About Course
About Databricks Training
Delta Lake, Unity Catalog and jobs — plus the vector search and MLflow layer that makes the lakehouse where AI work actually runs.
The lakehouse is the substrate most analytics and AI workloads now sit on: Delta tables you can time travel, a Unity Catalog that knows who may read what, and jobs that refuse to publish data that fails its own checks. This course teaches that substrate as an engineering discipline rather than a console tour — schema evolution, file layout, concurrency and cost all behave differently at lakehouse scale, and those differences are what interviews and production incidents are actually about.
Course Overview
12-week Databricks training (72 hours) covering workspaces, Delta Lake, Unity Catalog governance, notebooks and SQL, jobs and pipelines, MLflow and vector search. Classroom, Live Online and Hybrid.
Why Learn Databricks Training
Databricks sits under a large share of enterprise data work in India, from services firms running a client's migration to in-house teams consolidating warehouses. Because the platform is broad, certified engineers are needed across ingestion, modelling, governance and machine learning rather than in one specialism, and employers use the credential as a quick signal of depth. It is one of the more portable data credentials available.
It also connects the data and AI halves of a career. Vector search, feature tables and MLflow all live on the same platform as your tables and jobs, so engineers who understand the whole surface are the ones asked to take a model into production rather than to hand it over. Governance knowledge compounds too: row filters, column masks and lineage are becoming the part of the job that carries the most responsibility.
Who Should Join This Course
- Data Engineers — Moving batch pipelines onto a lakehouse
- Spark Developers — Formalising Delta and Unity Catalog practice
- Analytics Engineers — Modelling in Databricks SQL with governance applied
- Machine Learning Practitioners — Using MLflow and vector search for AI workloads
- Fresh Graduates — Targeting the lakehouse engineering track
- Platform Administrators — Owning Unity Catalog, permissions and cost
Prerequisites
You should be comfortable with SQL joins and aggregates and able to read Python at a basic level. No Spark or Databricks experience is assumed. Prior work with any data warehouse, ETL tool or cloud object store helps in the modelling and migration material, and anyone who has operated a data pipeline will recognise the failure cases immediately.
Learning Objectives
- Build and optimise Delta tables including schema evolution and Z-ordering
- Apply Unity Catalog governance with catalogs, schemas and row filters
- Write notebooks and parameterise them for reuse in jobs
- Build an orchestrated job or pipeline with retries and dependencies
- Model and query in Databricks SQL with materialised views
- Track experiments and register models with MLflow
- Create a vector search index for a retrieval workload
- Debug a Spark job from the UI by reading the plan and the skew
What the Syllabus Covers
The syllabus is mapped to the published Databricks Data Engineer Associate objectives: Delta Lake, Unity Catalog, Databricks SQL, Jobs & Pipelines, MLflow, Vector Search together with Delta Lake table maintenance and time travel, Unity Catalog governance with row filters and lineage, multi-task jobs with data quality expectations, and MLflow with vector search for AI workloads.
Career Opportunities After This Programme
- Databricks Data Engineer
- Lakehouse Engineer
- Spark Developer
- Analytics Engineer (Databricks)
- ML Platform Engineer
- Data Platform Administrator
Hands-on Practice and Capstone
Capstone: Ingest raw files into Delta with schema enforcement and time travel, model the result in SQL with fact and dimension tables, govern it in Unity Catalog with grants, row filters and column lineage, build a multi-task job with data quality expectations, retries and alerting, add an MLflow-tracked model plus a vector search index, then deliver the runbook, cost estimate and access review.
Production scenario: A table that was 200 GB now holds 8 TB, the daily job runs for four hours and nobody knows which copy finance is reading. The build here answers all three: the transaction log proves which snapshot was queried, Z-ordering and compaction bring the scan back down, expectations reject malformed rows instead of poisoning downstream, and Unity Catalog makes the authoritative copy unambiguous.
Course Duration, Mode and Certification
The course runs for 12 weeks (72 hours), three sessions a week of two hours each. Modes: Classroom / Live Online / Hybrid. Certificate issued: SkilBrill Course Completion Certificate. The syllabus prepares you for the Databricks Data Engineer Associate credential, which is booked and paid for with Databricks separately from the course fee.
Why Train at SkilBrill
SkilBrill Training Institute in Thoraipakkam focuses on making you employable. Trainers are working AI, data and platform engineers, every module produces portfolio evidence rather than a completion badge, and the placement cell connects eligible candidates with hiring companies. Fee: ₹55,000 – ₹85,000. Enquire on +91 8610964691.
Career and Job Support
- Resume assistance tailored to AI, data and platform engineering roles
- LinkedIn profile rewrite and recruiter outreach
- Mock interviews covering evaluation, pipeline design and system-review scenarios
- Capstone walkthrough and portfolio review preparation
- Placement assistance with 200+ hiring partners
- Typical salary band after completion: ₹7 – 20 LPA
Frequently Asked Questions
What does Databricks training at SkilBrill cover?
Twelve weeks and 72 hours across eight modules: the lakehouse and workspace, Delta Lake in depth, Unity Catalog governance, notebooks and SQL modelling, jobs and pipelines, MLflow with vector search, applied practice, and a governed capstone pipeline.
Do I need to know Spark beforehand?
No. Spark is taught in the course, starting from how a job executes and how to read the UI, then moving into Delta specifics. Anyone who has written SQL or Python will follow along, and prior Spark exposure simply makes the optimisation module quicker.
Is this course also about AI?
The core is lakehouse engineering, which is why it sits in the Data Engineering category. One full module covers MLflow, model serving, foundation model endpoints and vector search, because the lakehouse is where AI workloads are increasingly run, and it cross-links to the LLM and MLOps courses.
How is Databricks different from Snowflake?
Both query structured data, but Databricks is built around Spark and Delta with notebooks and ML tooling attached, while Snowflake is a managed warehouse. Your site carries a Snowflake course, and the two are positioned as alternatives with different centres of gravity rather than competitors for the same job.
Is certification included?
The syllabus follows the Databricks Data Engineer Associate objectives, so the course doubles as exam preparation. The vendor exam is booked through Databricks' own programme, and the SkilBrill completion certificate is issued by us on the capstone.
What environment do I train on?
A working Databricks workspace with compute, Unity Catalog configured and jobs you can run and monitor, so schema evolution, expectations and the Spark UI are experienced rather than described.
What do I leave with?
A governed pipeline: Delta tables with time travel enabled, a modelled SQL layer, Unity Catalog grants with row-level filters, a multi-task job with data quality expectations and alerting, an MLflow-tracked model, and the runbook, cost estimate and access review.