Databricks Advanced Training
Design, tune and govern an enterprise Databricks lakehouse for performance, cost, security, generative AI and platform migration. Learn Databricks through a clear Foundation to Advanced path, with hands-on labs, live cohort options, certification support and full LMS access.
Last updated
Self-paced video subscription
Billed yearly at ₹9,996. This includes all four levels, complete LMS access for one year, plus 20+ additional courses and 50+ tools access.
- All four Databricks levels in one price.
- 20+ additional courses and 50+ tools included, even when subscribing to one course.
- Best fit for learners who want full-library access and flexible progress.
Live & Interactive session
Instructor-led cohort, max 8 to 10 hours, with demos, labs, Q&A, assignments and practical review.
- Live small-batch cohort from soon.
- Interactive discussion and lab troubleshooting.
- Recommended for teams that need guided implementation.
1:1 Live training
The same agenda, taught one-to-one by the trainer. Start immediately, without waiting for the next batch. Max 8 to 10 hours, one-to-one.
About Databricks Advanced Training.
Understand where Databricks fits in modern software delivery, what this level covers, and how the program builds practical confidence step by step.
Program overview
Databricks is taught as a practical technical skill for modern engineering, operations and platform teams. The course explains where the tool fits, how it is used in real delivery environments, and how learners can apply it through guided practice.
This DevOpsSchool Databricks program is built for working professionals who want usable skills instead of only conceptual exposure. The training emphasizes live examples, workflow clarity, operational controls and progressive skill depth from Foundation to Advanced.
Private Databricks training for delivery teams, platform groups and engineering organizations.
DevOpsSchool provides corporate Databricks training for teams that need faster adoption, workflow standardization, administrative control, integration guidance and governance-ready rollout. Private batches can be aligned to development practice, release workflow, access model, maturity level and internal tooling.
- Private online or classroom delivery for engineering teams
- Aligned to Databricks usage maturity, admin depth and collaboration patterns
- Useful for rollout, migration, governance and workflow consistency goals
- Can be tailored for developers, administrators, DevOps teams and leadership stakeholders
Why teams use DevOpsSchool
DevOpsSchool combines live demonstration, practical workflow discussion, implementation-oriented examples, and strong post-session learning support. That makes the training more usable for real teams than generic slide-led courses.
Choose any level. The page still shows the full progression.
Foundation, Essential, Intermediate and Advanced are presented as one linked training flow. Self-paced buyers get all levels by default, even if they start from only one course page.
Instructor-led, self-paced and corporate delivery options.
Compare delivery format, duration, level access and batch style. Fees are shown only in the top decision panel and once again after the agenda.
| Duration | Mode | Level access | Batch |
|---|---|---|---|
| 8 to 10 Hrs. max | Live & Interactive | Advanced | Public batch |
| 8 to 10 Hrs. max | 1:1 Live training | Advanced | Starts immediately |
| 1 year LMS access | Self-paced video | All 4 levels | Anytime access |
| 5 Days | Corporate | Databricks Advanced Training | Private team batch |
What learners should be able to do after Databricks training.
The outcomes are written for practical delivery teams: developers, DevOps engineers, release engineers, administrators and technical leads.
Engineer reliable lakehouse pipelines
Build batch and streaming pipelines on Delta Lake with the medallion pattern and data quality expectations.
Govern data with Unity
Secure, share and audit data and models using Unity Catalog permissions, lineage and system tables.
Operate ML in production
Track, register, serve and monitor models with MLflow and Databricks Model Serving.
Why this skill, what you will achieve, and who should attend.
Use this section to decide whether the course matches your role, current experience and expected Databricks responsibilities before reviewing the detailed agenda.
Why Databricks matters
- Databricks helps teams standardize a critical part of their software delivery, infrastructure, automation or operations workflow.
- It gives learners a repeatable way to move from basic usage to confident execution in team and enterprise environments.
- For DevOps and platform teams, Databricks becomes more valuable when connected with automation, governance, monitoring and delivery practices.
- Teams using Databricks effectively reduce ad-hoc execution and move toward consistent engineering standards.
Objectives
- Understand Databricks concepts, architecture, terminology and operational usage patterns.
- Learn practical configuration, workflow design, access controls, team usage and quality practices.
