Private corporate batches, live online cohorts and 1-on-1 mentoring in the Apache 2.0 fork of Elasticsearch — Security plugin, ISM, alerting, SQL and PPL, and k-NN vector search — 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 OpenSearch as its own engine rather than as Elasticsearch with a different logo: what carries over from the shared Lucene foundation — inverted indexes, mapping and analysis, the Query DSL, aggregations, shard mechanics — and what is genuinely different. Sessions work the Security plugin end to end, including LDAP and OpenID Connect, roles and role mappings, and document-level and field-level security with masking; Index State Management policies against real retention requirements; alerting monitors, triggers and notification channels; anomaly detection; and SQL and PPL alongside the Query DSL. The vector half — k-NN index configuration, engine and space-type selection, ML Commons model deployment, neural and hybrid search — is built live, as is ingest with Data Prepper and the operations material of shard sizing, snapshots, cross-cluster replication and upgrades.
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 OpenSearch that means a trainer who has run it in production — the Apache 2.0 fork of Elasticsearch — Security plugin, ISM, alerting, SQL and PPL, and k-NN vector search — 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 OpenSearch batchOpenSearch is a distributed search and analytics engine forked from Elasticsearch 7.10.2 and Kibana 7.10.2 in 2021, after Elastic moved those projects off the Apache 2.0 licence. It kept the Apache 2.0 licence, and in 2024 the project moved to the OpenSearch Software Foundation under the Linux Foundation. The data model is the one engineers already know: JSON documents indexed into Lucene, an inverted index per analysed field, mappings that decide how each field is stored and searched, indices divided into primary and replica shards distributed across a cluster, and a JSON Query DSL with query and filter context plus a full aggregation framework.
What diverged is the plugin surface, and that divergence is the reason organisations choose OpenSearch. Capabilities that Elastic keeps under its own licence ship here as Apache-licensed plugins that are on by default. The Security plugin provides TLS, an internal user database, LDAP, Active Directory, SAML and OpenID Connect authentication, role-based access control, and fine-grained document-level and field-level security with field masking and audit logging. Index State Management replaces index lifecycle management, driving indices through hot, warm, cold and delete states by policy. Alerting with per-monitor triggers and notification channels, Anomaly Detection built on the Random Cut Forest algorithm, and Cross-Cluster Replication are all included.
The query surface is wider too: the same indices answer in SQL and in PPL, a piped language familiar from log platforms, as well as the Query DSL. OpenSearch Dashboards is the Kibana fork, carrying Observability, Query Workbench and Index Management. The k-NN plugin adds vector search across the Faiss, nmslib and Lucene engines, and ML Commons with Neural Search turns that into embedding-based and hybrid retrieval — which is why it so often sits behind retrieval-augmented generation.
Licensing decided this one. When Elasticsearch moved to SSPL and the Elastic Licence, a large number of organisations discovered their search platform now carried terms their legal teams would not accept — cloud providers and ISVs most obviously, but also enterprises with redistribution obligations or open-source policies that permit Apache 2.0 and little else. OpenSearch exists because that constituency needed a supported path, and it has grown into a first-class engine rather than a snapshot.
The commercial arithmetic reinforces it. Security, alerting, index lifecycle policy and cross-cluster replication are all included at no cost, so the features that most often push an Elasticsearch deployment into a paid subscription are simply present. For a logging or security-analytics platform ingesting terabytes a day, that difference is a line item large enough to justify the migration on its own.
And then vector search changed the demand profile again. The k-NN plugin, ML Commons and Neural Search let the same cluster serve keyword relevance and semantic similarity, and hybrid queries that blend both, so OpenSearch is now a common retrieval layer under generative-AI applications. Teams need engineers who can size an HNSW index, choose between Faiss, nmslib and Lucene engines, and tune hybrid relevance — not only engineers who can write a match query.
Start with the honest comparison, because most attendees arrive with Elasticsearch experience or an Elasticsearch decision to make. The licensing history, what forked and what did not, the plugin surface that diverged, feature-by-feature naming differences, and how to evaluate the two against a real requirement.
Topics: The 2021 fork from Elasticsearch 7.10.2 and Kibana 7.10.2 · Apache 2.0 versus SSPL and the Elastic Licence · Governance and the OpenSearch Software Foundation · What is shared: Lucene, the data model, the Query DSL, aggregations · What diverged: the plugin set, ISM versus ILM, security, alerting, ML · OpenSearch Dashboards versus Kibana · Client compatibility and the version-check problem · Migrating from Elasticsearch: what actually needs changing · Amazon OpenSearch Service and OpenSearch Serverless
A cluster you can see inside. Installation from tarball, package, container and Helm, the configuration files that matter, node roles and discovery, JVM sizing, then the diagnostic surface: cluster health, the cat APIs, allocation explain and node statistics.
Topics: Installing OpenSearch: tarball, RPM and DEB, Docker, Helm · opensearch.yml, jvm.options and heap sizing · Node roles: cluster manager, data, ingest, ml, coordinating-only · Cluster formation, discovery and quorum · Cluster, node, index, document, shard and replica concepts · Cluster health, and what green, yellow and red really mean · The cat APIs for readable diagnostics · Allocation explain and unassigned shards · Installing and listing plugins · OpenSearch Dashboards installation and connection
The decisions you cannot easily undo. Index creation and settings, document CRUD through the REST API, bulk indexing and partial failure handling, aliases; then mapping — dynamic versus explicit, text versus keyword, multi-fields, index templates — and the analysis chain that decides what is actually searchable.
