Private corporate batches, live online cohorts and 1-on-1 mentoring in CI/CD for datasets, orchestration, data contracts, quality gates and lineage-backed governance — 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 DataOps as a release problem rather than a tooling tour: pull-request checks on SQL and ELT, environment parity and promotion, quality expectations as blocking gates, data contracts between producers and consumers, and orchestration with retries, SLAs and backfills. Sessions work from the value stream — finding the queues, manual gates and duplicated pipelines that cause rework — and end with flow and quality metrics that show whether the platform is actually improving.
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 DataOps that means a trainer who has run it in production — CI/CD for datasets, orchestration, data contracts, quality gates and lineage-backed governance — 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 DataOps batchDataOps is the practice of applying engineering discipline to the movement of data from source to decision. It borrows lean manufacturing's idea of an assembly line with quality checks at every station, and DevOps's idea that changes should be small, tested, automated and reversible. The unit of work is a dataset rather than a service, but the mechanics are familiar: version the transformation in Git, review it, test it, promote it through environments, and observe it once it is live.
What makes DataOps distinct from DevOps is that correctness has two dimensions. Application CI asks whether the code runs; DataOps has to ask whether the code runs and whether the data it produced is trustworthy. That adds a second class of test — freshness, completeness, uniqueness, referential integrity, distribution — enforced as gates in the pipeline rather than as a dashboard nobody reads. It also adds contracts: an explicit agreement about schema, semantics and service level between the team producing a dataset and the teams consuming it, so a column rename upstream fails a check instead of silently breaking a report.
In practice DataOps is built from a small, stable set of pieces. Git-based workflows and pull-request checks for SQL and ELT code. Orchestration as directed graphs with retries, SLAs and backfills, usually Airflow or a managed equivalent. Environment parity so a transformation behaves the same in dev, staging and production. Quality expectations executed on every run. Lineage and cataloguing so impact can be traced both ways. And flow metrics, the data equivalent of DORA, that tell you whether the platform is getting faster or just busier.
Analytics stopped being a quarterly reporting function and became a dependency of live products. Pricing, fraud scoring, recommendation, forecasting and regulatory reporting all read from pipelines, which means a broken load is now an incident rather than an inconvenience.
Meanwhile the platform itself changed shape. Cloud warehouses and lakehouses made compute elastic and cheap enough that the bottleneck moved from processing to change management: hundreds of models, dozens of contributors, schemas evolving weekly, and no equivalent of the release process that application teams have had for a decade. Most data teams have adopted the tooling of modern data stacks without adopting the practices, and the symptoms are consistent — environment drift, silent schema breaks, duplicated pipelines, rework, and no reliable answer to who consumes this table.
The demand is therefore for engineers who can bring release discipline to data without stalling delivery. That combination — SQL and pipeline fluency plus CI/CD, testing, orchestration and governance — is what organisations are hiring for, and it is exactly what DataOps names.
A precise definition before any tooling: a collaborative practice that improves communication, integration and automation of data flows so that value arrives predictably. The DataOps Manifesto's analytics-as-manufacturing framing, continuous improvement, and quality at every station — plus the myths that derail adoption.
Topics: Definition and intent: predictable value from data flows · The DataOps Manifesto and its principles · Analytics as manufacturing: stations, flow and quality · Myth: DataOps is a tool you can buy · Myth: DataOps is just DevOps applied to data · How DataOps relates to DevOps, MLOps and data governance
How data delivery got here and what specifically hurts. From batch BI, waterfall analytics and long release cycles to cloud data platforms, continuous change, streaming plus batch and product thinking — then the organisational pain that follows: silos, environment drift, schema breaks, rework and platform sprawl.
Topics: Past: batch BI, waterfall analytics, long release cycles · Present: cloud platforms, continuous change, streaming and batch · Product thinking applied to data · Siloed teams and handoff cost · Environment drift and schema breakage · Large-organisation realities: governance, audit, platform sprawl · Finding waste in the value stream: queues, manual gates, duplicated pipelines
The operating principles that everything later is built from, made concrete rather than aspirational. Small batches and fast feedback, quality at the source instead of at the end, automation over coordination, reproducibility as a default, and observability designed in rather than added after the first outage.
Topics: Small batches and why large data releases fail · Fast feedback loops for data changes · Quality at the source vs inspection at the end · Automation over coordination · Reproducibility: same input, same output, any environment · Observability designed in from the start · Continuous improvement rituals that survive contact with delivery
Who owns what. Treating datasets as products with named owners and published service levels for freshness, completeness and accuracy; the platform team's job of providing paved paths rather than gatekeeping; golden sources; and review and sign-off covering both code and data contracts.