- Apply Databricks in real delivery workflows using automation, integrations, traceability and governance patterns.
- Build practical confidence to use Databricks for individual, team and organizational use cases.
Pre-requisites
- Basic computer literacy and familiarity with software development workflows.
- Helpful but not mandatory: introductory DevOps, cloud, automation or software delivery awareness.
- No deep programming background is required for the Foundation and Essential levels.
Who this course is for
- Developers, administrators and engineers who need practical Databricks usage skills.
- DevOps engineers, release engineers, SREs and platform teams managing delivery standards.
- Engineering leads and technical managers responsible for workflow quality, access controls and governance.
- Corporate teams migrating to or standardizing on Databricks.
Small-batch live training starts every month.
Weekday batches start on the 1st and weekend batches on the first Saturday of every month. One-to-one (1:1) training can start immediately; corporate sessions are scheduled on demand.
Weekend live batch
Starts on the first Saturday of every month. Weekend sessions with demos, labs and Q&A; max 10 learners.
Weekday live batch
Starts on the 1st of every month. Weekday sessions for working professionals and distributed teams; max 10 learners.
Ask advisorAgenda depth up to the selected level.
This page shows the learning flow from Foundation through Advanced. Each level adds to the previous one, and the agenda stops at the current page level. Day counts are for corporate (day-wise) delivery; live online batches cover the same agenda in max 8 to 10 hours.
Foundation Level: platform, Spark basics, Delta Lake and MLflowDay 1
- Introduction to Databricks: overview of the platform and its components, workspace, clusters and notebooks architecture
- Setting up Databricks: creating, configuring and managing clusters; runtime versions, autoscaling and auto-termination
- Core features: notebooks, version control, jobs and collaboration across data engineering and data science
- Apache Spark basics: Spark core components, driver and executors, RDDs, DataFrames and Spark SQL
- Spark DataFrame API: how DataFrames are used for large-scale processing, data cleaning and manipulation
- Spark SQL: performing queries on large datasets, temporary views and mixing SQL with Python cells
- Building data pipelines: techniques for transforming and storing data using Spark
- Working with Delta Lake: reliable and scalable data storage, reading and writing Delta tables
- Machine learning with MLlib: data preparation, feature extraction, model training and evaluation
- MLflow: managing machine learning experiments, model tracking and hyperparameter tuning
- Collaboration in Databricks: sharing notebooks, Git integration for version control, team-based workflow
- Data visualization: built-in notebook visualizations and building interactive dashboards to present insights
- Hands-on: set up a workspace, run Spark queries, create a Delta Lake pipeline and build an MLlib model and dashboard
Essential Level = Foundation + Unity Catalog, ingestion, medallion design and SQL warehousesDay 2 · 2 days cumulative
- Unity Catalog basics: metastore, catalogs, schemas, tables, volumes, managed versus external tables and GRANT statements
- Data ingestion: COPY INTO, Auto Loader (cloudFiles) with schema inference and evolution, reading CSV, JSON and Parquet
- Medallion architecture: bronze, silver and gold layers, naming conventions and data contracts between layers
- Delta Lake operations: MERGE INTO upserts, UPDATE and DELETE, time travel, DESCRIBE HISTORY and RESTORE
- Delta table maintenance: OPTIMIZE, liquid clustering, VACUUM retention and table properties
- Databricks SQL: SQL warehouses (serverless, pro, classic), SQL editor, saved queries and query history
- Lakeflow Jobs: multi-task workflows, task dependencies, parameters, schedules, retries and notifications
- Notebook utilities: dbutils for files, widgets and secrets, %run, magic commands and parameterised notebooks
- Compute choices: all-purpose versus job compute, serverless compute, cluster policies and Photon
- Git folders: cloning repos, branches, commits and pull requests from the workspace
- AI/BI dashboards and Genie spaces: datasets, visual widgets, filters, sharing and scheduled refresh
- Working with files: workspace files, Unity Catalog volumes and cloud object storage paths
Intermediate Level = Essential + declarative pipelines, CI/CD, streaming and governanceDay 3 · 3 days cumulative
- Lakeflow Declarative Pipelines (formerly Delta Live Tables): streaming tables, materialized views, expectations for data quality
