Private corporate batches, live online cohorts and 1-on-1 mentoring in Power Query, star-schema modelling, DAX, report design and governed publishing at enterprise scale — 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 Power BI around the model rather than the visual: why a star schema outperforms a flattened table, what filter context actually is and how CALCULATE modifies it, and why the same measure returns different numbers in two visuals. Sessions cover the operational half most courses skip — storage modes and composite models, incremental refresh against a large fact table, row-level security that scopes one report to many audiences, gateway configuration, deployment pipelines and reading the Performance Analyzer to find the visual that is costing three seconds. Every concept is demonstrated against a live model rather than described on slides.
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 Power BI that means a trainer who has run it in production — Power Query, star-schema modelling, DAX, report design and governed publishing at enterprise scale — 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 Power BI batchPower BI is Microsoft's business intelligence platform. It has three parts that people often blur together: Power BI Desktop, where you connect to data, shape it, model it and design reports; the Power BI service, where semantic models and reports are published, refreshed, secured and shared; and the mobile and embedded surfaces where the results are consumed. Underneath all of them sits a tabular analytical engine — the same VertiPaq columnar store used by Analysis Services — which is why a well-modelled Power BI file can scan tens of millions of rows in under a second and a badly modelled one cannot.
Three languages do the work. M, the Power Query language, handles connection, shaping, merging and cleansing at refresh time. DAX handles calculation at query time — measures, filter context, time intelligence and everything that makes a number respond correctly when a user clicks a slicer. The report canvas handles interaction: visual interactions, bookmarks, drillthrough and tooltips. Most Power BI problems that look like DAX problems are actually modelling problems, and most that look like performance problems are storage-mode or cardinality problems.
The platform side matters just as much in an organisation. Workspaces and apps decide who sees what, on-premises data gateways connect to data that is not in the cloud, incremental refresh keeps large models refreshing in minutes rather than hours, row-level security scopes a single report to many audiences, and deployment pipelines move content from development to test to production without rebuilding it. Getting these right is the difference between a few useful reports and a BI capability the business trusts.
Reporting has moved from a central BI team producing fixed outputs to analysts and domain teams building their own models. That is faster, and it fails in a predictable way: dozens of near-identical files, contradictory numbers for the same metric, refreshes that time out, and no clear owner. Organisations are now investing in the discipline — shared semantic models, governed workspaces, defined metric definitions — rather than more dashboards.
At the same time the technical bar has risen. Composite models and DirectQuery, incremental refresh, calculation groups, the XMLA endpoint and integration with lakehouse storage mean Power BI is no longer only a desktop tool. Handling a model that will not fit in memory, or a refresh window that has run out of room, requires genuinely different skills from building a first report.
The demand is for people who can do both halves: design a star schema and write DAX that returns the right number under every filter combination, and then publish, secure, refresh and govern the result so that the business can rely on it. Report-building alone is now table stakes.
The whole pipeline before any button is pressed. Where Desktop, the service, gateways and capacity sit; what a semantic model actually is; and the VertiPaq columnar engine that explains most performance behaviour later in the course. Then the workflow itself — connect, shape, model, calculate, visualise, publish, govern — and the failure that happens when a step is skipped.
Topics: Power BI Desktop, service, mobile and Report Server · Semantic models, reports and dashboards — what each one is · The VertiPaq columnar engine and compression · Licensing tiers and capacity in plain terms · The end-to-end analytics workflow · Where Power BI fits alongside Excel, a warehouse and a lakehouse · Common anti-patterns and how they start
Getting data in and getting it clean. Connectors and connection settings, the query editor and what each transformation actually generates in M, and the transformations that matter most: unpivot, merge, append, group by, split and type handling. Then query folding — the single most important performance concept in Power Query — parameters, and dataflows for reuse across models.
Topics: Connectors, credentials and privacy levels · The query editor and applied steps · Reading and editing M in the advanced editor · Filtering, splitting, replacing and type conversion · Unpivot, merge and append · Group by and aggregation at source · Query folding and why breaking it wrecks refresh time · Parameters and reusable functions · Dataflows for shared, reusable transformations · Error handling and profiling data quality
The module that determines whether everything after it works. Facts and dimensions, why a star schema beats both a single flat table and a normalised snowflake, relationship cardinality and cross-filter direction, and the specific problems — ambiguous paths, bidirectional filtering, many-to-many — that produce wrong numbers. Plus a proper date table, hierarchies and model hygiene.
Topics: Facts, dimensions and grain · Star schema versus flat table versus snowflake · Relationship cardinality: one-to-many, one-to-one, many-to-many · Cross-filter direction and the cost of bidirectional relationships · Active and inactive relationships and USERELATIONSHIP · Role-playing dimensions · Building and marking a date table · Hierarchies and display folders · Hiding columns, formatting and model documentation · Cardinality, column data types and model size
DAX properly, starting from the concept that explains everything else. Calculated columns versus measures and when each is correct; row context and filter context; how a visual creates filter context; and CALCULATE as the only function that modifies it. Then variables for readability and performance, and iterator functions.