Topics: Creating indices: settings, shard and replica counts · Document CRUD, versioning and optimistic concurrency control · Bulk indexing and handling partial failures · Aliases, and why clients should never name an index directly · Dynamic mapping and its dangers · Field data types: text, keyword, numeric, date, boolean, object, nested · Multi-fields and the text-plus-keyword pattern · Index templates and component templates · The analysis chain: character filters, tokenizer, token filters · Built-in and custom analysers; stemming, stopwords, synonyms · Testing analysis with the _analyze API · Which mapping changes force a reindex, and the reindex API
Three ways to ask the same question, and when each is right. The Query DSL in depth including the query and filter context split; the aggregation framework for analytics; then the SQL and PPL interfaces that OpenSearch adds, which change who in the organisation can query the data.
Topics: Query context versus filter context, scoring and caching · Full-text queries: match, match_phrase, multi_match, query_string · Term-level queries: term, terms, range, exists, prefix, wildcard · Boolean compound queries and boosting · Relevance with BM25, and the _explain API · Pagination, sorting, search_after and point-in-time · Metric aggregations: avg, sum, stats, percentiles, cardinality · Bucket aggregations: terms, range, date_histogram, filters · Multi-level nested aggregations and pipeline aggregations · SQL support: syntax, joins, limitations and the JDBC driver · PPL: piped commands, stats, eval and where · Query Workbench in OpenSearch Dashboards · Search templates and stored queries
The module that most distinguishes OpenSearch operationally, because all of it is included. Transport and HTTP TLS, the security configuration model and securityadmin, authentication back ends from the internal user database through LDAP, Active Directory, SAML and OpenID Connect, then roles, role mappings, tenants, and fine-grained access control.
Topics: The security configuration model and the security index · Certificates: transport TLS, HTTP TLS and node certificate validation · securityadmin.sh and applying configuration changes · Internal user database and password hashing · LDAP and Active Directory authentication and authorisation · SAML and OpenID Connect integration · Roles, permissions, action groups and role mappings · Index permissions, cluster permissions and index patterns · Document-level security with query-based restrictions · Field-level security and field masking · Audit logging: what to capture and what it costs · Multi-tenancy in OpenSearch Dashboards · Common security misconfigurations and how to detect them
Making the cluster look after itself. ISM policies driving indices through hot, warm, cold and delete states with rollover and shrink; then the alerting plugin — monitors, triggers, actions and notification channels — and anomaly detection using Random Cut Forest, plus the Observability interfaces in Dashboards.
Topics: Index State Management policies, states and transitions · Rollover, shrink, force merge, snapshot and delete actions · Hot-warm-cold architectures and node attribute allocation · Policy templates and applying policy to new indices · Data streams and time-series index patterns · Alerting: monitors, per-query and per-bucket triggers · Trigger conditions, throttling and action scripts · Notification channels: email, Slack, webhook, Amazon SNS and Chime · Anomaly Detection: detectors, features and Random Cut Forest · Interpreting anomaly grade and confidence · Observability in Dashboards: event analytics, traces and operational panels · Index Management and Notifications interfaces
The capability driving most new OpenSearch adoption. The k-NN plugin and approximate nearest-neighbour indexing, engine and space-type selection, ML Commons for hosting and calling models, Neural Search for embedding at ingest and query time, and hybrid queries that combine lexical relevance with semantic similarity.
Topics: Vector fields, dimensions and the knn_vector type · Approximate versus exact k-NN, and when brute force is correct · Engines compared: Faiss, nmslib and Lucene · HNSW parameters: m, ef_construction and ef_search · IVF and product quantisation for memory reduction · Space types: l2, cosine similarity, inner product · Memory sizing for vector indices, and the circuit breaker · ML Commons: model groups, registering and deploying models · Connectors to externally hosted embedding models · Neural Search: ingest pipelines that embed, and neural queries · Hybrid search and the normalisation processor · Sparse and neural sparse retrieval · Reranking and search pipelines · Evaluating retrieval quality rather than assuming it
Getting data in and keeping the cluster healthy over years. Ingest pipelines inside the cluster and Data Prepper outside it, then shard sizing, snapshots to object storage, cross-cluster replication and search, performance tuning for both indexing and query, and the upgrade path.
Topics: Ingest pipelines: processors, the simulate API and on-failure handling · Data Prepper: sources, processors, sinks and pipeline configuration · Fluent Bit, Logstash and Beats-compatible ingest paths · Language clients and bulk helpers · Shard sizing for a stated volume, growth rate and retention · Snapshot repositories, policies and restore, including to object storage · Cross-cluster replication: leader and follower indices · Cross-cluster search · Indexing performance: bulk size, refresh interval, translog and merge policy · Search performance: caching, doc values, profiling and slow logs · JVM heap, garbage collection and circuit breakers · Rolling upgrades and version compatibility · Monitoring the cluster, and what deserves an alert
We rebuild the agenda around the tools you actually run.
Request a custom agendaBuild a three-node cluster with transport and HTTP TLS, replace the demo certificates, and connect OpenSearch Dashboards through an identity provider.
Configure roles with document-level and field-level security so two users querying the same index see different documents and masked fields, and audit both.
Answer the same analytical questions in the Query DSL, in SQL and in PPL, then compare the plans, the limits and the cost of each.
Write an ISM policy that rolls over on size, moves to warm nodes, force-merges, snapshots and deletes — then prove every transition happens.
Embed a corpus through a neural ingest pipeline, tune HNSW for a recall target, then build a hybrid query and measure it against BM25 and vector search alone.
Deliver ingest via Data Prepper, explicit mappings, ISM retention, alerting and anomaly detection, least-privilege security and a snapshot and upgrade runbook.
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
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.
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.
| 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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