- Structured Streaming: readStream and writeStream, checkpoints, triggers, watermarks and stateful aggregations
- Change data capture: Change Data Feed, APPLY CHANGES (AUTO CDC) and slowly changing dimensions type 1 and 2
- Databricks Asset Bundles: databricks.yml, targets for dev, staging and prod, bundle validate, deploy and run
- Databricks CLI and REST API: workspace automation, service principals and OAuth machine-to-machine tokens
- CI/CD: GitHub Actions or Azure DevOps pipelines that test notebooks, run pytest and deploy bundles
- Unity Catalog security: row filters, column masks, dynamic views, ownership, lineage and system tables for audit
- Lakehouse Federation and Delta Sharing: querying external databases and sharing data across organisations
- MLflow in depth: model registry in Unity Catalog, aliases, model signatures and Feature Engineering tables
- Model Serving: deploying real-time endpoints, batch inference with ai_query and monitoring request payloads
- Databricks Connect and VS Code extension: local development against remote compute
- Troubleshooting: Spark UI stages and tasks, data skew, out-of-memory errors, failed jobs and driver logs
Advanced Level = Intermediate + performance, cost, security and enterprise lakehouseDays 4-5 · 5 days cumulative
- Spark performance tuning: adaptive query execution, partition sizing, broadcast joins, caching and skew hints
- Photon and storage optimisation: predictive optimization, deletion vectors, file sizing and clustering key choice
- Cost management: system.billing tables, budgets, tagging, cluster policies, serverless versus classic cost trade-offs
- Workspace architecture: account console, multiple workspaces, metastore per region, environment isolation
- Network security: private connectivity, customer-managed VPC or VNet, IP access lists and customer-managed keys
- Identity at scale: SCIM provisioning, account groups, service principals and least-privilege entitlement design
- Data governance programme: Unity Catalog tags, attribute-based policies, lineage reviews and audit log monitoring
- Reliability: job repair runs, idempotent pipelines, disaster recovery with Deep Clone and cross-region replication
- Generative AI on Databricks: Vector Search, Foundation Model APIs, Mosaic AI Agent Framework and agent evaluation
- Lakehouse Monitoring: data and model drift profiles, inference tables and quality alerts
- Migration: moving Hive metastore tables to Unity Catalog with UCX, and legacy Hadoop or warehouse workloads to Databricks
- Capstone: deliver a governed, cost-tracked lakehouse with streaming ingestion, ML model serving and CI/CD
Choose live training or a full year of self-paced access.
If someone buys Foundation, Essential, Intermediate or Advanced as self-paced video, the price stays the same and access is granted to all four levels by default.
Self-paced video subscription
Billed yearly at ₹9,996. This includes all four levels, complete LMS access for one year, plus 20+ additional courses and 50+ tools access.
- All four Databricks levels in one price.
- 20+ additional courses and 50+ tools included, even when subscribing to one course.
- Best fit for learners who want full-library access and flexible progress.
Live & Interactive session
Instructor-led cohort, max 8 to 10 hours, with demos, labs, Q&A, assignments and practical review.
- Live small-batch cohort from soon.
- Interactive discussion and lab troubleshooting.
- Recommended for teams that need guided implementation.
1:1 Live training
The same agenda, taught one-to-one by the trainer. Start immediately, without waiting for the next batch. Max 8 to 10 hours, one-to-one.
- Start immediately: no waiting for the monthly batch.
- One learner, one trainer: full attention on your questions.
- Sessions planned around your working hours.
- Your pace, with examples from your own projects and tools.
Method of training and assessment.
The course emphasizes practical demonstration, hands-on labs and assignment-based validation instead of passive lecture-only learning.
| SL | Method of training and assessment | Weightage |
|---|---|---|
| 1 | Understanding the problem space | 5% |
| 2 | Concept discussion | 10% |
| 3 | Live demo and walkthrough | 25% |
| 4 | Lab and hands-on exercises | 50% |
| 5 | Assignments and evaluation | 10% |
Labs, exercises and real workflow practice.
Every level moves from explanation to hands-on practice, review and troubleshooting.
From raw files to a dashboard
Create a workspace and cluster, load a dataset with Spark, write it to Delta Lake, train and log a simple MLlib model in MLflow, and publish a dashboard.
Build a governed medallion pipeline
Ingest raw files with Auto Loader into bronze, clean them into silver with MERGE, aggregate gold tables in Unity Catalog and schedule it as a job.
