Book Image

Learning Hunk

By : Dmitry Anoshin, Sergey Sheypak
Book Image

Learning Hunk

By: Dmitry Anoshin, Sergey Sheypak

Overview of this book

Hunk is the big data analytics platform that lets you rapidly explore, analyse, and visualize data in Hadoop and NoSQL data stores. It provides a single, fluid user experience, designed to show you insights from your big data without the need for specialized skills, fixed schemas, or months of development. Hunk goes beyond typical data analysis methods and gives you the power to rapidly detect patterns and find anomalies across petabytes of raw data. This book focuses on exploring, analysing, and visualizing big data in Hadoop and NoSQL data stores with this powerful full-featured big data analytics platform. You will begin by learning the Hunk architecture and Hunk Virtual Index before moving on to how to easily analyze and visualize data using Splunk Search Language (SPL). Next you will meet Hunk Apps which can easy integrate with NoSQL data stores such as MongoDB or Sqqrl. You will also discover Hunk knowledge objects, build a semantic layer on top of Hadoop, and explore data using the friendly user-interface of Hunk Pivot. You will connect MongoDB and explore data in the data store. Finally, you will go through report acceleration techniques and analyze data in the AWS Cloud.

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Learning Hunk
Dmitry Anoshin | Sergey Sheypak
Dec, 2015 | 156 pages
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Table of Contents (14 chapters)
Learning Hunk
Learning Hunk
Credits
Credits
About the Authors
About the Authors
About the Reviewer
About the Reviewer
www.PacktPub.com
www.PacktPub.com
Preface
Preface
Free Chapter
1
Meet Hunk
Meet Hunk
Big data analytics
The big problem
The elegant solution
Getting to know Hunk
Hunk architecture
Setting up Hadoop
Starting the VM and cluster in VirtualBox
Big data use case
Summary
2
Explore Hadoop Data with Hunk
Explore Hadoop Data with Hunk
Setting up Hunk
Exploring data
Controlling security with Hunk
Summary
3
Meeting Hunk Features
Meeting Hunk Features
Knowledge objects
Introducing Pivot
Summary
4
Adding Speed to Reports
Adding Speed to Reports
Big data performance issues
Hunk report acceleration
Hunk accelerations limits
Summary
5
Customizing Hunk
Customizing Hunk
What we are going to do with the Splunk SDK
Dashboard customization using Splunk Web Framework
A description of time-series aggregated CDR data
Implementation
Custom map components
The final result
Summary
6
Discovering Hunk Integration Apps
Discovering Hunk Integration Apps
What is Mongo?
Counting by shop in a single collection
Counting events in all collections
Summary
7
Exploring Data in the Cloud
Exploring Data in the Cloud
An introduction to Amazon EMR and S3
Integrating Hunk with EMR and S3
Converting Hunk from an hourly rate to a license
Summary
Index
Index

Implementation

Now it's time to implement heatmap application. We will start from creating query to get sample data for application and then move forward to coding visualization using Javascript and Python.

Querying the visualization

Let us start to get acquainted with the data we have. We are going to explore sample data to make the process faster.

We are going to use the next query during development. We will display a heatmap for the center of Milano. The other simplification is a hardcoded time interval. We removed all other intervals from the sample dataset using the Pig script earlier. The general idea is to reduce the amount of data and make the development cycle shorter:

(index="milano_cdr_sample" time_interval=1385884800000 
AND (
 (square_id >5540 AND square_id < 5560) OR 
 (square_id >5640 AND square_id < 5660) OR 
 (square_id >5740 AND square_id < 5760) 
 )
)

| fields square_id, sms_in, time_interval 

| stats sum(sms_in) as cdrActivityValue by square_id, time_interval...
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