Apache Spark and Scala Certification
Your mastery of the fundamentals of the Apache Spark open-source framework and the Scala programming language, including Shell Scripting Spark, GraphX programming, machine learning, and Spark streaming, is facilitated by this Spark certification class. You will also comprehend how Spark helps to overcome MapReduce’s drawbacks.
Overview
The goal of the Apache Spark Certification Training Course is to give you the know-how and abilities needed to succeed as a Big Data & Spark Developer. You can pass the CCA Spark and Hadoop Developer (CCA175) Exam with the aid of this course. You will comprehend the fundamentals of Hadoop and Big Data as well as how Spark performs far quicker than Hadoop MapReduce and allows for in-memory data processing. Additionally covered in this course are RDDs, Spark SQL for structured processing, and other Spark APIs, including Spark Streaming and Spark MLlib. An essential component of a Big Data Developer’s professional path is this online Scala course. It will also cover basic ideas like messaging systems like Kafka, data loading with Sqoop, and data collecting with Flume.
What you will learn in Apache Spark and Scala Certification course?
- Big Data Introduction
- Introduction on Scala
- Spark Introduction
- Spark Framework & Methodologies
- Spark Data Structure
- Spark Ecosystem
Who should go for Apache Spark and Scala Certification course?
- Data Scientists
- Data Engineers
- Data Analysts
- BI Professionals
- Research professionals
- Software Architects
- Software Developers
- Testing Professionals
- Anyone who is looking to upgrade Big Data skills
Our Package
Introduction to Big Data Hadoop and Spark
- 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
- Hadoop Terminal Commands
- Big Data Analytics with Batch & Real-time Processing
- Why Spark is needed?
- What is Spark?
- How Spark differs from other frameworks?
- Spark at Yahoo!
Introduction to Scala for Apache Spark
- 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
Functional Programming and OOPs Concepts in Scala
- 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
Deep Dive into Apache Spark Framework
- 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
Playing with Spark RDDs
- 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
DataFrames and Spark SQL
- 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
Machine Learning using 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
Deep Dive into Spark MLlib
- 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)
Understanding Apache Kafka and Apache Flume
- 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
Apache Spark Streaming - Processing Multiple Batches
- 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
- Stateful Operators
Apache Spark Streaming - Data Sources
- 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
Upcoming Batch
April 20th (Weekends)
FRI & SAT (4 Weeks)
08:30 PM to 01:00 AM (CDT)
April 18th (Weekdays)
MON – FRI (18 Days)
10:00 AM to 12:00 PM (CDT)
Apache Spark and Scala Certification FAQs
Q. Why should I learn Apache Spark?
Ans.
- Spark can be integrated well with Hadoop and that’s a great advantage for those who are familiar with the latter.
- According to technology forecasts, Spark is the future of worldwide Big Data Processing. The standards of Big Data Analytics are rising immensely with Spark, driven by high-speed data processing and real time results.
- Spark is an in-memory data processing framework and is all set to take up all the primary processing for Hadoop workloads in the future. Being way faster and easier to program than MapReduce, Spark is now among the top-level Apache projects.
- The number of companies that are using Spark or are planning the same has exploded over the last year. There is a massive surge in the popularity of Spark, the reason being its matured open-source components and an expanding community of users.
- There is a huge demand for Spark Professionals and the demand for them is increasing.
Q. What should be the system requirements for me to learn Apache Spark online?
Ans.
You just need 4GB RAM to learn Spark.
Windows 7 or higher OS
i3 or higher processor
Q. What are the course objectives?
Ans.
You will get in-depth knowledge on Apache Spark and the Spark Ecosystem, which includes Spark RDD, Spark SQL, Spark MLlib and Spark Streaming. You will get comprehensive knowledge on Scala Programming language, HDFS, Sqoop, FLume, Spark GraphX and Messaging System such as Kafka.
Q. How long will it take to complete the course?
Ans.
The sessions that are conducted are 24 hours of live sessions, with 70+ hours MCQs and Assignments and 23 hours of hands-on sessions.
Q. What are the prerequisites for learning Apache Spark?
Ans.
- Basics of Hadoop file system
- Understanding of SQL concepts
- Basics of any Distributed Database (HBase, Cassandra)
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