DevOps.com

  • Latest
    • Articles
    • Features
    • Most Read
    • News
    • News Releases
  • Topics
    • AI
    • Continuous Delivery
    • Continuous Testing
    • Cloud
    • Culture
    • DevSecOps
    • Enterprise DevOps
    • Leadership Suite
    • DevOps Practice
    • ROELBOB
    • DevOps Toolbox
    • IT as Code
  • Videos/Podcasts
    • DevOps Chats
    • DevOps Unbound
  • Webinars
    • Upcoming
    • On-Demand Webinars
  • Library
  • Events
    • Upcoming Events
    • On-Demand Events
  • Sponsored Communities
    • AWS Community Hub
    • CloudBees
    • IT as Code
    • Rocket on DevOps.com
    • Traceable on DevOps.com
    • Quali on DevOps.com
  • Related Sites
    • Techstrong Group
    • Container Journal
    • Security Boulevard
    • Techstrong Research
    • DevOps Chat
    • DevOps Dozen
    • DevOps TV
    • Digital Anarchist
  • Media Kit
  • About
  • AI
  • Cloud
  • Continuous Delivery
  • Continuous Testing
  • DevSecOps
  • Leadership Suite
  • Practices
  • ROELBOB
  • Low-Code/No-Code
  • IT as Code
  • More
    • Application Performance Management/Monitoring
    • Culture
    • Enterprise DevOps

Home » Blogs » Don’t Be Samsung: Here’s How You Can Protect Yourself

Don’t Be Samsung: Here’s How You Can Protect Yourself

By: Brian Yin on November 3, 2016 Leave a Comment

Imagine a factory building 100,000 units a day, and you can see how a small error can have huge consequences. Now imagine that factory is a database.

Recent Posts By Brian Yin
  • Protecting Data in Public Cloud and Hybrid Cloud
  • PyMongo Pointers: How to Make Robust and Highly Available Mongo Queries
More from Brian Yin
Related Posts
  • Don’t Be Samsung: Here’s How You Can Protect Yourself
  • Jamstack Will Revolutionize How Websites Are Built
  • Why Data Lineage Matters and Why it’s so Challenging
    Related Categories
  • Blogs
  • Doin' DevOps
    Related Topics
  • big data
  • business continuity
  • data protection
  • devops
  • disaster recovery
  • distributed database
  • errors
  • point-in-time recover
  • relational database
  • samsung
Show more
Show less

The world’s top smartphone maker this month pulled the plug on its $882 Galaxy Note 7 after phones overheated and caught fire, concluding in a disastrous recall for Samsung that could cost the company as much as $17 billion and shutter a global revenue stream. In an unprecedented move, Samsung has told mobile carriers to halt sales or exchanges of the device, requested that users to shut off their phones and issued a mass recall while the South Korean manufacturer investigates reports of fires in original and replacement Note 7s.

DevOps Connect:DevSecOps @ RSAC 2022

This a scant two months after the Galaxy Note 7’s launch, a device that one TechCrunch consumer electronics expert raved as “undoubtedly one of the most iconic handsets around.” While it is not clear what caused these fires, there is speculation that it was due to a faulty component that interacts with the battery, rather than the battery itself.

It goes without saying that there are ample opportunities for error in the assembly of a smartphone. The complicated, highly coordinated process is fraught with thousands of input touchpoints, from hard- and software components to tightly controlled manufacturing processes to manual labor. And though there may be countless checks and balances in place, mistakes do happen. What may seem to be an innocuous error, such as not sufficiently tightening a screw or the shorting of a circuit, in this case has led to epic consequences.

The truth is, these instances happen more often than we think, and to technologies even more vital to the population than a personal smartphone.

Consider today’s era of big data and cloud applications, where enterprise applications ingest and process vast amounts of information from various sources, often in real time, to deliver critical analytics, contextual information or business insights.

This data has weight. It can influence whether fleets of airplanes are grounded at gates all across the country. It can restrict customers from accessing funds from online banking. It can dictate whether millions of online retail dollars go unspent. The reliance on accurate data, and its grip on lives and business, is growing at an exponential rate—like ever-widening ripples in a lake.

To support all of this data, new applications (such as real-time analytics, internet of things, ecommerce and others) are developed and deployed on scale-out, non-relational databases or ported from traditional relational databases.

But, like the manufacturing gaffs experienced by Samsung, corruptions and operational errors by developers occur in enterprise IT environments. Enterprises know this, and billions have been invested in traditional backup and recovery and data protection products to protect against such “err” moments. However, the irony is that data protection products like those that exist for traditional relational databases do not exist for today’s modern, next-generation scale-out databases supplying the new types of data that increasingly control our lives. We’re essentially trusting that the Ma Bell rotary phone repairman will be able to recover personal data—such as contacts, photos and messages—after your Galaxy Note 7 starts smoldering.

So how did we get from the world’s most explosive phablet to distributed databases and big data? Because, like the Galaxy Note 7, the tiniest data error can rapidly expand into magnificent, catastrophic consequences.

