Pyspark Training Certification

What is Big Data? Big Data Customer Scenarios
The 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 the Batch and Real-Time Processing
Why Spark is Needed?
What is Spark?
How Spark Differs from its Competitors?
Spark at eBay
Spark’s Place in Hadoop Ecosystem

Overview of Python Different Applications where Python is Used
Values, Types, Variables
Operands and Expressions
Conditional Statements
Loops
Command Line Arguments
Writing to the Screen
Python files I/O Functions
Numbers
Strings and related operations
Tuples and related operations
Lists and related operations
Dictionaries and related operations
Sets and related operations

Functions Function Parameters
Global Variables
Variable Scope and Returning Values
Lambda Functions
Object-Oriented Concepts
Standard Libraries
Modules Used in Python
The Import Statements
Module Search Path
Package Installation Ways

Spark Components & its Architecture Spark Deployment Modes
Introduction to PySpark Shell
Submitting PySpark Job
Spark Web UI
Writing the first PySpark Job Using to Jupyter Notebook
Data Ingestion using Sqoop

Challenges in Existing Computing Methods The Probable Solution & How RDD Solves the Problem
What is RDD, It’s Operations, Transformations & Actions
The Data Loading and Saving Through RDDs
The Key-Value Pair RDDs
Other Pair RDDs, Two Pair RDDs
RDD Lineage
RDD Persistence
The WordCount Program Using RDD Concepts
Partitioning RDD & How is it Helps Achieve Parallelization
Passing Functions to Spark

Need for Spark SQL What is Spark SQL
Spark SQL Architecture
SQL Context in Spark SQL
Schema RDDs
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 the Learning: Linear Regression, Logistic Regression, Decision Tree, Random Forest Unsupervised the Learning: K-Means Clustering & How It Works with MLlib
The Analysis of US Election Data using to 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
Describes Windowed Operators and Why is it 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

Domain: Media and Entertainment Statement: The Analyze and deduce of the best performing movies based on the customer feedback and review.
Use the two different of API's (Spark RDD and Spark DataFrame) on datasets to find the best ranking movies.

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