DevSecOps: It’s Time To Pay for Your Demand, Not Ingestion

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I remember back when mobile devices started to gain momentum and popularity.

While I was excited about a way to stay in touch with frienobservabilityds and family, I was far less excited about limits being placed on call length minutes and tdata center cloud computinghe number of text messages I could utilize … before beingobservability tools forced to pay more.

Bedata computing definitionlieve it or not, the #646 (#MIN) and #674 (#MSG) contact entries were still lingering in my address book until a robservability toolsecentcareer clean-up effort. At one time, those numbers provided a handy mechadevsecopsnism to determine how close I was to hitting the monthly limits enforced by my service provider.

Alcareer quizonobservability matrixg some very similar lines, I recdevsecopsentcareersafely found myselfdata computing wikipedia icareerbuildern an interesting position as a software engineerdevsecops engineer – figuring out how to ldevsecops best practicesog less to avoid exceeding lobservability vs monitoringog ingestion licareerbuildermits set by our observability platform provider.

I began to wonder how much longer this paradigm was going to last.

The Toil of Evaluating logins for Ingestion

I remember the first time my project team wasdevsecops software contacteddevsecops engineer because logobservability tools ingeobservability matrixstion thresholds were exceedinobservabilityg the expected limit withcloud computing data center architecture our observability partner. A collection of new RcareerESTful services had recently been deployed in order to replace an aging monolith.

From a supportability perspective, our team hadcloud computing data center architecture made a conscious effort to provide the production support team with a great deal of logging – in the event the seobservability platformrvicesdata computing ebook did not perform as expected. There were more edge cases than there were regression test coverage, so we werdevsecops softwaree expecting alternative flows to trigger results that would require additional debugging if they did not process as expected. Like most caseobservability vs monitorings, the project had aggressive deadlines that could ndevsecops securityot be missed.

When we were instrcareerbuilderucted to “log less” an unplanobservabilityned effort became our priority. The problem was, we weren’t 100% certain how best to proceed. We didn’t know what components were indata computing definition a better state of validation (to have their logs reduced), and we weren’t exactly sure how much logbig data cloud computinggindevsecopsg we would need to remove to no longer exceed thedata computing definition threshodevsecops solutionsld.

To our team, this effort was a great example ocareer testf what has become known as toil:

“Toil is the kind of work that tends tobservabilityo be manual, robservability platformepetitive, automatable, tactical, devoid of enduring value, and that scales lidevsecops solutionsnearly as a service grows.” – Eric Harvieux (Google Site Reliability Engineering)

Every minobservability platformute our team spent on reducing the amount of logs ingested into the observability platfodata computing wikipediarm came at the expensecareer test of delivering fewer features and functionobservability vs monitoringality for our services. After all, this was our first of many planned releases.

Seeking a “Logobservability software Whatever You Feel Necessary” Approach

What our team really needed was a scenario where our observability partner was fully invested in the success of our prodevsecops best practicesject. In this case, it would translate to a “log whatever you feel necessary” approach.

Those who have walked this path before will likely be thinking “this is where JV has finally lost his mind.” Stay with mbig data cloud computinge here as I think I am on to soobservability platformmething big.

Unfortunately, the current expectatcareer testion is that the observability platform can place licloud computing data protectionmits on the amount of logs that can be ingdata computing kaplanested. Thedevsecops definition sad part of thiscareeronestop approach is that, in doing so, observabilidevsecops solutionsty platforms put their needs ahead of their customers – who areobservability meaning relying on and paying for their services.

This is really no differdevsecops toolsent from a time when I relied on the #MIN and #MSG cdevsecops definitionontactsdata center cloud computing idata computing ebookn my phone to make sure I lived withinobservability meaning the limits placed on me by my mobile service provider. Eventually, my loginbile carrier removedobservability matrix those limobservability platformits, allowobservability meaninging me to use their services in a manner that made me successful.

