- 30 Hours of Case Studies on Real-life Scenarios
- 10 Sessions of 3 hours each on weekends
- Complimentary course of Java Essentials
- Practical Assignments
- Lifetime Access to Learning Management System
- 24x7 Expert Support
- Course Completion Certificate
- Online Forum for Discussions
Available Courses Delivery
This course is available in the following formats:
Access live online training from anywhere taught by expert instructors
Search and study from listed class recordings and materialsView Batches
Filling Fast Delivery: Online
Sat - Sun (5 Weeks)
Timings - 10:00 AM to 01:00 PM (EST)
Weekend Batch (Morning)
This course has been curated with the aim of training learners in a distributed streaming platform, known as Apache Kafka. It teaches about storing streams of records in a fault-tolerant substantial way. It trains in publishing streams of records and consequently subscribing to it. It educates about the various concepts, such as, Apache Spark Framework, Functional Programming and OOPs concepts, Machine Learning using Spark MLlib, Apache Kafka, Apache Flume, and Apache Spark Streaming.
- Educate about Kafka and its components
- Train in installing and configuring Kafka
- Acquaint with fundamental concepts and architecture of Kafka
- Teach about incorporating Kafka with real-time streaming, such as, Spark and Storm
- Teach about developing a quality messaging system using basic and advanced features
- Educate about applying Kafka to develop messages from numerous streaming sources
- Train in setting up end-to-end Kafka cluster along with Hadoop and YARN cluster
- Familiarize with Kafka Stream API
- Demonstrate expertise in Kafka Architecture, Installation, Configuration, Performance Tuning and Kafka Client?s API
- Great remuneration as an expert in Apache Kafka
- Multi-industry opportunities
- Basic knowledge of Java fundamental concepts will be beneficial
Who should take up?
- Project Managers
- Big Data Architects
- Professionals keen on learning Apache Kafka
- Testing Professionals
- 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!
- 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
- Higher Order Functions
- Anonymous Functions
- Class in Scala
- Getters and Setters
- Custom Getters and Setters
- Properties with only Getters
- Auxiliary Constructor and Primary Constructor
- Extending a Class
- Overriding Methods
- Traits as Interfaces and Layered Traits
- 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
- 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
- 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
- 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)
- 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
- 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
- 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
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