Private corporate batches, live online cohorts and 1-on-1 mentoring in full-stack observability with APM, infrastructure, logs, browser and synthetics queried through NRQL — taught by a practitioner who runs it in production.
20 years across DevOps, SRE and Security · 10,000+ engineers trained · Trained teams at JPMorgan Chase, Verizon, Nokia and the World Bank
Principal DevOps Engineer & Architect
Rajesh teaches New Relic around NRQL and instrumentation rather than around the default dashboards — because the queries you can write and the attributes you chose to send are what decide whether the platform answers a real question. Sessions cover infrastructure and APM agent deployment across Linux, Windows, Docker and Kubernetes, custom instrumentation and custom attributes, transaction traces and thread profiling, logs in context with APM, browser and synthetic monitoring, NRQL-based alert conditions with Applied Intelligence, and the ingest and retention decisions that determine what the platform costs.
Twenty years across DevOps, SRE and Security, in principal and architect roles at PayPay, SoftwareAG, ServiceNow, JDA Software, Intuit, Adobe and others. He has trained engineers at JPMorgan Chase, Verizon, Nokia, the World Bank, VMware, Oracle, Mercedes-Benz and Airbus — more than 10,000 people personally. He teaches what he runs, not what he reads.
You are booked with a named engineer, and that is who turns up. Marketplaces and larger providers rotate whoever is free, so the person who sold you the agenda is rarely the person teaching it.
The same trainer is available for the next engagement, which matters when a team builds on what it learned last time.
Your batch is assigned a named trainer before it starts, and that is who teaches it. See the full faculty.
Engagements are matched on the tool, not the calendar. For New Relic that means a trainer who has run it in production — full-stack observability with APM, infrastructure, logs, browser and synthetics queried through NRQL — rather than whoever is free that week. You are told who is teaching before you commit, and that person is on the discovery call that shapes the agenda.
Where a batch is large enough to need a second trainer, the pairing is declared up front. The lead trainer stays accountable for the syllabus and the assessment either way.
Principal DevOps Engineer & Architect
Twenty years across DevOps, SRE and Security in principal and architect roles at PayPay, SoftwareAG, ServiceNow, JDA Software, Intuit, Adobe, IBM/Emptoris, Ness, MindTree and Accenture. He has trained more than 10,000 engineers personally, at organisations including JPMorgan Chase, Verizon, Nokia, the World Bank, VMware, Oracle, Mercedes-Benz and Airbus. He teaches what he runs, not what he reads.
Teams of 8–30
Custom agenda, your timezone, onsite or online, NDA-friendly.
Request a quoteIndividual engineers
A private instructor and a curriculum built around your goal.
₹99,999Individuals who want peers
Scheduled batch, max 8 to 10 hours of live instruction.
₹34,999A private batch starts with a discovery call. We look at the stack you actually run — the CI system, the cloud, the constraints — and map the agenda onto it, so examples use your topology rather than a generic one.
Delivery is onsite at your premises, live online, or hybrid, scheduled around your release calendar rather than ours. Batches run 8 to 30 engineers.
Every attendee leaves with recordings, slides, lab repositories and a completion certificate. You receive an attendance and assessment report. Invoicing supports PO and GST.
Talk to us about a private New Relic batchNew Relic is a hosted observability platform that ingests metrics, events, logs and traces into a single telemetry store and lets you query all four with one language. Data arrives from language agents embedded in applications, an infrastructure agent on hosts and containers, log forwarders, browser and mobile agents, synthetic monitors, and open standards including OpenTelemetry and Prometheus remote write.
What distinguishes New Relic operationally is that everything it collects is queryable. NRQL — the New Relic Query Language — runs SQL-shaped queries with faceting, time-series bucketing, histograms and funnels across every data type, and any NRQL query can become a chart, a dashboard widget or an alert condition. That removes the usual boundary between what a product's built-in page shows you and what you can actually ask.
The product surface spans APM with transaction traces, thread profiling, error analytics and service maps; infrastructure monitoring for Linux, Windows, Docker, Kubernetes and cloud services; Log Management with parsing and logs-in-context; Browser monitoring for real user experience and Core Web Vitals; Synthetics for scripted API and browser checks; Network monitoring; and vulnerability management. Alerts sit above all of it, with NRQL-based conditions and Applied Intelligence for anomaly detection and incident correlation.
Observability stopped being a monitoring purchase and became an engineering discipline. The three pillars — metrics, logs and traces — are only useful when they are correlated, and correlation is a design decision made when instrumentation is added, not a feature you enable afterwards. New Relic is one of the few platforms where a single query language spans all of them, which is why teams adopting it get further than teams that simply install agents.
The practical demand is for engineers who can move past default dashboards. Auto-instrumentation gives you a service list; it does not tell you which transaction is slow because of a downstream call, which error is new versus chronic, or which deploy introduced a regression. Custom instrumentation, well-chosen custom attributes and NRQL that answers a real question are what separate a platform that gets used from one that gets ignored.