Enterprises are adopting cloud applications that gather large amounts of data at a high-ingestion rate and process that data in real-time to deliver actionable insights. These applications are deployed on non-relational databases. As migration to cloud and multi-cloud application environments increases, enterprises also must be able to manage and recover at scale without data loss or corruption and with minimal application downtime.

However, enterprises today face a significant gap in addressing their data protection requirements. Their most valuable data, often needed 24/7 and without fail, runs absent any point-in-time backup options for that ‘“Houston, we have a problem”’ moment. Most studies suggest that more than half of data downtime and loss is triggered by human error, corruption or virus attacks. And yes, while native replication helps enterprises meet business continuity and disaster recovery requirements by providing protection from hardware failures or even natural disasters, it is insufficient (meaning it cannot scale) to meet next-gen enterprise data protection demands. Case in point: If a minor schema corruption impacts the primary data copy, it causes a multitudinous ripple effect that impacts all replicas down the line. Yet, even without protection, industries continue to adopt and deploy scale-out database systems at an accelerated pace.

So just as Samsung witnessed an error replicate itself into such a massive meltdown of global proportions that it threatens to kill an entire product line, organizations also must understand how replication-based protection mechanism can actually act like a virus—spreading a corruption uncontrollably across non-relational, cloud databases.

Samsung’s troubles won’t end with the simple recall of a single product, and their predicament should resonate. There will be punitive measures, and perhaps even a total enterprise shift from the smartphone market. It is not an enviable position, but is does offer a clear example of how a peripheral manufacturing error can swell to engulf an entire brand.

For enterprises rushing to deploy scale-out databases, let Samsung serve as a warning: that deploying any infrastructure that could potentially lead to exponential, irreversible corruption of all data copies is a recipe for harm (if not disaster). As enterprises make the switch to next-gen applications running on scale-out, non-relational databases, an assembly line e-stop needs to be built in, in the form of a point-in-time, version-friendly data protection solution.

Data corruption is not a matter of “if,” but “when.” So have a kill switch ready.

errors-replicating-image

— Brian Yin

Filed Under: Blogs, Doin' DevOps Tagged With: big data, business continuity, data protection, devops, disaster recovery, distributed database, errors, point-in-time recover, relational database, samsung

Sponsored Content
Featured eBook
Hybrid Cloud Security 101

Hybrid Cloud Security 101

No matter where you are in your hybrid cloud journey, security is a big concern. Hybrid cloud security vulnerabilities typically take the form of loss of resource oversight and control, including unsanctioned public cloud use, lack of visibility into resources, inadequate change control, poor configuration management, and ineffective access controls ... Read More
« What the Current DDoS Landscape Means for DevOps
Trace3 Named Western U.S. Partner of the Year by Cisco »

TechStrong TV – Live

Click full-screen to enable volume control
Watch latest episodes and shows

Upcoming Webinars

Continuous Deployment
Monday, July 11, 2022 - 1:00 pm EDT
Using External Tables to Store and Query Data on MinIO With SQL Server 2022
Tuesday, July 12, 2022 - 11:00 am EDT
Goldilocks and the 3 Levels of Cardinality: Getting it Just Right
Tuesday, July 12, 2022 - 1:00 pm EDT

Latest from DevOps.com

Rust in Linux 5.20 | Deepfake Hiring Fraud | IBM WFH ‘New Normal’
June 30, 2022 | Richi Jennings
Moving From Lift-and-Shift to Cloud-Native
June 30, 2022 | Alexander Gallagher
The Two Types of Code Vulnerabilities
June 30, 2022 | Casey Bisson
Common RDS Misconfigurations DevSecOps Teams Should Know
June 29, 2022 | Gad Rosenthal
Quick! Define DevSecOps: Let’s Call it Development Security
June 29, 2022 | Don Macvittie

Get The Top Stories of the Week

  • View DevOps.com Privacy Policy
  • This field is for validation purposes and should be left unchanged.

Download Free eBook

The State of Open Source Vulnerabilities 2020
The State of Open Source Vulnerabilities 2020

Most Read on DevOps.com

What Is User Acceptance Testing and Why Is it so Important?
June 27, 2022 | Ron Stefanski
Rust in Linux 5.20 | Deepfake Hiring Fraud | IBM WFH ‘New No...
June 30, 2022 | Richi Jennings
Chip-to-Cloud IoT: A Step Toward Web3
June 28, 2022 | Nahla Davies
DevOps Connect: DevSecOps — Building a Modern Cybersecurity ...
June 27, 2022 | Veronica Haggar
The Two Types of Code Vulnerabilities
June 30, 2022 | Casey Bisson

On-Demand Webinars

DevOps.com Webinar ReplaysDevOps.com Webinar Replays
  • Home
  • About DevOps.com
  • Meet our Authors
  • Write for DevOps.com
  • Media Kit
  • Sponsor Info
  • Copyright
  • TOS
  • Privacy Policy

Powered by Techstrong Group, Inc.

© 2022 ·Techstrong Group, Inc.All rights reserved.