Python Spark using PySpark Certification Training in Mexico City

• 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

• 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 your first PySpark Job Using Jupyter Notebook
• 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
• 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 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)

• 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

• Project 1- Domain: Finance

• Statement: A leading financial bank is trying to broaden the financial inclusion for the unbanked population by providing a positive and safe borrowing experience. In order to make sure this underserved population has a positive loan experience, it makes use of a variety of alternative data including telco and transactional information to predict their client's repayment abilities. The bank has asked you to develop a solution to ensure that clients capable of repayment are not rejected and that loans are given with a principal, maturity, and repayment calendar that will empower their clients to be successful.

• Project 2- Domain: Media and Entertainment 

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

In this module, you will be learning the key concepts of Spark GraphX programming concepts and operations along with different GraphX algorithms and their implementations. 

Topics:

• 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