You're reading from Modern Big Data Processing with Hadoop

Product typeBook

Published inMar 2018

Reading LevelIntermediate

PublisherPackt

ISBN-139781787122765

Edition1st Edition

Languages

Scala

Tools

Hadoop Apache Spark

Concepts

Big Data

Authors (3):

V Naresh Kumar

Manoj R Patil

Prashant Shindgikar

View More author details

Designing Data Visualization Solutions

Once we have the data living in the Hadoop ecosystem and it's been processed, the next logical step is to build the analytics that drive the business decisions.

In this chapter, we will learn the following topics:

Data visualization
Apache Druid
Apache Superset

Data visualization

Data visualization is the process of understanding the relationships between various entities in the raw data via graphical means. This is a very powerful technique because it enables end users to get the message in a very easy form without even knowing anything about the underlying data.

Data visualization plays a very important role in visual communication of insights from big data. Its both an art and a science, and requires some effort in terms of understanding the data; at the same time we need some understanding of the target audience as well.

So far, we have seen that any type of data can be stored in the Hadoop filesystem (HDFS). In order to convert complex data structures into a visual form, we need to understand the standard techniques that are used to represent the data.

In data visualization, the message is conveyed to the end users in the form of...

Practical data visualization in Hadoop

Hadoop has a rich ecosystem of data sources and applications that help us build rich visualizations. In the coming sections, we will understand two such applications:

Apache Druid
Apache Superset

We will also learn how to use Apache Superset with data in RDBMSes such as MySQL.

Apache Druid

Apache Druid is a distributed, high-performance columnar store. Its official website is https://druid.io.

Druid allows us to store both real-time and historical data that is time series in nature. It also provides fast data aggregation and flexible data exploration. The architecture supports storing trillions of data points on petabyte sizes.

In order to understand more about the Druid architecture...

Summary

In this chapter, we learned about data visualization and how it helps the users to receive the required message without any knowledge of the underlying data. We then saw the different ways to visualize our data graphically.

We walked through Hadoop applications such as Apache Druid and Apache Superset that are used to visualize data and learned how to use them with RDBMses such as MySQL. We also saw a sample database to help us understand the application better.

In the next chapter, we will learn how to build our Hadoop cluster on the cloud.

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Authors (3)

V Naresh Kumar

Naresh has more than a decade of professional experience in designing, implementing and running very large scale Internet Applications in Fortune Top 500 Companies. He is a Full Stack Architect with hands-on experience in domains like E-commerce, Web-hosting, Healthcare, Bigdata & Analytics, Data Streaming, Advertising and Databases. He believes in Opensource and contributes to them actively. He keeps himself up-to-date with emerging technologies starting from Linux Systems Internals to Frontend technologies. He studied in BITS-Pilani, Rajasthan with Dual Degree in Computer Science & Economics.
Read more about V Naresh Kumar

Manoj R Patil

Manoj R Patil is the Chief Architect in Big Data at Compassites Software Solutions Pvt. Ltd. where he overlooks the overall platform architecture related to Big Data solutions, and he also has a hands-on contribution to some assignments. He has been working in the IT industry for the last 15 years. He started as a programmer and, on the way, acquired skills in architecting and designing solutions, managing projects keeping each stakeholder's interest in mind, and deploying and maintaining the solution on a cloud infrastructure. He has been working on the Pentaho-related stack for the last 5 years, providing solutions while working with employers and as a freelancer as well. Manoj has extensive experience in JavaEE, MySQL, various frameworks, and Business Intelligence, and is keen to pursue his interest in predictive analysis. He was also associated with TalentBeat, Inc. and Persistent Systems, and implemented interesting solutions in logistics, data masking, and data-intensive life sciences.
Read more about Manoj R Patil

Prashant Shindgikar

Prashant Shindgikar is an accomplished big data Architect with over 20 years of experience in data analytics. He specializes in data innovation and resolving data challenges for major retail brands. He is a hands-on architect having an innovative approach to solving data problems. He provides thought leadership and pursues strategies for engagements with the senior executives on innovation in data processing and analytics. He presently works for a large USA-based retail company.
Read more about Prashant Shindgikar

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