Python Spark Training Course Content

1. 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
Big Data Analytics with Batch & 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

2. Introduction to Python for Apache Spark
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

Hands-On:
Creating “Hello World” code
Demonstrating Conditional Statements
Demonstrating Loops
Tuple – properties, related operations, compared with list
List – properties, related operations
Dictionary – properties, related operations
Set – properties, related operations

3. Functions, OOPs, and Modules in Python
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

Hands-On:
Functions – Syntax, Arguments, Keyword Arguments, Return Values
Lambda – Features, Syntax, Options, Compared with the Functions
Sorting – Sequences, Dictionaries, Limitations of Sorting
Errors and Exceptions – Types of Issues, Remediation
Packages and Module – Modules, Import Options, sys Path

4. Deep Dive into Apache Spark Framework
Spark Components & its Architecture
Spark Deployment Modes
Introduction to PySpark Shell
Submitting PySpark Job
Spark Web UI
Writing your first PySpark Job Using Jupyter Notebook
Data Ingestion using Sqoop

Hands-On:
Building and Running Spark Application
Spark Application Web UI
Understanding different Spark Properties

5. 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

Hands-On:
Loading data in RDDs
Saving data through RDDs
RDD Transformations
RDD Actions and Functions
RDD Partitions
Wordcount through RDDs

6. Data Frames and Spark SQL
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

Hands-On:
Spark SQL – Creating data frames
Loading and transforming data through different sources
Stock Market Analysis
Spark-Hive Integration

7. 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

8. 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 of US Election Data using MLlib (K-Means)

Hands-On:
K- Means Clustering
Linear Regression
Logistic Regression
Decision Tree
Random Forest

9. 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

Hands-On:
Configuring Single Node Single Broker Cluster
Configuring Single Node Multi-Broker Cluster
Producing and consuming messages through Kafka Java API
Flume Commands
Setting up Flume Agent
Streaming Twitter Data into HDFS

10. 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 Reduce By Window Operators
State full Operators

Hands-On:
Wordcount Program using Spark Streaming

11. 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

Hands-On:
Various Spark Streaming Data Sources

12. Spark GraphX (Self-Paced)
Introduction to Spark GraphX
Information about a Graph
GraphX Basic APIs and Operations
Spark GraphX Algorithm – PageRank, Personalized PageRank, Triangle Count, Shortest Paths, Connected Components, Strongly Connected Components, Label Propagation

Hands-On:
The Traveling Salesman problem
Minimum Spanning Trees

13. Implementing an End-to-End Project

Getting the right solution based on the criteria curated by SoftPro9 Team