•Spark Analytics for RealTime Data Processing
•The course overview
•Spark SQL Introduction
•Spark SQL Core Abstractions
•Creating DataFrames from RDD
•Creating DataFrames from Files
•Creating DataFrames from Data Sources
•DataFrame API Common Operations
•DataFrame API Query Operations
•DataFrame API Actions
•DataFrame API BuiltIn Functions
•Spark Streaming Introduction
•Spark Streaming Quick Example
•Spark Streaming Architecture
•Spark Streaming Transformations
•Spark Streaming Input Sources
•Spark Streaming Performance Considerations
•Best Practices for High Velocity Streams
•Best Practices for External Data Sources
•Design Patterns
•Test Your Knowledge
•Advanced Analytics and RealTime Data Processing in Apache Spark
•Introducing Spark Streaming
•Streaming Context
•Processing Streaming Data
•Use Cases
•Spark Streaming Word Count HandsOn
•Spark Streaming Understanding Master URL
•Integrating Spark Streaming with Apache Kafka
•mapWithState Operation
•Transform and Window Operation
•Join and Output Operations
•Output Operations Saving Results to Kafka Sink
•Handling Time in High Velocity Streams
•Connecting External Systems That Works in At Least Once Guarantee Deduplicaion
•Building Streaming Application Handling Events That Are Not in Order
•Filtering Bots from Stream of Page View Events
•Introducing Machine Learning with Spark
•Feature Extraction and Transformation
•Transforming Text into Vector of Numbers ML BagofWords Technique
•Logistic Regression
•Model Evaluation
•Clustering
•Gaussian Mixture Models
•Principal Component Analysis and Distributing the Singular Value Decomposition
•Collaborative Filtering Building Recommendation Engine
•Introducing Spark GraphXHow to Represent a Graph
•Limitations of GraphParallel System Why Spark GraphX
•Importing GraphX
•Create a Graph Using GraphX and Property Graph
•List of Operators
•Perform Graph Operations Using GraphX
•Triplet View
•Perform Subgraph Operations
•Neighbourhood Aggregations Collecting Neighbours
•Counting Degree of Vertex
•Caching and Uncaching
•GraphBuilder
•Vertex and Edge RDD
•Structural Operators Connected Components
•Introduction to SparkR and How Its Used
•Setting Up from RStudio
•Creating Spark DataFrames from Data Sources
•SparkDataFrames Operations Grouping Aggregation
•Run a Given Function on a Large Dataset Using dapply or dapplyCollect
•Running Large Dataset by Input Columns and Using gapply or gapplyCollect
•Run Local R Functions Distributed Using sparklapply
•Running SQL Queries from SparkR
•PageRank Using Spark GraphX
•Sending RealTime Notification to User on an ECommerce site
•Big Data Analytics Projects with Apache Spark
•Explaining Ways of Joining Datasets
•Developing Spark Algorithm for JoiningWindowing Datasets
•Testing Logic in MapReduce Spark Finding Top Sellers
•Drawing Conclusions from Top Sellers Data
•Market Basket Analysis Goals
•Where MBA Algorithms Are Useful
•Implementing MBA MapReduce Algorithm in Spark
•Finding Association Rules Between Products
•Analyzing Post for an Author
•Extracting Information from Unstructured Text
•Extracting Information via Spark DataFrame
•Sentiment Analysis of Posts Using Logistic Regression
•Finding an Author of a Post
•ContentBased Recommendation Systems Explanation
•Finding Correlation Between Movies and Users
•Testing Logic in MapReduce Spark
•Finding Recommendation for Given User
•Finding Common Friends Problem Graph Approach
•Creating a Graph Using GraphX and Property Graph
•Solution Examining Available Methods
•Spark Analytics for Real-Time Data Processing
•Spark SQL – Core Abstractions
•DataFrame API – Common Operations
•DataFrame API – Query Operations
•DataFrame API – Actions
•DataFrame API – Built-In Functions
•Spark Streaming – Introduction
•Spark Streaming – Quick Example
•Spark Streaming – Architecture
•Spark Streaming – Transformations
•Spark Streaming – Input Sources
•Spark Streaming – Performance Considerations
•Advanced Analytics and Real-Time Data Processing in Apache Spark
•Spark Streaming Word Count Hands-On
•Spark Streaming – Understanding Master URL
•Output Operations –Saving Results to Kafka Sink
•Connecting External Systems That Works in At Least Once Guarantee – Deduplicaion
•Building Streaming Application –Handling Events That Are Not in Order
•Transforming Text into Vector of Numbers – ML Bag-of-Words Technique
•Collaborative Filtering – Building Recommendation Engine
•Introducing Spark GraphX–How to Represent a Graph?
•Limitations of Graph-Parallel System – Why Spark GraphX?
•Neighbourhood Aggregations – Collecting Neighbours
•Structural Operators – Connected Components
•Introduction to SparkR and How It’s Used?
•SparkDataFrames Operations – Grouping, Aggregation
•Running Large Dataset by Input Column(s) and Using gapply or gapplyCollect
•Run Local R Functions Distributed Using spark.lapply
•Sending Real-Time Notification to User on an E-Commerce site
•Developing Spark Algorithm for Joining/Windowing Datasets
•Testing Logic in MapReduce Spark — Finding Top Sellers
•Where MBA Algorithms Are Useful?
•Content-Based Recommendation Systems Explanation
•Finding Common Friends Problem — Graph Approach
•Solution — Examining Available Methods
•Finding Closest Friend for Given User Using Page Rank
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