud LAB environment provided by Edureka. You will access the Cloud LAB via a browser. In case of any doubts, Edureka’s support team will be available 24/7 for prompt assistance.

CloudLab is a cloud-based Spark and Hadoop environment that Edureka offers with the Spark Course. It allows you to execute all the in-class demos and work on real-life Spark case studies seamlessly. This not only saves you from the trouble of installing and maintaining Spark and Scala on a virtual machine but also provides you with an experience of a real big data and Spark production cluster. You can access the Spark Training CloudLab via your browser, which requires minimal hardware configuration. In case you get stuck at any step, our support team is ready to assist 24/7.

You don't have to worry about the system requirements as you will be executing your practicals in a Cloud LAB, which is a pre-configured environment. This environment already contains all the necessary tools and services required for Edureka's course.

CloudLab is a cloud-based Spark and Hadoop environment that Edureka offers with the Spark Course where you can execute all the in-class demos and work on real life spark case studies fluently. This will not only save you from the trouble of installing and maintaining Spark and Scala on a virtual machine, but will also provide you an experience of a real big data and spark production cluster. You’ll be able to access the Spark Training CloudLab via your browser which requires minimal hardware configuration. In case, you get stuck in any step, our support team is ready to assist 24×7.

You don’t have to worry about the system requirements as you will be executing your practicals on a Cloud LAB which is a pre-configured environment. This environment already contains all the necessary tools and services required for Edureka's course.

Project 1- Domain: Financial Statement: A leading financial bank is trying to broaden the financial inclusion for the unbanked population by providing a positive and safe borrowing experience. In order to make sure this underserved population has a positive loan experience, it makes use of a variety of alternative data--including telco and transactional information--to predict their clients' repayment abilities. The bank has asked you to develop a solution to ensure that clients capable of repayment are not rejected and the loans are given with a principal, maturity, and repayment calendar that will empower their clients to be successful. Project 2- Domain: Transportation Industry Business challenge/requirement: With the spike in pollution levels and the fuel prices, many Bicycle Sharing Programs are running around the world. Bicycle sharing systems are a means of renting bicycles where the process of obtaining membership, rental and bike return is automated via a network of joint locations throughout the city. Using this system people can rent a bike from one location and return it to a different place as and when needed. Considerations: You are building a Bicycle Sharing demand forecasting service that combines historical usage patterns with weather data to forecast the Bicycle rental demand in real-time. To develop this system, you must first explore the dataset and build a model. Once it’s done you must persist the model and then on each request run a Spark job to load the model and make predictions on each Spark Streaming request