We use cookies, including third-party cookies from Google to serve personalized ads through AdSense, to operate this site and understand how it is used. By continuing to browse, you accept this use. See our Privacy Policy and Terms of Use for details, including how to opt out of personalized advertising.
Accept
SmartData CollectiveSmartData Collective
  • Analytics
    AnalyticsShow More
    What Kind of Problem-Solving Distinguishes Data Analysts From Software Engineers -- AI-generated illustration
    What Kind of Problem-Solving Distinguishes Data Analysts From Software Engineers
    7 Min Read
    chatgpt image jul 21, 2026, 04 34 30 pm
    4 Core Benefits of Predictive Maintenance after Vibration Analysis
    10 Min Read
    How Does Data Mining Boost Customer Satisfaction in Logistics? Harnessing Analytics for Results -- AI-generated illustration
    How Does Data Mining Boost Customer Satisfaction in Logistics? Harnessing Analytics for Results
    11 Min Read
    chatgpt image jul 13, 2026, 04 23 45 pm
    How Data Analytics Helps Companies Improve User Engagement
    19 Min Read
    chatgpt image jul 13, 2026, 03 59 46 pm
    How Data Analytics Improves Multi-Location Search Strategies
    10 Min Read
  • Big Data
  • BI
  • Exclusive
  • IT
  • Marketing
  • Software
Search
© 2008-25 SmartData Collective. All Rights Reserved.
Reading: Web 2.0 Data Scalability
Share
Notification
Font ResizerAa
SmartData CollectiveSmartData Collective
Font ResizerAa
Search
  • About
  • Help
  • Privacy
Follow US
© 2008-23 SmartData Collective. All Rights Reserved.
SmartData Collective > Big Data > Data Mining > Web 2.0 Data Scalability
Business IntelligenceData MiningData WarehousingPredictive Analytics

Web 2.0 Data Scalability

TonyBain
TonyBain
2 Min Read
Web 2.0 Data Scalability
Illustration generated with Qwen Image.
SHARE

Image via Wikipedia

In this previous post I wrote a section on Web 2.0 data platform scalability, in it I mentioned the importance of scale out.  I also mentioned that scale out was important but currently it is hard.  There really aren’t a lot of “elegant” or “natural” ways to scale out a database if you are using any of the mainstream database vendor products (SQL Server, MySQL, Oracle has grids but isn’t shared nothing scale out so is limited).

A question I was asked was “Surely this has all been sorted by now through their widespread use in the enterprise?”.  Good question but unfortunately the answer is no.  The reason behind this is simple.  There are very few enterprise workloads that cannot be satisfied with a scale up (big server) solution.  Maybe 1 in every 20,000 databases will have a workload so great that it can’t be satisfied by a single server, in fact most enterprise databases will happily share servers with other databases.  The biggest ERP’s and online share trading applications that I am aware of all are provided for by a single big server solution.  Quite simply, none of these applications have the workload requirements of tens of thousands of concurrent users that a Twitter or Facebook has.

So this issue really hasn’t been addressed in any adequate way by the mainstream vendors.  Later on I will post on why the current products are not suited for scale out, and the efforts in progress to rectify this.

More Read

How Analytics Can Improve Collaboration Behavior
How Analytics Can Improve Collaboration Behavior
A Billion Dollar Purchase: Why Google Bought Nest
MicroStrategy: The Start of the End? or Just a New Product Cycle?
Powering Personalized Medicine with Hadoop and Apache Spark
Can Predictive Analytics Prevent Tax Evasion?

Innovations in information management

Share This Article
Facebook Pinterest LinkedIn
Share

Follow us on Facebook

Latest News

Flat editorial illustration: The article connects duplicate and decayed CRM records to distorted attribution; a SaaS-editorial sc
Your Attribution Model Is Fine. Your CRM Is Lying to It
Business Intelligence Exclusive
Flat editorial illustration: The article is about replacing informal co-located management with deliberate asynchronous workflows
Managing a Software Development Dedicated Team Across Time Zones
Development Exclusive Software
Technical architecture diagram and decision framework for wikipedia editing services and ai training data: why llms still treat wikipedia as a source of truth
Wikipedia Editing Services and AI Training Data: Why LLMs Still Treat Wikipedia as a Source of Truth
Artificial Intelligence Exclusive
Flat editorial illustration: The article connects fragmented social-service records through shared pathways and selective informa
Turning Disconnected Information Data Into Better Social Service Outcomes
Big Data

Stay Connected

1.2KFollowersLike
33.7KFollowersFollow
222FollowersPin

You Might also Like

Big Data Projects – When You're Not Getting the ROI You Expect
AnalyticsBig DataBusiness IntelligenceCommentaryHadoopITMapReduceRisk Management

Big Data Projects – When You’re Not Getting the ROI You Expect

4 Min Read
Personalizing Learning Experience with AI Voice Over Generator
Artificial Intelligence

Personalizing Learning Experience with AI Voice Over Generator

7 Min Read
Gen AI Helps Developers Automate Writing Coding
Artificial IntelligenceDevelopmentExclusive

Gen AI Helps Developers Automate Writing Coding

9 Min Read
Analyst, Scientist, or Specialist? Choosing Your Data Job Title
Big DataBusiness IntelligenceJobs

Analyst, Scientist, or Specialist? Choosing Your Data Job Title

9 Min Read

SmartData Collective is one of the largest & trusted community covering technical content about Big Data, BI, Cloud, Analytics, Artificial Intelligence, IoT & more.

ai chatbot
How AI Website Chatbots Improve Customer Support and Lead Generation
Chatbots Exclusive
5 Great Tips for Using Data Analytics for Website UX
5 Great Tips for Using Data Analytics for Website UX
Big Data

Quick Link

  • About
  • Contact
  • Privacy
Follow US
© 2008-26 SmartData Collective. All Rights Reserved.
Welcome Back!

Sign in to your account

Username or Email Address
Password

Lost your password?