Topics: Calculated columns vs measures vs calculated tables · Row context and filter context · How a visual generates filter context · CALCULATE and filter modification · FILTER, ALL, ALLEXCEPT and REMOVEFILTERS · Iterators: SUMX, AVERAGEX and friends · VAR and RETURN for readable, faster measures · RELATED and RELATEDTABLE · DIVIDE and safe division · Common DAX mistakes and how to spot them
The calculations businesses actually ask for. Year to date, prior year, rolling periods and period-over-period comparison, built on a proper date table rather than improvised. Then ranking, running totals and semi-additive measures such as opening and closing balances. Finally calculation groups, which stop a model from needing forty near-identical measures.
Topics: Time intelligence functions and their date-table requirement · YTD, QTD, MTD and prior-period comparison · Rolling and moving averages · Custom and fiscal calendars · Ranking with RANKX and TOPN · Running totals and cumulative measures · Semi-additive measures: opening and closing balances · Calculation groups and calculation items · Dynamic format strings · Measure organisation and naming conventions
Reports that answer a question instead of displaying everything. Choosing the right visual for the comparison being made, layout and visual hierarchy, and colour and accessibility. Then the interaction model: slicers and sync, edit interactions, drillthrough, tooltips, bookmarks and buttons, conditional formatting, and designing for mobile.
Topics: Choosing a visual for the question being asked · Layout, grouping and visual hierarchy · Colour, contrast and accessibility · Slicers, sync slicers and filter panes · Edit interactions and controlling cross-filtering · Drillthrough and report page tooltips · Bookmarks, selection pane and buttons · Conditional formatting and dynamic titles · Small multiples and custom visuals · Mobile layout · Paginated reports and when to use them instead
Getting content out of Desktop and under control. Workspaces and roles, apps for distribution, and the difference between sharing a report and publishing an app. Then governance: row-level security so one report serves many audiences, sensitivity labels, endorsement, the shared semantic model pattern, and deployment pipelines that move content through environments properly.
Topics: Workspaces, roles and access · Publishing apps and audiences · Row-level security: static and dynamic roles · Testing RLS with view-as · Object-level security · Sensitivity labels and data protection · Certified and promoted semantic models · The shared semantic model and thin report pattern · Deployment pipelines: development, test, production · Usage metrics and adoption tracking · Source control and Git integration for workspaces
Making it work when the data is large and the refresh window is short. Storage modes and composite models, aggregation tables, incremental refresh with partitions, and the on-premises data gateway. Then performance work with a method rather than guesswork: Performance Analyzer to find the slow visual, DAX Studio to see the query, and the model changes that actually help.
Topics: Import, DirectQuery, Dual and composite models · Aggregation tables and automatic aggregations · Incremental refresh, partitions and the refresh window · On-premises data gateway: install, clustering and troubleshooting · Scheduled refresh, failures and alerting · The XMLA endpoint and external tools · Tabular Editor for model development at scale · Performance Analyzer: finding the slow visual · DAX Studio: reading the query plan and server timings · Reducing model size: cardinality, columns and data types · Lakehouse and Direct Lake connectivity · Capacity monitoring and cost
We rebuild the agenda around the tools you actually run.
Request a custom agendaShape a deliberately awkward source with unpivot, merge and type handling, then find and fix the step that broke query folding and measure the refresh difference.
Convert a wide flat table into facts and dimensions with a marked date table, then compare model size, refresh time and measure behaviour before and after.
Build percentage-of-total, prior-period and rolling measures, then debug a measure that returns different numbers in two visuals and explain exactly why.
Take a cluttered report, identify the decision it should support, and rebuild it with controlled interactions, drillthrough and tooltips.
Implement dynamic RLS, verify it with view-as for three different users, then promote the content from development to production through a pipeline.
Configure incremental refresh and a gateway against a large fact table, tune the model with aggregations, and cut a slow report page in half with Performance Analyzer evidence.
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 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.
I was looking to improve my understanding of AIOps, and this training helped me achieve that goal. Rajesh Kumar explained the subject in a structured and practical manner. The sessions on different AIOps concepts were informative.
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 Rundeck developer session was excellent and highly engaging. I appreciated how well the session was structured, with the theoretical concepts explained clearly and in simple terms. What stood out most to me was the demo — it was both informative and enjoyable. I especially liked how Rajesh walked us through not only the happy path but also the sad path, showcasing common issues and sharing practical troubleshooting tips.
Rajesh's experience and knowledge are exceptional and we learnt invaluable practical knowledge which we can apply in our production environment. Incredibly friendly and gave us a fantastic insight both in-depth and at a high level of the Rundeck product.
| 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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