Databricks Course in Hyderabad
Build lakehouse pipelines with Spark, Delta Lake and Unity Catalog on Databricks
The Databricks course teaches you to build and operate data pipelines on the Databricks Data Intelligence Platform. You start with SQL, Python and the lakehouse concept, then work with workspaces, clusters and notebooks, Apache Spark DataFrames and Spark SQL, Delta Lake tables with ACID transactions, time travel and MERGE, and Auto Loader for incremental ingestion. You build medallion pipelines with Lakeflow Spark Declarative Pipelines (the evolution of Delta Live Tables), orchestrate them with Lakeflow Jobs, govern data with Unity Catalog, query it with Databricks SQL and build dashboards, and package work with Git folders and Databricks Asset Bundles. A closing module introduces MLflow and AI features at an overview level. The topics are relevant to the Databricks Certified Data Engineer Associate exam, whose guide Databricks revised in May 2026, so a counsellor will confirm the current outline. The course suits graduates and data professionals with basic SQL; Python is taught from the basics. Classes run in Ameerpet, Hyderabad, and in live online batches.
Syllabus
Lakehouse & Databricks Platform Basics
- •Data warehouse, data lake and lakehouse compared
- •Databricks workspace, notebooks, clusters, SQL warehouses and serverless compute
- •Cost basics: DBUs, auto-termination and compute sizing
- •Lab: Create a workspace and run a first notebook
- •Outcome: Navigate Databricks and pick the right compute
SQL & Python Refresher
- •SQL queries, joins, CTEs and window functions
- •Python functions, collections and pandas basics
- •File formats: CSV, JSON, Parquet and Delta
- •Lab: Explore a dataset with SQL and Python in a notebook
- •Outcome: Use SQL and Python confidently in notebooks
Apache Spark on Databricks
- •Spark architecture: driver, executors, jobs, stages and tasks
- •DataFrame API: select, filter, withColumn, join, groupBy and window
- •Spark SQL and temporary views; UDFs and when to avoid them
- •Lab: Transform a multi-million-row dataset in PySpark
- •Outcome: Process data at scale with Spark
Delta Lake
- •Delta tables, transaction log and ACID transactions
- •Schema enforcement and evolution; constraints
- •MERGE, DELETE, UPDATE, time travel and restore
- •OPTIMIZE, VACUUM, Z-ordering and liquid clustering
- •Lab: Build an upsert pipeline with MERGE
- •Outcome: Build reliable, queryable lakehouse tables
Incremental Ingestion with Auto Loader
- •Cloud storage ingestion patterns
- •Auto Loader: file notification, schema inference and evolution
- •COPY INTO versus Auto Loader; Structured Streaming basics
- •Lab: Ingest arriving JSON files incrementally
- •Outcome: Load new files automatically and exactly once
Medallion Architecture & Lakeflow Declarative Pipelines
- •Bronze, silver and gold layer design
- •Declarative pipelines: streaming tables, materialised views and expectations
- •Change data capture with AUTO CDC APIs
- •Lab: Build a declarative pipeline with data quality expectations
- •Outcome: Build maintainable, monitored pipelines
Orchestration with Lakeflow Jobs
- •Jobs, tasks, task dependencies and triggers
- •Parameters, retries, repair runs and notifications
- •Scheduling and file-arrival triggers
- •Lab: Orchestrate an ingest, transform and publish workflow
- •Outcome: Run pipelines on a dependable schedule
Unity Catalog & Governance
- •Unity Catalog hierarchy: metastore, catalog, schema and objects
- •Privileges, grants, row filters and column masks
- •Volumes, external locations and lineage
- •Lab: Secure a schema for analyst and engineer groups
- •Outcome: Govern data access centrally
Databricks SQL & Dashboards
- •SQL warehouses and query performance basics
- •Dashboards, alerts and scheduled refresh
- •Connecting Power BI and Tableau
- •Lab: Build a sales dashboard from gold tables
- •Outcome: Serve analytics from the lakehouse
Performance & Cost Optimisation
- •Reading the Spark UI; shuffles, skew and spills
- •Adaptive query execution, partitioning and file sizes; Photon
- •Cluster sizing, Spot instances and cost monitoring
- •Lab: Diagnose and tune a slow job
- •Outcome: Find and fix common performance issues
DevOps: Git, Asset Bundles & CI/CD
- •Git folders and branching for notebooks and code
- •Databricks Asset Bundles for deployable projects
- •Unit testing PySpark code and CI/CD with GitHub Actions
- •Lab: Deploy a bundle to dev and test targets
- •Outcome: Promote pipelines like software
MLflow & AI Overview
- •MLflow experiment tracking and model registry basics
- •Overview of Mosaic AI and AI/BI features
- •How data engineers support data science teams
- •Lab: Track a simple model training run
- •Outcome: Understand how pipelines feed ML and AI
Capstone & Exam Readiness
- •Capstone: ingest, transform, govern, orchestrate and serve a lakehouse
- •Review of Data Engineer Associate topics with practice questions
- •Portfolio write-up and interview practice
- •Outcome: A portfolio lakehouse project and a study plan for the vendor exam
Tools Covered
Projects Included
- Retail Lakehouse: Ingest raw sales files with Auto Loader and build bronze, silver and gold Delta tables; deliverable is a pipeline with a data dictionary.
- CDC Upsert Pipeline: Apply change data to a dimension table with MERGE or AUTO CDC; deliverable is a tested upsert notebook.
- Quality-Checked Declarative Pipeline: Add expectations and a quarantine path for bad records; deliverable is a pipeline and quality report.
- Governed Analytics Schema: Configure Unity Catalog roles, masks and row filters; deliverable is a permission matrix and tests.
- Capstone Lakehouse Platform: Orchestrate the pipeline with Jobs, deploy with bundles and publish a dashboard; deliverable is a GitHub repository and architecture diagram.
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Call: +91 9908099980Prerequisites & Eligibility
Understand the foundation and skills needed to succeed in the Databricks course.
Course Prerequisites
- Any graduate is eligible for enrollment across all program tracks
- Dedication to work on real-time scenario projects and live client project implementations
- Willingness to engage in aptitude, soft skills and interview readiness sessions
- Commitment to complete full-day training or intensive/internship program schedules
Why Choose GloryTecks
Build practical capabilities through structured learning, expert mentors and hands-on projects.
5+ Years of Excellence
Established training institute in Hyderabad with a proven track record of producing industry-ready professionals.
Expert Faculty
Learn from trainers with real-world industry experience, who teach the way the work is actually done.
Placement Assistance
A dedicated placement team with tie-ups across hiring companies. We support you until you get hired.
Comprehensive Curriculum
Updated syllabus covering the latest technologies and industry best practices, with hands-on projects.
Career Growth Focus
Not just training, but complete career transformation with soft skills and interview preparation.
Online & Classroom
Attend live online batches or classroom training at our Ameerpet centre, whichever suits you.
Flexible Batches
Weekday, weekend and online batch timings to suit working professionals, students and freshers.
Real-time Projects
Work on real-time scenario projects so you finish with practical experience to show employers.
We provide placement support with direct access to hiring companies, along with comprehensive training that helps freshers become job-ready professionals.
Certification
Explore certification and practical learning opportunities with a focus on Databricks.
Certificate of Completion
Databricks
This certifies that
[Your Name]
has successfully completed the
Databricks
Training Program
GloryTecks
Ameerpet, Hyderabad
Certificate ID
GT-YYYY-XXXX
Certificate Includes
- Course completion certificate from GloryTecks
- Project completion certificates
- Internship completion certificate for eligible internship programs
- Digital certificate with a unique verification ID
- Shareable on LinkedIn and other professional platforms
- Valid proof of skill acquisition for employers
- Includes the detailed syllabus covered
Industry Focus
A certificate built around the tools and skills employers ask for, adding credibility to your resume.
Skill Validation
Proves your expertise in the course and the technologies you worked with.
Career Advancement
Strengthens your profile when you apply for jobs and negotiate your salary.
Digital & Physical
Get a digital certificate for online sharing and a physical copy for framing.
Course Certificate
Upon successful completion of the training program
Project Certificate
For major projects completed during training
Course Features & Highlights
Build a strong foundation through a structured Databricks course with practical learning and hands-on exposure.
Live Projects
Work on real-world projects from day one with industry use cases.
Expert Trainers
Learn from industry professionals with real-world experience.
Placement Support
Dedicated placement team with assistance until you get hired.
Industry Certification
Get certified and boost your resume with recognized credentials.
LMS Access
Access to learning materials and recorded sessions.
Mock Interviews
Weekly mock interviews to prepare you for real job scenarios.
Resume Building
Professional resume preparation and LinkedIn profile optimization.
Additional Benefits
- Flexible Payment Plans
- 6-12 Months LMS Access
- Doubt Clearing Sessions
- 24/7 Learning Support
Frequently asked questions
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Best Databricks Training in
Ameerpet, Hyderabad
Located at Ameerpet — Hyderabad’s IT training hub. Easily accessible from all areas of Hyderabad. Just 2 minutes walk from Ameerpet Metro Station.
Placement Support
Career support covers resume and LinkedIn preparation, mock interviews and a portfolio of 5 course projects. Roles that use these skills include Databricks Data Engineer, Data Engineer, Spark Developer, Analytics Engineer; support is guidance with the job search, not a guarantee of a job.