{ Abhilash Kumar Bhattaram : Follow on LinkedIn }
In Oracle database environments , most database solutions comes down to some kind of a change management. Organizations "define" it and technical "teams" follow it , but rarely does leadership team look to see the value to "improve" the system.
Predictable SLAs are not about avoiding incidents.
They’re about engineering feedback loops that ensure what’s under control stays under control.
In every organization, SLAs begin as confidence statements — not technical metrics. But when the database fails to respond, SLAs become the single most visible number in a CTO’s report. Predictable SLAs are not about avoiding incidents — they’re about
measuring, controlling, and continuously validating what’s truly under control.
I can give you 2 solid examples
A) RTO (Recovery Time Objective ) / RPO (Recovery Point Objective) is a paper metric for growing databases
I have a database that grows by 1 TB every month. At around 60 TB, with a 5-year data ( 60 months ) retention window, I’ll finally reach a maintainable size — on paper. But here’s the real challenge: Until I reach that 5-year mark, how do I maintain a consistent RTO/RPO when the only guidance I have is a vague number from the management policy ?
But what is 60 TB --> it's the size of the database
In reality my actual storage (and costs) would amount to
- Size of DC + DR
- 30 day Archive log Retention
- My compliance of having monthly yearly backups
So recovery time and points will just keep moving and will not be achievable.
B) SLA's are set on "gut feel" by compliance teams
I have a performance problem , I have been set a 15 min SLA to solve Performance Issues, so I must be an extraordinary DBA to solve any kind of issue ( even an hardware issue ) to meet this SLA.
1. Ground Zero: Where Challenges Start - Understand your own SLA's
It's always a good idea for IT leadership to understand thier own SLA's , shown below areas are a good starting point.
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| 1. Ground Zero: Where Challenges Start |
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| - SLAs set on “gut feel,” not operational data | Solution: Use data-driven SLA baselines derived from actual performance logs
| - No unified view of uptime, MTTR, or incident density | Solution: Integrate monitoring and alerting dashboards across infra, DB, and app
| - Database vs Infra vs App teams report different truths | Solution: Centralize reporting through cross-domain observability metrics
| - Reactive RCA (Root Cause Analysis) after impact, not before | Solution: Embed predictive checks within incident automation workflows
| - Lack of traceability from business event → technical cause | Solution: Map SLA breaches directly to business transaction impact
| - SLAs driven by contracts, not service behaviour | Solution: Define SLAs based on service behavior, not just contractual uptime
| - Metrics stored in spreadsheets, not observability systems | Solution: Migrate SLA metrics from spreadsheets to observability platforms
| |
| >> When SLAs are defined in isolation, predictability becomes guesswork. |
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2. Underneath Ground Zero: Finding the Real Problem
Once you have a measure of the issues trying to adapt the SLA's rather than to fit some arbitary SLA's
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| 2. Underneath Ground Zero: Finding the Real Problem |
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| - SLAs are *targets* — not *systems*. Without system design, no SLA holds. | Solution: Build SLAs as part of system architecture, not after incidents occur
| - Database alerts and metrics don’t map to user-facing transactions | Solution: Correlate DB metrics with business-side indicators (TPS, latency)
| - MTTR data often incomplete (no clear start/end markers) | Solution: Automate start–end timestamps for accurate MTTR calculation
| - SLA reviews happen quarterly — too slow to act | Solution: Run rolling SLA reviews with deviation tracking
| - Support hours not aligned with business load patterns | Solution: Align DBA support coverage with real business activity peaks
| - No automated SLA breach detection or alerting | Solution: Implement threshold-based alerting for SLA drift
| - Lack of baseline benchmarking for “normal” performance | Solution: Benchmark performance weekly to sustain realistic targets
| |
| >> The problem isn’t the SLA value — it’s the missing mechanism to ensure it. |
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3. Working Upwards: From Understanding to Solution
Working upwards is making bussiness efficiency in line with service efficiency.
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| 3. Working Upwards: From Understanding to Solution |
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| - Establish SLA baselines from *measured performance*, not assumptions | Solution: Use monitored production metrics to define all SLA thresholds
| - Correlate DBA metrics with business metrics (TPS, active users, latency) | Solution: Create unified SLA dashboards combining business and technical KPIs
| - Use managed delivery dashboards to capture MTTR, uptime, and backlog trends | Solution: Track MTTR trends over time to highlight bottlenecks
| - Run post-patch SLA validation — ensure consistency post-change | Solution: Validate SLAs after every patch or configuration change
| - Align support schedules with business peak windows | Solution: Optimize on-call and shift models to match transaction volume
| - Convert manual RCA into structured knowledgebase inputs | Solution: Create searchable RCA repositories for repeat issue prevention
| - Measure SLA compliance *per environment* — Prod, DR, UAT, Non-Prod | Solution: Track SLA metrics per environment to ensure parity
| - Create a “predictability scorecard” visible to management | Solution: Build a leadership-facing SLA scorecard with color-coded trends
| |
| >> Predictable SLAs are not promises — they are engineered feedback loops. |
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How Nabhaas helps you
If you’ve made it this far, you already sense there’s a better way — in fact, you have a way ahead.
If you’d like Nabhaas to assist in your journey, remember — TAB is just one piece. Our Managed Delivery Service ensures your Oracle operations run smoothly between patch cycles, maintaining predictability and control across your environments.
TAB - Whitepaper ,
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Managed Delivery Services - Whitepaper ,
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Stay tuned for my next post.
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