DZone
Thanks for visiting DZone today,
Edit Profile
  • Manage Email Subscriptions
  • How to Post to DZone
  • Article Submission Guidelines
Sign Out View Profile
  • Post an Article
  • Manage My Drafts
Over 2 million developers have joined DZone.
Log In / Join
Refcards Trend Reports
Events Video Library
Over 2 million developers have joined DZone. Join Today! Thanks for visiting DZone today,
Edit Profile Manage Email Subscriptions Moderation Admin Console How to Post to DZone Article Submission Guidelines
View Profile
Sign Out
Refcards
Trend Reports
Events
View Events Video Library
Zones
Culture and Methodologies Agile Career Development Methodologies Team Management
Data Engineering AI/ML Big Data Data Databases IoT
Software Design and Architecture Cloud Architecture Containers Integration Microservices Performance Security
Coding Frameworks Java JavaScript Languages Tools
Testing, Deployment, and Maintenance Deployment DevOps and CI/CD Maintenance Monitoring and Observability Testing, Tools, and Frameworks
Culture and Methodologies
Agile Career Development Methodologies Team Management
Data Engineering
AI/ML Big Data Data Databases IoT
Software Design and Architecture
Cloud Architecture Containers Integration Microservices Performance Security
Coding
Frameworks Java JavaScript Languages Tools
Testing, Deployment, and Maintenance
Deployment DevOps and CI/CD Maintenance Monitoring and Observability Testing, Tools, and Frameworks

Integrating PostgreSQL Databases with ANF: Join this workshop to learn how to create a PostgreSQL server using Instaclustr’s managed service

Mobile Database Essentials: Assess data needs, storage requirements, and more when leveraging databases for cloud and edge applications.

Monitoring and Observability for LLMs: Datadog and Google Cloud discuss how to achieve optimal AI model performance.

Automated Testing: The latest on architecture, TDD, and the benefits of AI and low-code tools.

Related

  • The Magic of Apache Spark in Java
  • The Complete Apache Spark Collection [Tutorials and Articles]
  • Data Warehouses: The Undying Titans of Information Storage
  • Ethical AI and Responsible Data Science: What Can Developers Do?

Trending

  • Unraveling Lombok's Code Design Pitfalls: Exploring Encapsulation Issues
  • Information Security: AI Security Within the IoT Industry
  • Five Tools for Data Scientists to 10X their Productivity
  • Spring Authentication With MetaMask
  1. DZone
  2. Data Engineering
  3. Big Data
  4. Overcoming the Challenges of Big Data Clustering

Overcoming the Challenges of Big Data Clustering

Clustering has made big data analysis much easier. However, clustering has introduced its own challenges that data engineers must address.

Ryan Kh user avatar by
Ryan Kh
·
Jul. 28, 17 · Opinion
Like (3)
Save
Tweet
Share
7.60K Views

Join the DZone community and get the full member experience.

Join For Free

Data storage used to be the biggest challenge with big data. Due to advances in cloud infrastructures, storing data is no longer a key concern. Today, the accessing data is the biggest concern data scientists face.

Clustering has made big data analysis much easier. However, clustering has introduced its own challenges that data engineers must address.

What Is Data Clustering?

The concept of data clustering goes back at least 20 years. Dr. Anil Kumar Jain, a professor of the Department of Computer Science and Engineering at Ohio State University, provides a great description of the term in one of his white papers:

“Clustering is the unsupervised classification of patterns (observations, data items, or feature vectors) into groups (clusters). The clustering problem has been addressed in many contexts and by researchers in many disciplines; this reflects its broad appeal and usefulness as one of the steps in exploratory data analysis. However, clustering is a difficult problem combinatorially, and differences in assumptions and contexts in different communities have made the transfer of useful generic concepts and methodologies slow to occur.”

In other words, data engineers use clustering to identify trends and patterns in raw data. They need to break it down and categorize it into clusters.

What Are the Primary Challenges With Data Clustering?

Clustering has been a challenge since the concept of big data was born. The problem stems from the volume of data and processing limitations. The University of Rabat listed the following as the top concerns with big data clustering.

Volume

The amount of data stored on most network is growing exponentially. As the volume of data grows, it becomes more difficult to extract it. According to Nakivo Research, backing up data as part of your disaster recovery plan can also amplify these problems.

Velocity

The speed at which data is generated is another clustering challenge data scientists face. This problem isn’t limited to the volume of data on a network. As networks generate new data at unprecedented speeds, they will have a harder time extracting it in real-time.

The problem this creates is two-fold:

  • New patterns will be constantly emerging from known data sets. Data analysts may feel they are having difficulty drawing accurate conclusions from data when in actuality their analyses are a better representative of the problem they are modeling. They may not know when to analyze their existing data sets and when to wait for more data to be collected.
  • If data is created faster than it can be extracted, trends may change as they try to collect it.

The problem will grow as networks use the Internet of Things (IoT) to collect data from more devices and they can collect data at quicker speeds.

Variety

Clustered data is stored in many different forms, which can make it difficult to make accurate comparisons. Some data is stored in structured formats, while other data sets are completely unstructured.

How Can These Problems Be Addressed?

There are a variety of tools and strategies that simplify the process of extracting and analyzing clustered data.

K-Means Clustering

The k-means clustering approach is a portioning-based solution that requires networks to assign objects to one and only one cluster. This eliminates the concern that a single object may bias analysis by appearing in multiple data sets.

Unsupervised Classification Algorithms

Unsupervised classification algorithms are data mining tools that consolidate very large data sets based on predefined parameters. This is a good solution for dealing with growing data volumes, especially with robust Hadoop tools.

COALA

COALA uses instance-level constraints to avoid the problems that arise from similar grouping. The constraints don’t need to be met with 100% satisfaction.

Dimension Reduction

Every data has two dimensions:

  1. Variables.
  2. Examples.

As the number of variables increases, total data volume increases exponentially. The problem can be mitigated by using dimension reduction strategies (otherwise referred to as a dimensionality reducing transformation).

Identify the Novel Solutions to Data Clustering Challenges

Data clustering is a solution to many of the problems wrought by storing high volumes of structured and structured data. However, it isn’t an infallible solution because data still needs to be accessed and analyzed as quickly and accurately as possible. Fortunately, there are a number of great tools and approaches that simplify the process.

Big data Data science clustering

Published at DZone with permission of Ryan Kh. See the original article here.

Opinions expressed by DZone contributors are their own.

Related

  • The Magic of Apache Spark in Java
  • The Complete Apache Spark Collection [Tutorials and Articles]
  • Data Warehouses: The Undying Titans of Information Storage
  • Ethical AI and Responsible Data Science: What Can Developers Do?

Comments

Partner Resources

X

ABOUT US

  • About DZone
  • Send feedback
  • Careers
  • Sitemap

ADVERTISE

  • Advertise with DZone

CONTRIBUTE ON DZONE

  • Article Submission Guidelines
  • Become a Contributor
  • Visit the Writers' Zone

LEGAL

  • Terms of Service
  • Privacy Policy

CONTACT US

  • 3343 Perimeter Hill Drive
  • Suite 100
  • Nashville, TN 37211
  • support@dzone.com

Let's be friends: