Private corporate batches, live online cohorts and 1-on-1 mentoring in the query and visualisation layer over Elasticsearch — Discover, KQL, Lens, dashboards and alerting — 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 Kibana as a query interface first and a chart builder second — because a dashboard is only as good as the KQL and filters underneath it, and a panel that scans a billion documents will fail regardless of how it looks. Sessions cover Discover and field-level investigation, KQL and Lucene syntax including wildcards, ranges, negation with parentheses and null checks, then the full visualisation range from Lens and aggregation-based charts to TSVB and Timelion with offsets and splits. Dashboards, controls and drilldowns, alerting rules and connectors, reporting, Dev Tools, Spaces and role-based access are all taught against a live Elasticsearch cluster.
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 Kibana that means a trainer who has run it in production — the query and visualisation layer over Elasticsearch — Discover, KQL, Lens, dashboards and alerting — 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 Kibana batchKibana is the query and visualisation interface for data held in Elasticsearch. It stores no data of its own: every chart, table, map and dashboard is a query issued against an Elasticsearch index, and every saved object — data view, visualisation, dashboard, alert rule — lives in a hidden Elasticsearch index that Kibana manages. Understanding that relationship is what makes the difference between building dashboards that work and building dashboards that time out.
The day-to-day surfaces are Discover and the visualisation editors. Discover is where investigation happens: pick a data view, set a time range, write a query in KQL or Lucene syntax, add filters, and read raw documents field by field. Kibana Query Language covers the cases people actually need — field matching, wildcards, ranges, boolean operators with parentheses, and existence or null checks — while Lucene syntax and the filter pills remain available for anything it does not express.
On top of that sit the building tools. Lens for drag-and-drop charts, the aggregation-based editors for line graphs, tables, pie and gauge charts and metrics, TSVB and Timelion for time-series work with offsets, splits and expression syntax, Maps for geospatial data, and Canvas for presentation-grade output. Dashboards compose those panels with shared filters, controls and drilldowns. Around them, Kibana provides alerting rules with connectors, reporting, Dev Tools for raw queries, Spaces for separating teams, and role-based access control down to index and field level.
Most organisations that run Elasticsearch have far more people who need to read the data than people who can write a Query DSL request. Kibana is what closes that gap, which is why it is usually the most widely used component of the Elastic Stack and the one whose quality determines whether the whole investment is judged a success.
The skill in demand is not clicking through the chart wizard. It is knowing which visualisation honestly represents the question, writing KQL that narrows a billion documents before the aggregation runs, using filters and controls so one dashboard serves every team rather than spawning forty copies, and building drilldowns that carry context from an overview into raw documents. Those are the habits that make an incident dashboard usable at three in the morning.
There is also a governance dimension that surfaces the moment more than one team shares an instance. Spaces, saved-object management, role-based access down to field level, and exporting dashboards as objects that can be version-controlled are what stop a shared Kibana turning into an unmanaged sprawl nobody trusts.
Getting Kibana connected and understanding what it is doing. Installation and the connection to Elasticsearch, kibana.yml, the saved-objects index, then data views — index patterns, the time field, field types and formatters — and the well-known gotchas that account for most first-week confusion.
Topics: Installing Kibana and connecting it to an Elasticsearch cluster · kibana.yml: the settings that matter · How Kibana stores saved objects, and why that index matters · Data views: index patterns, wildcards and the time field · Field types as Kibana sees them: keyword vs text, and what is aggregatable · Field formatters, custom labels and scripted versus runtime fields · Gotchas: missing time field, unanalysed vs analysed fields, no results for the right reason · Time range, refresh and the timepicker's effect on every query · Advanced settings worth changing on day one
Where real work starts. Navigating Discover, selecting and reordering fields, reading the field list and its value distribution, expanding documents, surrounding-document context, saved searches, and the workflow of narrowing from millions of events to the handful that explain an incident.
Topics: The Discover interface and the document table · Selecting fields, column order and table density · The field list, top values and quick visualise · Expanding a document: table view and JSON view · Viewing surrounding documents and single-document context · Saved searches and reusing them as dashboard panels · Sorting, sampling and the document limit · Exporting results to CSV · An investigation workflow from symptom to root-cause documents
The language layer under every panel. KQL in depth — matching, wildcards, ranges, boolean operators, parentheses and negation, existence and null checks — then Lucene syntax for what KQL does not express, filter pills including custom DSL filters, and the difference between a query and a filter in how results are cached.
Topics: Basic text queries in KQL: free text vs field-scoped · Field matching, quoting and case behaviour · Boolean operators, NOT and parentheses · Querying numbers and dates with range syntax · Querying with wildcards, and what wildcards cost · Querying for nulls and the existence of a value · Lucene query syntax and when to switch to it · Filter pills: add, edit, negate, disable, pin · Custom DSL filters for anything the UI cannot express · Query vs filter: scoring, caching and performance · Saving queries and sharing them across dashboards
Turning a query into a chart that tells the truth. The visualisation editors and how the aggregation model maps onto a chart — metrics on one axis, buckets on the other. Line graphs from first creation through options, metrics and saving; then tables, pie charts, gauges and single-metric panels, and the rules for choosing between them.