Cost discipline matters here too. New Relic bills on ingested gigabytes and on full-platform users, so data retention, sampling, log filtering and attribute design are engineering decisions with a direct financial consequence — and they are the ones teams typically address late.
The concepts before the console. What observability means as distinct from monitoring, the three pillars and how they correlate, the data model New Relic uses underneath, and how to build a roadmap for an organisation that currently has agents installed but no strategy.
Topics: Observability: definition, and how it differs from monitoring · The three pillars: metrics, events, logs and traces · Introduction to metrics: types, collection and aggregation · Fundamentals of logging: structure, levels and patterns · Distributed tracing and why it exists · Correlating metrics, logs and traces for root-cause analysis · Observability in cloud-native and Kubernetes environments · Observability data management, retention and security considerations · Building an observability roadmap for an organisation
Getting hosts, containers and cloud services under observation. Installing and configuring the infrastructure agent on Linux and Windows, the key host metrics and events worth alerting on, on-host integrations for web servers and databases, and the configuration model that scales past hand-editing files.
Topics: New Relic Infrastructure features and capabilities · Infrastructure agent installation and configuration on Linux and Windows · Key host metrics and events for health and performance · On-host integrations: Apache, Tomcat, nginx · MySQL and database monitoring: key metrics and performance indicators · Configuration management: newrelic-infra.yml, integrations directory, custom attributes · Inventory and change tracking · Flex integration for arbitrary data sources
Observability where hosts are ephemeral. Docker container monitoring, the Kubernetes integration for cluster and workload visibility, the cluster explorer, and cloud integrations that pull metrics from services with nowhere to install an agent — EC2, RDS and the rest of the AWS surface.
Topics: Docker and container monitoring in New Relic · Kubernetes integration: cluster, node, pod and workload data · Cluster explorer and Kubernetes events · Kubernetes APM auto-instrumentation and pod annotations · Connecting New Relic to AWS: polling and metric streams · Monitoring EC2, RDS and other managed services · Azure and Google Cloud integrations · Prometheus remote write and OpenTelemetry ingest
Application performance monitoring past the default view. APM architecture and data flow, advanced agent configuration, custom instrumentation for the code paths auto-instrumentation misses, custom attributes and events, and the diagnostic tools: transaction traces, thread profiler, error analytics and service maps.
Topics: APM architecture and data flow · Advanced agent configuration and agent performance tuning · Custom instrumentation for specific application functionality · Custom attributes, custom events and the Event API · Transaction traces: reading them and finding the real cost · Thread profiler and error analytics · Errors inbox: grouping, triage and regression detection · Distributed tracing across services and message queues · Service maps and dependency analysis · Deployment markers and change tracking · Common performance bottlenecks and how they present
The language that makes everything else queryable. NRQL syntax and structure, aggregation and faceting, TIMESERIES and comparison over time, histograms, percentiles, funnels and cohorts, subqueries and nested aggregation, and the query performance rules that decide whether a dashboard loads.
Topics: NRQL syntax: SELECT, FROM, WHERE, FACET, SINCE, UNTIL · Event types and the data model behind them · Aggregation functions: average, percentile, uniqueCount, rate, apdex · TIMESERIES and COMPARE WITH · histogram, funnel and cohort analysis · Subqueries and nested aggregation · String and math functions; capture and parsing in queries · Query performance: cardinality, time window and limit behaviour · The NerdGraph API and NRQL over GraphQL
Logs as queryable telemetry rather than as files. Forwarding paths, ingestion at scale, custom parsing rules that turn text into attributes, log enrichment, logs in context with APM and infrastructure, log-based alerts, and the drop rules and retention policies that control what logging costs.
Topics: Log ingestion, storage and management at scale · Forwarding: infrastructure agent, Fluent Bit, Fluentd, Logstash, cloud services · Logs in context with APM, infrastructure and Kubernetes · Custom parsing rules and Grok patterns · Log enrichment with additional attributes · Querying logs with NRQL · Log-based alert conditions and managing alert noise · Log patterns and anomaly surfacing · Data partitions, drop rules and retention policies · Securing log data, obfuscation and compliance
What users actually experience, as opposed to what the server reports. Browser agent deployment and configuration, single-page application monitoring, custom browser instrumentation, session traces, Core Web Vitals, and correlating front-end experience with the back-end transactions that cause it.
Topics: New Relic Browser: capabilities and agent deployment · Advanced configuration for real user monitoring · Single-page application monitoring and route-change tracking · Custom attributes and custom instrumentation in the browser agent · Session traces and AJAX analysis · Core Web Vitals and front-end performance optimisation · JavaScript error tracking and source maps · Correlating browser data with APM transactions · Mobile monitoring basics
The outside-in view and the security-adjacent products. Scripted API and browser monitors, private locations for internal endpoints, monitoring schedule and location strategy, network performance tracking, and vulnerability management with prioritisation and alerting.
