Apache Spark and Scala Certification Training
Apache Spark and Scala Certification Training is designed to provide knowledge and skills to become a successful Spark Developer and prepare you for the Cloudera Certified Associate Spark Hadoop Developer Certification Exam CCA175. You will get in-depth knowledge of concepts such as HDFS, Flume, Sqoop, RDDs, Spark Streaming, MLlib, SparkSQL, Kafka cluster & API by taking this Course.
Why this course ?
Spark has overtaken Hadoop as the most active open source Big Data framework – Forbes
Apache Spark will dominate the Big Data landscape by 2022 – Wikibon
The average pay stands at 108,366 USD p.a – Indeed.com
Learning Objectives: Understand Big Data and its components such as HDFS. You will learn about the Hadoop Cluster Architecture, Introduction to Spark and the difference between batch processing and real-time processing.
What is Big Data?
Big Data Customer Scenarios
Limitations and Solutions of Existing Data Analytics Architecture with Uber Use Case
How Hadoop Solves the Big Data Problem?
What is Hadoop?
Hadoop’s Key Characteristics
Hadoop Ecosystem and HDFS
Hadoop Core Components
Rack Awareness and Block Replication
YARN and its Advantage
Hadoop Cluster and its Architecture
Hadoop: Different Cluster Modes
Big Data Analytics with Batch & Real-time Processing
Why Spark is needed?
What is Spark?
How Spark differs from other frameworks?
Spark at Yahoo!
Learning Objectives: Learn the basics of Scala that are required for programming Spark applications. 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.
What is Scala?
Why Scala for Spark?
Scala in other Frameworks
Introduction to Scala REPL
Basic Scala Operations
Variable Types in Scala
Control Structures in Scala
Foreach loop, Functions and Procedures
Collections in Scala- Array
ArrayBuffer, Map, Tuples, Lists, and more
Scala REPL Detailed Demo
Learning Objectives: In this module, you will learn about object-oriented programming and functional programming techniques in Scala.
Higher Order Functions
Class in Scala
Getters and Setters
Custom Getters and Setters
Properties with only Getters
Auxiliary Constructor and Primary Constructor
Extending a Class
Traits as Interfaces and Layered Traits
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.
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
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).
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
WordCount Program Using RDD Concepts
RDD Partitioning & How It Helps Achieve Parallelization
Passing Functions to Spark
Loading data in RDDs
Saving data through RDDs
RDD Actions and Functions
WordCount through RDDs
Learning Objectives: In this 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.
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
Spark SQL – Creating Data Frames
Loading and Transforming Data through Different Sources
Stock Market Analysis
Learning Objectives: Learn why machine learning is needed, different Machine Learning techniques/algorithms, and SparK MLlib.
Why Machine Learning?
What is Machine Learning?
Where Machine Learning is Used?
Face Detection: USE CASE
Different Types of Machine Learning Techniques
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.
Supervised Learning – Linear Regression, Logistic Regression, Decision Tree, Random Forest
Unsupervised Learning – K-Means Clustering & How It Works with MLlib
Analysis on US Election Data using MLlib (K-Means)
Machine Learning MLlib
K- Means Clustering
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.
Need for Kafka
What is Kafka?
Core Concepts of Kafka
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
Integrating Apache Flume and Apache Kafka
Configuring Single Node Single Broker Cluster
Configuring Single Node Multi Broker Cluster
Producing and consuming messages
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.
Drawbacks in Existing Computing Methods
Why Streaming is Necessary?
What is Spark Streaming?
Spark Streaming Features
Spark Streaming Workflow
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
Learning Objectives: In this 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.
Apache Spark Streaming: Data Sources
Streaming Data Source Overview
Apache Flume and Apache Kafka Data Sources
Example: Using a Kafka Direct Data Source
Perform Twitter Sentimental Analysis Using Spark Streaming
Different Streaming Data Sources
Learning Objectives: Work on an end-to-end Financial domain project covering all the major concepts of Spark taught during the course.
Learning Objectives: In this module, you will be learning the key concepts of Spark GraphX programming and operations along with different GraphX algorithms and their implementations.