Topics: The visualisation model: metrics, buckets and how they map to a chart · Creating a line graph and configuring its options · Line graph metrics: count, average, sum, percentile, unique count · Bucket aggregations: date histogram, terms, filters, ranges · Split series and split charts · Saving, reusing and duplicating visualisations · Grids and data tables · Pie and donut charts, and when they mislead · Gauge charts and goal panels · Metric and single-value panels · Lens: drag-and-drop building and switching chart types · Choosing the honest visualisation for a given question
Composing panels into something a team relies on. Creating dashboards, layout and panel sizing, applying filters and queries at dashboard level, controls that let readers slice without editing, drilldowns between dashboards and into Discover, time synchronisation, sharing and embedding, and dashboard performance.
Topics: Creating a new dashboard and adding existing or new panels · Layout, panel sizing and reading order · Dashboard-level query and filter bar; pinned filters · Controls: options lists, range sliders and time slider · Drilldowns: dashboard-to-dashboard and dashboard-to-Discover · URL drilldowns to external systems · Panel-level time ranges and per-panel options · Saving, cloning and organising dashboards · Sharing: links, embed code, snapshots and permalinks · Dashboard performance: panel count, time range and aggregation cost · Designing for on-call versus designing for reporting
The tools built for time. TSVB for time-series visual builder panels with derived series and annotations, then Timelion and its expression language: the syntax, offsets for comparing a period against a prior one, splits for breaking a series by a field, and the arithmetic and transformation functions.
Topics: TSVB: time series, metric, top N, gauge and markdown panel types · TSVB aggregations, series options and annotations · Derived series: derivative, cumulative sum, moving average, math · Introduction to Timelion and its expression syntax · Timelion data sources and the .es() function · Timelion offsets: comparing this week with last week · Timelion split by field and multiple series · Timelion transformation functions: derivative, movingaverage, trend, scale · Styling, labels and conditional formatting in Timelion · Choosing between Lens, TSVB and Timelion for a given question
Getting output out of Kibana. Alerting rules and their evaluation model, connectors to email, Slack, webhook and ticketing systems, rule management and muting; then reporting and scheduled exports, geospatial work in Maps, and Canvas for presentation-grade output built from live queries.
Topics: Kibana alerting: rule types, conditions and evaluation schedule · Threshold rules on index data and on Elasticsearch queries · Actions, connectors and action frequency · Rule management: enabling, muting, snoozing and troubleshooting · Alerting on log and metric thresholds · Reporting: PDF and PNG generation, CSV export · Scheduled reports and the reporting queue · Maps: layers, geo fields, clustering and choropleths · Canvas: elements, expressions and live data in a presentation · Choosing between a dashboard, a report and an alert for a given need
Running Kibana for an organisation rather than a person. Dev Tools for raw queries and diagnostics, saved-object management including export and import, Spaces for separating teams, role-based access control down to index and field level, upgrade considerations, and putting dashboards under version control.
Topics: Dev Tools console: running Query DSL directly and reading responses · Grok debugger, painless lab and the search profiler · Saved objects: browsing, editing, exporting and importing · Resolving broken saved-object references after a data view change · Spaces: creating them and scoping saved objects per team · Role-based access control: Kibana privileges, index privileges, field and document-level security · Users, roles and integration with an identity provider · Stack Management: index management, index lifecycle policies and data views · Monitoring Kibana and Elasticsearch from the Stack Monitoring UI · Dashboards as code: exporting NDJSON and deploying through CI · Upgrade considerations and saved-object migrations
We rebuild the agenda around the tools you actually run.
Request a custom agendaWork a real incident in Discover: narrow by time, add KQL and filters, inspect field distributions and document context, and end with the documents that explain the failure.
Translate twelve written questions into KQL using wildcards, ranges, parentheses, negation and null checks, then convert three to filters and measure the difference.
Build the same question as a line graph, table, pie chart and gauge, then argue which one is honest and rebuild a misleading chart from an existing dashboard.
Collapse three near-duplicate dashboards into one driven by controls, add drilldowns into Discover, and cut its load time by restructuring the panel queries.
Use Timelion offsets, splits and moving averages to build a comparison view, then reproduce the same analysis in TSVB and compare the two approaches.
Stand up a multi-team Kibana with Spaces and field-level access control, export every saved object as NDJSON, and deploy it into a clean instance through CI.
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.
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Certificate of completion
Basics explanation was exemplary from Rajesh where he dealt with complicated topics to be simple. Great learning stuff personally for me.
Very detailed explanation and has lots of patience in attending the questionnaire. Thanks again for your wonderful sessions.
Good discussion, helped us to understand different tools in SRE.
Got good lab sessions which kept the new DevOps tool learnings to the point and it helped a lot in my career.
I took Terraform training with the tutor named Mithilesh. I requested to tailor the course curriculum for my needs. He did an excellent job of showing me how to write the Terraform script per the instructions provided.
My experience with the AIOps training was positive. The course covered important topics in a structured way, and Rajesh Kumar explained the concepts patiently. I found the practical aspects particularly helpful because they made the technical content easier to understand.
| 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.
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