Topics: Synthetic monitor types: ping, simple browser, scripted browser, API test · Writing and maintaining synthetic scripts for multi-step workflows · Scheduling, locations and private locations · Analysing synthetic results for trends and outages · Synthetics integration with APM · Identifying synthetics traffic in application data · Network monitoring setup and key network health metrics · Synthetics for network endpoint testing · Vulnerability management: integration, dashboards and prioritisation · Automating alerts for new vulnerabilities
Turning telemetry into action. Alert policies and conditions including NRQL conditions, thresholds, signal loss and gap-filling behaviour, workflows and destinations, then Applied Intelligence for anomaly detection, incident correlation and noise reduction. Alongside it, dashboard design and reporting.
Topics: Alert policies, conditions and incident preferences · NRQL alert conditions for complex thresholds · Static, baseline and outlier conditions · Signal loss, gap filling and evaluation offset · Workflows, destinations and enrichment · Muting rules for planned work · Managing alert policies at scale · Applied Intelligence: anomaly detection and incident correlation · Custom dashboards: widgets, visualisations and layout · Dashboard organisation, sharing and cross-team use · Custom NRQL charts and reporting · Automating report generation and export
New Relic as part of the delivery pipeline rather than a console people visit. NerdGraph for configuration as code, Terraform provisioning, CI/CD integration with deployment markers and quality gates, ITSM and incident tooling integration, then user management, data ingest control and the cost model.
Topics: NerdGraph API: querying and mutating configuration · Terraform provider for alerts, dashboards and workloads · Embedding New Relic in CI/CD: deployment markers and release quality gates · Integrating alerts with ITSM, PagerDuty and Slack · Workloads and entity relationships · Service levels: SLIs and SLOs in New Relic · User management, roles and full-platform versus core users · Data ingest governance: sampling, drop rules, retention tiers · Auditing and controlling observability spend · Migrating from another platform without a visibility gap
We rebuild the agenda around the tools you actually run.
Request a custom agendaDeploy infrastructure, APM, browser and log agents for one application across Linux, Windows and Kubernetes, and confirm every data type lands with consistent attributes.
Answer ten real production questions in NRQL using faceting, TIMESERIES, percentiles, funnels and subqueries — then optimise the two slowest queries.
Use transaction traces, distributed tracing and the thread profiler to diagnose a latency regression, then add custom instrumentation so it is visible next time.
Write parsing rules for an unstructured format, enable logs in context with APM, then reduce ingest with drop rules while keeping the incident signal intact.
Build NRQL alert conditions with baseline thresholds and correct signal-loss handling, route them through workflows, and mute a planned maintenance window.
Define alert policies, dashboards, workloads and service levels in Terraform, wire deployment markers into CI, and document the resulting ingest budget.
Billed yearly at ₹9,996
Max 8 to 10 hours of live instruction
Full program, private instructor
8–30 engineers · custom agenda · onsite or online · PO and GST invoicing
Refunds. If we cancel or postpone a cohort, you get a full refund within 15 days. There is no money-back guarantee otherwise.
Terms. Course material remains licensed to the attendee. Read the terms.
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Certificate of completion
I recently did a SRE Session with Rajesh Kumar from DevOps School and the session was great. Right from 1st day till day 15, we had a very interactive session. Rajesh clarified our doubts and the tool demos were excellent without any hiccups. He simplified the concepts while sticking to the content with a fine balance between theory and practice. Am convinced he is one of the best trainers for SRE & DevOps concepts.
The trainer (Rajesh) provided very good sessions on SRE profession. Not only hands-on learning on the tools but also SRE mindset.
Very good training session. Well explained from the basics to the complex concepts. Also tried to cover practicals and demos within the 3 hour sessions. The learning content and videos are of a great deal of help.
Basics explanation was exemplary from Rajesh where he dealt with complicated topics to be simple. Great learning stuff personally for me.
Good discussion, helped us to understand different tools in SRE.
Great learning experience from a very knowledgeable instructor with well-prepared course notes. The lab exercises on AWS instance work well to learn the hands-on side of the course.
| What matters | YouTube + blogs | Generic online course | Freelance marketplace | DevOpsSchool |
|---|---|---|---|---|
| Named practitioner | No | Rarely | Varies per booking | Yes — same trainer each time |
| Production experience | Unknown | Unknown | Unverified | 20 years, named employers |
| Custom agenda | No | No | Sometimes | Built from your stack |
| Onsite delivery | No | No | Sometimes | Yes |
| Lab environment | None | Sandbox that expires | Varies | Your own cloud — skill goes with you |
| Assessment | None | Quiz | Rarely | Assignments + capstone per module |
| Per-attendee certificates | No | Sometimes | Rarely | Yes |
| Corporate invoicing | No | Limited | Varies | PO and GST |
| Post-training support | None | Forum, time-limited | None | Lifetime forum access |
Tell us the team, the stack and the timeline. You'll get a straight answer, not a sales sequence.
Talk to an advisorDelivered onsite at your premises or live online in your timezone.
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