Topics: Data as products with SLAs and SLOs · Freshness, completeness and accuracy as published commitments · Ownership, stewardship and on-call for data · Platform enablement and paved paths · Golden sources and eliminating duplicate truth · Review and sign-off for code and data contracts · Interfaces between producing and consuming teams
The pipeline as a sequence of stations, each with an entry check and an exit criterion: ingest, prepare, validate, transform, serve, observe. Then the environment story — reproducible environments, promotion from dev to staging to production, and rollback readiness for when a bad load reaches production.
Topics: Ingest: batch, incremental and change data capture · Prepare: standardisation, typing and deduplication · Validate: entry checks before transformation · Transform: modelling layers and idempotent rebuilds · Serve: marts, extracts and APIs · Observe: freshness, volume and quality signals · Reproducible environments and seed data · Promotion dev to stage to prod, and rollback readiness
Release engineering applied to SQL and ELT. Git workflows suited to data teams, pull-request checks that compile, lint and test transformations against sample data, automated quality gates, artefacted releases, environment parity, deployment policies and auditable runs.
Topics: Git-based workflows for data teams · Pull-request checks for SQL and ELT code · Unit and integration testing of transformations · Contract checks in CI · Automated quality gates and blocking criteria · Artefacted releases and versioned deployments · Environment parity and configuration management · Deployment policies and auditable run history
Running pipelines reliably. DAG-based orchestration, retries and backoff, SLA definitions and misses, backfills that do not corrupt history, and the choice between schedule-driven, event-driven and hybrid execution. Airflow as the portable default, plus the managed options on the major clouds.
Topics: DAG-based pipelines and dependency modelling · Retries, backoff and idempotent tasks · SLAs, SLA misses and alerting · Backfills and reprocessing without corruption · Event-driven vs schedule-driven vs hybrid · Combining batch and streaming in one platform · Apache Airflow as a portable choice · Managed orchestration options on AWS and GCP · Dynamic DAGs, sensors and anti-patterns
Making trust testable. Expressing expectations as executable checks, deciding which failures warn and which block, handling schema evolution deliberately, and enforcing contracts at the boundary so producers learn about a breaking change before consumers do.
Topics: Expectation frameworks and executable checks · Freshness, completeness, uniqueness and referential integrity · Distribution and anomaly checks on data values · Warn vs block: choosing gate severity · Schema evolution and backward compatibility · Data contracts between producers and consumers · Quarantine, dead-letter and partial-load strategies · Expectation pass-rate and SLA compliance as metrics
The controls that make a data platform defensible without turning it into a queue. Access control and least privilege, tagging and classification, lineage as an operational tool rather than documentation, policy-as-code, and the handling of personal data through masking, tokenisation and row-level policies.
Topics: Access control, roles and least privilege · Classification and tagging of sensitive data · Column-level and row-level access policies · Masking, tokenisation and PII handling · Lineage capture and impact analysis both directions · Policy-as-code and automated policy testing · Audit trails and evidence for compliance reviews · Platform capabilities: catalogues, lineage and quality monitoring
Proving the practice works and rolling it out. Flow metrics adapted to data — lead time for a transformation change, release frequency, change failure rate for bad loads, MTTR for broken DAGs — alongside quality metrics. Then the sequencing: quick wins first, then paved roads.
Topics: Pipeline observability: freshness, volume, latency, cost · Change lead time for ELT and SQL changes · Release frequency and change failure rate for data · MTTR for broken DAGs and failed loads · Expectation pass-rate and freshness SLA compliance · Quick wins: PR checks, one quality gate, lineage for one domain · A 30/60/90 roadmap toward platformised paved roads · Choosing your next three improvements
We rebuild the agenda around the tools you actually run.
Request a custom agendaTrace a real dataset from source system to consumed report, timestamp every handoff, and quantify wait time, manual gates and rework.
Add compile, lint, unit test and data-test stages to a transformation repository so a breaking change cannot be merged.
Build an Airflow DAG with idempotent tasks, retries, an SLA and a backfill path, then kill a source mid-run and recover cleanly.
Publish a contract for a shared table, rename a column upstream, and watch the pipeline fail the producer rather than the consumer.
Capture lineage across a set of models and answer both directions — what feeds this table, and what breaks if it changes — without asking anyone.
Deliver an end-to-end path where a change is reviewed, tested, gated on quality, promoted through environments, observed in production and measurable.
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
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.
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.
Very detailed explanation and has lots of patience in attending the questionnaire. Thanks again for your wonderful sessions.
| 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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