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You're reading from Learning Hunk

Product type Book

Published in Dec 2015

Publisher

ISBN-13 9781782174820

Pages 156 pages

Edition 1st Edition

Languages

Java

Concepts

Data Analysis

Authors (2):

Dmitry Anoshin

Sergey Sheypak

View More author details

Table of Contents (14) Chapters

Learning Hunk

Credits

About the Authors

About the Reviewer

www.PacktPub.com

Preface

Meet Hunk

Explore Hadoop Data with Hunk

Meeting Hunk Features

Adding Speed to Reports

Customizing Hunk

Discovering Hunk Integration Apps

Exploring Data in the Cloud

Index

Chapter 3. Meeting Hunk Features

Big data analytics is a very popular trend. As a result, most business users want to discover their big data using intuitive and user-friendly tools because exploring data stored in Hadoop or any NoSQL data stores is a challenging task. Fortunately, Hunk does away with all the complexity obstructing analysts and business users. Moreover, it gives additional features that allow us to handle big data in just several mouse clicks. This is possible with Hunk knowledge objects.

In the previous chapter, we created virtual indexes based on web logs for the international fashion retailer Unicorn Fashion. We created some queries and reports via Search Processing Language (SPL). Moreover, we created a web operation dashboard and learnt how to create alerts.

In this chapter, we will explore Hunk knowledge objects, which will help us to achieve better results with less effort. Moreover, we will become familiar with pivots and data models, in order to learn how to work...

Knowledge objects

Hunk has the same capabilities as Splunk; as a result we can create various knowledge objects that can help us explore big data and make it more user-friendly.

Tip

A knowledge object is a configuration within Hunk that uses permissions and is controlled via the Hunk access control layer. Knowledge objects can be scoped to specific applications. Read/write permissions for them are granted to roles.

To work with knowledge objects, go to the KNOWLEDGE menu under Settings:

There are various knowledge objects available in Hunk. We encountered SPL, reports, dashboards, and alerts in the previous chapter. Let's expand our knowledge of Hunk and explore additional knowledge objects.

Tip

For more information about knowledge objects, see: http://docs.splunk.com/Documentation/Splunk/latest/Knowledge/WhatisSplunkknowledge.

Field aliases

Field aliases help us to normalize data over several sources. We can create multiple aliases in one field.

Tip

Aliases should be applied after field extraction...

Introducing Pivot

A data model is a semantic layer that makes Hunk a powerful analytical tool for business users. Pivot is a user-friendly interface where anyone can create complex multidimensional reports or interactive dashboards that can be distributed across colleagues.

Let's explore the Pivot UI. Click on Pivot, select the Unicorn Fashion Digital Analytics data model, and Pivot UI will come up:

Pivot UI consists of the following main elements:

Summary

In this chapter, we learnt about extended Hunk features such as knowledge objects. They give additional power to Hunk users during big data analytics. We learnt how to create a semantic layer and work with Pivot. In addition, we explored field aliases and learnt how to extract new fields or calculate them.

In the next chapter, we are going to explore Hunk's report acceleration feature, which can be very useful for getting business insights from big data very quickly.

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Authors (2)

Dmitry Anoshin

Dmitry Anoshin is a data-centric technologist and a recognized expert in building and implementing big data and analytics solutions. He has a successful track record when it comes to implementing business and digital intelligence projects in numerous industries, including retail, finance, marketing, and e-commerce. Dmitry possesses in-depth knowledge of digital/business intelligence, ETL, data warehousing, and big data technologies. He has extensive experience in the data integration process and is proficient in using various data warehousing methodologies. Dmitry has constantly exceeded project expectations when he has worked in the financial, machine tool, and retail industries. He has completed a number of multinational full BI/DI solution life cycle implementation projects. With expertise in data modeling, Dmitry also has a background and business experience in multiple relation databases, OLAP systems, and NoSQL databases. He is also an active speaker at data conferences and helps people to adopt cloud analytics.

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Sergey Sheypak

Sergey Sheypak started his so-called big data practice in 2010 as a Teradata PS consultant. His was leading the Teradata Master Data Management deployment in Sberbank, Russia (which has 110 billion customers). Later Sergey switched to AsterData and Hadoop practices. Sergey joined the Research and Development team at MegaFon (one of the top three telecom companies in Russia with 70 billion customers) in 2012. While leading the Hadoop team at MegaFon, Sergey built ETL processes from existing Oracle DWH to HDFS. Automated end-to-end tests and acceptance tests were introduced as a mandatory part of the Hadoop development process. Scoring geospatial analysis systems based on specific telecom data were developed and launched. Now, Sergey works as independent consultant in Sweden.

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Pivot element	Definition
Filters (1)	This is used to cut down the result count for the object. There are restrictions in addition to those that might be applied via constraints or other means in the object's definition. All Pivots are filtered by time range. We can optionally add one or more filters by attribute.
Split Rows (2)	This splits out the Pivot results by row. For example, we could use this element to configure a page view object to display a row for each month of the past year, thus breaking out the page view count by month.
...