The bottom line here is thdevsecops toolsat consumers leveraging observability platforms shodevsecops best practicesuld be able to ingest whobservability platformatever they feel is important to support their customers, products, and services. It’s up to the observability platcloud computing data center architectureforms to aobservability meaningccommodate thecareerbuilder associatedevsecops securityd challenges as custodata computing ebookmers desire to ingcareer testest more.

This is jucareersafest like how we engineer oobservability meaningur services in a demdevsecops toolsand-dcareers microsoftriven world. I cannot imagine telling my customer, “Sorry, but you’ve given us too much to procesdata computing wikipedias this month.careeronestop

Pay for Your Demand – Not Ingestion

The better approach here is thdata computing definitione concept of paying for insights and not limiting the actual log ingestion. After all, this is 2024 – a time whdevsecops solutionsen we all should be udata center cloud computingsed to loginng massive quantities of data.

The “pay for your demand – nobservability meaningot ingestion” concept has beobservability engineeringen considered a “miss” in the observability industry… until recently loginen I read tcloud computing data protectionhat Sumo Logic has disrupted the DevSecOps world by removing limcareeronestopits on log ingestion. This market-disruptor approach embraces the concecareer quizpt of “log whatever you feel necessary” with a north starcareerbuilder focused on eldevsecops best practicesiminating silos of log data that were eitheobservability engineeringr disabled or skipped due to ingestion thcareer quizresholds.

Once ingestedcloud computing data center architecture, AI/ML algorithcareersafems help identify and diagnose issues – even before they surface as incidents and service interruptions. Sdevsecops best practicesumo Locareer testgic is taking on the burden of supporting additional data because they realize thcareerbuilderat customers are wdata center cloud computingilling to pay a fair pricareersafece for the insights gained from their approach.

So what does this new strategy to ocareers microsoftbservabilityobservability tools cost expectbig data cloud computingations look like?

It can be difficult to pinpcareerbuilderoint ecareerbuilderxactly, but as an example, if your small-to-medium organization is producing an average of 25 MB of log data for ingestion per hour, tobservability matrixhis could translate into an immediate 10-20% savings (usingSumo Logic’s price estimator)data computing wikipedia on your observability bill.

In taking this approachobservability matrix, every single log is available in a custoobservability engineeringm-built platform that scales along with an entity’s obsobservability vs monitoringervability growth. As a result, AI/ML features can draw upon this informatiobservability meaningon instantly to help dicareeragnose problems – even before they surface with consumers.

When I thinkobservability platform about the project I mentioned abovebig data cloud computing, I truly believe both my team and the producobservability platformtion support team would have been able to detect anomalies faster than what we were forced to implement. Instead, we had to react to unexpected iobservabilityncidents that impacted the customer’s experience.


I was able to delete the #MIN and #MSG ecloud computing data center architecturentries from my address bookdata computing kaplan because my mobile provider eliminated thodata computing wikipediase limitdata computing ebooks, provibig data cloud computingding a better experience for me, their customerdata computing definition.

My readers may recall that I have been focused on the following mission statement, which I feel can apply to any IT professional:

“Focus your ticareerbuildermedevsecops solutions on delivering features/data computing wikipediafunctionalobservabilityity that extends the value of your intellectual property. Leverage frameworks, products, and services for everything else.” – J. Vester

In 2023, I also started thinking hard about toil and making a conscious effort to look for ways to avoid or eliminate this annoying productivity killer.

The concept of “zero dodevsecops best practicesllar ingest” has disrupted the observability market by taking a lead from tcareer testhe mobile service provider's playbook. Eliobservability meaningminating log ingestion thresholds pobservability softwareucareersafets customers in a better poobservability softwaresition to be successful with their own customers, products, and secareers microsoftrvicesobservability platform (learn more about Sumo Logic’s project here).

From my loginerdevsecops toolsspective, not only does this adhere to mycareeronestop mdevsecopsission statement, it provides a toil-free solution to the problem of logdevsecops tools ingestion, data volume, anddata computing kaplan scale.

Have a really great day!