Solving 10 Hadoop'able Problems

Solving 10 Hadoop'able Problems, available at $19.99, has an average rating of 3.67, with 40 lectures, based on 3 reviews, and has 91 subscribers.

You will learn about Explore the Hadoop big data Ecosystem in a nutshell Process payment data from an event stream using the streaming API: Payment Analyzer Detect BOT traffic using Spark Streaming, make log data queryable, and investigate customer data Supply Chain analysis – find top-seller items in a streaming way, enhance top-seller items Analyze Customer churn amounts quantitatively with DataFrame queries Perform IoT sensor data analysis with device response to system failures and data streams High-performance computation with neighborhood aggregations Page ranking using Spark GraphX Threat Analysis – Analyzing weblogs for suspicious activity and anomalies in network traffic Extract information from unstructured text via Spark DataFrames Perform sentiment analysis of posts using Logistic Regression, and find the author of a post Find what product users want to buy using Cloudera Sandbox Toolkit Use movie history to suggest content, and test and experiment with Recommendation Enginec This course is ideal for individuals who are Data Engineers, and Machine Learning and Data analysts It is particularly useful for Data Engineers, and Machine Learning and Data analysts.

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Summary

Title: Solving 10 Hadoop'able Problems

Price: $19.99

Average Rating: 3.67

Number of Lectures: 40

Number of Published Lectures: 40

Number of Curriculum Items: 40

Number of Published Curriculum Objects: 40

Original Price: $109.99

Quality Status: approved

Status: Live

What You Will Learn

Explore the Hadoop big data Ecosystem in a nutshell
Process payment data from an event stream using the streaming API: Payment Analyzer
Detect BOT traffic using Spark Streaming, make log data queryable, and investigate customer data
Supply Chain analysis – find top-seller items in a streaming way, enhance top-seller items
Analyze Customer churn amounts quantitatively with DataFrame queries
Perform IoT sensor data analysis with device response to system failures and data streams
High-performance computation with neighborhood aggregations
Page ranking using Spark GraphX
Threat Analysis – Analyzing weblogs for suspicious activity and anomalies in network traffic
Extract information from unstructured text via Spark DataFrames
Perform sentiment analysis of posts using Logistic Regression, and find the author of a post
Find what product users want to buy using Cloudera Sandbox Toolkit
Use movie history to suggest content, and test and experiment with Recommendation Enginec

Who Should Attend

Data Engineers, and Machine Learning and Data analysts

Target Audiences

Data Engineers, and Machine Learning and Data analysts

The Apache Hadoop ecosystem is a popular and powerful tool to solve big data problems. With so many competing tools to process data, many users want to know which particular problems are well suited to Hadoop, and how to implement those solutions.

To know what types of problems are Hadoop-able it is good to start with a basic understanding of the core components of Hadoop. You will learn about the ecosystem designed to run on top of Hadoop as well as software that is deployed alongside it. These tools give us the building blocks to build data processing applications. This course covers the core parts of the Hadoop ecosystem, helping to give a broad understanding and get you up-and-running fast. Next, it describes a number of common problems as case-study projects Hadoop is able to solve. These sections are broken down into sections by different projects, each serving as a specific use case for solving big data problems.

By the end of this course, you will have been exposed to a wide variety of Hadoop software and examples of how it is used to solve common big data problems.

About the Author

Tomasz Lelek is a Software Engineer who programs mostly in Java and Scala. He is a fan of microservice architectures and functional programming. He dedicates considerable time and effort to be better every day. Recently, he’s been delving into big data technologies such as Apache Spark and Hadoop. He is passionate about nearly everything associated with software development.

Tomasz thinks that we should always try to consider different solutions and approaches to solving a problem. Recently, he was a speaker at several conferences in Poland – Confitura and JDD (Java Developer’s Day) and also at Krakow Scala User Group.

He also conducted a live coding session at Geecon Conference.

Course Curriculum

Chapter 1: Core Components

Lecture 1: The Course Overview

Lecture 2: Hadoop Distributed File System (HDFS)

Lecture 3: Distributed Compute Capability YARN

Chapter 2: Downstream Ecosystem

Lecture 1: Apache Hive for ETL and SQL Like

Lecture 2: Message Queuing and Data Ingestion Kafka

Lecture 3: NoSQL Datastores – Hadoop HBase, Accumulo

Lecture 4: Machine Learning – Spark and Spark MLlib

Lecture 5: Stream Processing – Spark Streaming

Chapter 3: Financial, Trade, and Time Series Applications – Trade Surveillance

Lecture 1: Processing Payment Data from an Event Stream

Lecture 2: Advanced Aggregations Using Streaming API – PaymentAnalyzer

Lecture 3: Storing Time Series Data in HBase

Chapter 4: AdTech – Ad Targeting

Lecture 1: Detecting BOT Traffic Using Spark Streaming

Lecture 2: Make Web Log Data Queryable – Hive Sink

Lecture 3: Investigating Customers Data in Hive

Chapter 5: Business/Point of Sale – Transaction Analysis

Lecture 1: Trending Supply Chain – Finding Top Seller Item in a Streaming Way

Lecture 2: Enriching Top Sellers with Additional Information

Chapter 6: Customer Churn Analysis

Lecture 1: Analyzing Customer Churn (Quantitative) Using DataFrame Queries

Lecture 2: Analyzing Customer Churn (Amounts) Using DataFrame Queries

Chapter 7: Internet of Things

Lecture 1: Storing Low Granularity Structured Sensor Data in HBase

Lecture 2: Consuming Sensor Data Stored in HBase – Scan and Count

Lecture 3: Building Summaries on Data Streaming from Devices

Chapter 8: Scientific and High Performance Computing

Lecture 1: Introducing Spark GraphX – How to Represent a Graph?

Lecture 2: Perform Graph Operations Using GraphX

Lecture 3: Counting Degree of Vertices

Lecture 4: Neighborhood Aggregations – Collecting Neighbors

Lecture 5: Structural Operators – Connected Components

Lecture 6: Page Rank Using Spark GraphX

Chapter 9: Security Concerns Intrusion Detection – Threat Analysis

Lecture 1: Anomaly Detection

Lecture 2: Analyzing Web Logs for Suspicious Activity and Loading into Spark

Lecture 3: Implementing Clustering – Choosing Number of Clusters

Lecture 4: Detecting Anomalies in Network Traffic

Chapter 10: Text Analysis

Lecture 1: Analyzing Post for an Author

Lecture 2: Extracting Information from Unstructured Text

Lecture 3: Extracting Information Via Spark DataFrame

Lecture 4: Sentiment Analysis of Posts Using Logistic Regression

Lecture 5: Finding an Author of a Post

Chapter 11: Data Warehouse/Data Lake/ Data Sandbox

Lecture 1: Downloading and Setting Cloudera Sandbox

Lecture 2: Finding What Products Users Wants to Buy Using Cloudera Sandbox Toolkit

Chapter 12: Personalization

Lecture 1: Using Movies History to Suggest Interesting Content

Lecture 2: Testing and Experimenting with Recommendation Engine

Instructors

Packt Publishing
Tech Knowledge in Motion

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5 stars: 1 votes

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