Ship a pipeline through CI/CD
Define a streaming declarative pipeline with expectations in an Asset Bundle, test it in GitHub Actions and promote it from dev to prod.
Tune and govern a production lakehouse
Profile a slow job in the Spark UI, fix it with clustering and AQE, apply row filters and column masks, and report cost from system tables.
Databricks is taught as part of a working delivery ecosystem.
You learn where the tool sits in the wider engineering workflow, not in isolation.
Core foundations
Terminology, concepts, architecture and the operating model behind the tool.
Workflow traceability
Map practical work to tasks, approvals, documentation and delivery visibility.
Automation and integration
Use Databricks with validation checks, automation hooks and delivery workflows.
Governance controls
Operate with permissions, approvals, policy checks and production-safe workflows.
How your practical work is assessed.
Your certificate is backed by labs, assignments, capstone thinking and completion evidence.
Assignments
Level-specific assignments verify setup, configuration, workflow quality and operating discipline.
Hands-on validation
Learners are expected to complete practical usage, automation and troubleshooting exercises.
Completion record
Certificate issuance is tied to demonstrated participation, practical completion and course-level coverage.
Led by Rajesh Kumar and the DevOpsSchool practitioner network.
Training is grounded in practical DevOps, SRE, security, release and platform engineering experience with real enterprise delivery context.
Earn a DevOpsSchool Databricks certificate.
Completion includes a digital certificate with credential details, practical assignments and a learning record aligned to the selected Databricks level.
Certificate verificationCertificate of Completion
Databricks Advanced Training
Unique credential ID, verifiable online
What learners say about training with Rajesh Kumar.
“Rajesh is very good trainer. Rajesh was able to resolve our queries and question effectively. We really liked the hands-on examples covered during this training program.”
“Very well organized training, helped a lot to understand the AWS concept and detailed related to various tools. Very helpful”
“The training was very useful and interactive. Rajesh helped develop the confidence of all.”
Rated 4.6 on Google · All testimonials · Video reviews on YouTube
Quoted as published, from learners in Rajesh Kumar's live trainings across DevOpsSchool courses.
Used by developers, DevOps engineers, release leads and engineering managers.
Private batches are available for teams that want the same agenda aligned to their own tools and workflows.
Why this Databricks path is different.
The value is not only one course. The subscription unlocks the full learning ecosystem for one year.
| Feature | DevOpsSchool Databricks Training | Typical short course |
|---|---|---|
| Levels | Foundation, Essential, Intermediate, Advanced | Single generic syllabus |
| Self-paced access | All four levels plus 20+ courses and 50+ tools | Usually one course only |
| Live training | Demos, labs, Q&A and assignment review | Often lecture-heavy |
| Certificate | DevOpsSchool completion certificate | Varies by provider |
Questions learners ask before joining.
Does Foundation cost less than Advanced?
No. Self-paced pricing is intentionally the same because access includes all four levels by default.
What is included in the yearly subscription?
One year of complete LMS access, all four levels for the selected course, plus 20+ additional courses and 50+ tools access.
Can corporate teams request customization?
Yes. The live format can be aligned to team tools, governance requirements, workflows and internal delivery standards.
What is the recommended sequence?
Foundation → Essential → Intermediate → Advanced. The agenda is designed so each level builds on the previous one.
Talk to DevOpsSchool before choosing the batch.
Use this support section for batch questions, corporate planning, LMS access clarifications, or guidance on which Databricks level fits your team.
India / Worldwide: +91 99057 40781
USA: +1 (469) 756-6329
Email: contact@DevOpsSchool.com
Chat on WhatsAppExtend the course with articles, explainers and workflow guidance.
Continue learning with supporting articles that connect Databricks practices to DevOps, automation, governance and delivery workflows.
Training snapshots and certificate preview.
A look at the completion certificate and the course.
Certificate preview
See what course completion evidence looks like.
Tool-focused training
Course visuals reinforce the Databricks workflow and subject area.
Live batch experience
Training supports live online, private team and workshop-style delivery.
Start Databricks Advanced training.
Next weekday batch starts on the 1st; weekend batch on the first Saturday. Batches are capped at 10 learners. Send your details and DevOpsSchool will share the syllabus and batch options.