Data Engineering with Apache Spark and Databricks Corporate Training
Build production-grade data pipelines with Apache Spark and Databricks, from ingestion to tuned delivery.
Edstellar, a corporate training company, delivers corporate training that helps your team build production-grade data pipelines with Apache Spark and Databricks. Employees learn ingestion, transformation, and tuning through guided labs led by practitioners, from DataFrames and Spark SQL to Delta Lake and structured streaming. Each session is tuned to your data sources, platform, and workflows.
Instructor-led group trainingVirtual / on-site / off-site10 languagesDelivered inEnglish, Spanish, Mandarin, German, Arabic, Portuguese, Hindi, French, Japanese, Italian100+ countriesWe have delivered inUnited States, United Kingdom, India, Canada, Germany, France, Netherlands, UAE, Saudi Arabia, Singapore, Australia, Japan, Brazil, South Africa and 90+ more countries
How we build a pipeline-driven data team
The Edstellar DATA ENGINEERING Capability Model
Diagnose pipeline gaps
Skill checks map your team's SQL fluency, Spark basics, and pipeline habits.
Standardize data practices
One shared playbook for ingestion, modeling, and tuning conventions.
Practice in guided labs
Labs cover DataFrames, Delta Lake, tuning, and streaming pipelines.
Measure the lift
Lab reviews benchmark each engineer's pipeline quality and job performance.
Scale across teams
One pipeline standard rolls out across functions, regions, and business units.
Need more than one program?Get a group quote
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Program Overview: What is Data Engineering with Apache Spark and Databricks?
Data Engineering with Apache Spark & Databricks is an instructor-led corporate training program for teams moving from fragile scripts and ad-hoc notebooks to reliable production pipelines. Over 24 hours, participants learn to ingest and clean business data, build Spark transformations, model Delta Lake tables, tune job performance, and run streaming pipelines. Everything is taught inside Databricks workspaces, on workloads that mirror their real jobs.
Each module blends concise instruction with guided labs that mirror day-to-day engineering: building ingestion jobs, tuning slow stages, modeling lakehouse tables, and wiring structured streaming. Instructors review every lab, coaching teams through Spark UI diagnostics, Delta Lake versioning choices, and orchestration patterns before the cohort moves on. Practice compounds across the week toward the capstone, where teams build a complete lakehouse pipeline. Feedback targets the team's own data shapes and cluster configurations.
Notebook sprawl and hand-rolled scripts break the moment scale arrives: jobs fail overnight, reruns double-count revenue, and nobody can explain what a pipeline actually does. This program replaces that fragility with engineered habits, tested transformations, observable jobs, and storage designed for reruns. Teams return building pipelines they can hand to an on-call engineer without apology.
The capstone asks each team to deliver a working end-to-end lakehouse pipeline in Databricks, from ingestion through transformation to queryable Delta tables, with streaming wired in where it matters. Instructors grade it the way production grades it: does it recover from failure, does it run fast enough, and can someone else read it. Graduates leave with a tuned pipeline template, a performance checklist, and the confidence to throw away the fragile scripts for good.
Six modules and 24 hours: Spark and Databricks setup, DataFrames and ingestion, transformation and tuning, Delta Lake modeling, streaming and orchestration. Every module pairs concepts with a guided lab, so each technique is practiced. It closes with a capstone lakehouse pipeline the team plans, builds, and presents.
Delivery is hybrid across virtual and onsite sessions, scheduled around your calendar. Cohorts train as one group or split into batches, with session materials and lab guides included for every participant.
What's Included in this Workshop for Your Enterprise Teams
- 1
Spark and Databricks workspace setup
Provision clusters and notebooks and run first distributed jobs.
- 2
DataFrames and ingestion labs
Ingest and clean multi-format sources into reliable tables.
- 3
Tuning and transformation practice
Diagnose slow jobs and apply proven optimization patterns.
- 4
Delta Lake modeling sessions
Build bronze, silver, and gold tables with quality checks.
- 5
Streaming and orchestration
Run streaming pipelines and schedule workflows with monitoring.
- Instructor-led delivery across virtual, onsite, and hybrid
- Guided labs in every module with instructor coaching
- Capstone lakehouse pipeline, reviewed with your team
- Flexible scheduling around your calendar
- Lab guides and reusable pipeline templates included
- Group size and batches set along with you
Build your team's skills. Anywhere in the world.
Real-world use cases
from day one
Instructor-led training
for your teams
Delivered globally
in 100+ countries
Enterprise Training Solutions Customized for Your Company
One partner for instructor-led corporate training, delivered the way your teams work.
Request a Training Quote- Customized Training Delivery
- Scale Your Training: Small to Large Teams
- In-person Onsite, Live Virtual or Hybrid Training Modes
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Data Engineering with Apache Spark and Databricks Course Topics and Program Outline
A tailored 24-hour, six-module workshop: one learning journey from Spark setup to a capstone lakehouse pipeline. Sessions pair concepts with labs on real business data. Materials include lab guides and reusable pipeline templates for every participant.
Built by Edstellar with data engineering practitioners
Built by practitioners who ship Spark and Databricks pipelines in production, shaped around your stack and use cases.
Skill checks baseline your team's SQL, Spark familiarity, and pipeline experience before training begins.
Live instruction covers Spark foundations, DataFrames, and ingestion across 6 focused hours.
Guided labs build transformation, tuning, and Delta Lake skill across 8 supervised hours.
Your team ships a capstone lakehouse pipeline in 10 hours, reviewed for design and performance.
From Spark setup to a capstone lakehouse pipeline, every technique is practiced in guided labs.
Delivered as
Delivered as a hybrid program of live instruction and guided labs, scheduled around your team's calendar in flexible batches.
Provision a Databricks cluster, configure notebooks, and run a first distributed Spark job on sample datasets in a guided walkthrough.
learna)Spark architecture: driver, executors, and jobs
b)Databricks workspace, clusters, and notebooks
c)Connecting to files, object storage, and tables
d)Jobs, scheduling, and notebook workflows
Ingest messy multi-format business files, clean types, and land validated tables with partitioning in a guided data prep lab.
learna)Reading CSV, JSON, Parquet, and cloud files
b)DataFrames vs Spark SQL: when to use each
c)Cleansing, casting, and handling bad records
d)Writing tables and partitioning basics
e)Schema inference and validation checks
Diagnose a slow Spark job, tune partitions and caching, and cut runtime with broadcast joins in a performance-tuning lab.
practicea)Joins, aggregations, and window functions
b)Caching, persistence, and storage levels
c)Shuffle control and partition sizing
d)Reading the Spark UI and stage plans
e)Broadcast joins and skew handling
Build bronze, silver, and gold Delta tables with MERGE upserts, time travel, and quality constraints in a guided lakehouse lab.
practicea)Delta Lake tables and ACID guarantees
b)Bronze, silver, and gold layer design
c)Time travel and versioning
d)MERGE upserts and change data capture
e)Data quality expectations and constraints
Build a streaming pipeline into Delta tables with windowing and watermarks, then orchestrate it in scheduled workflows.
applya)Structured Streaming sources and sinks
b)Event time, watermarks, and windowing
c)Exactly-once semantics with Delta
d)Databricks workflows and job orchestration
e)Monitoring, retries, and failure alerts
Design and build a complete lakehouse pipeline end to end, then present the architecture, tuning results, and handover notes.
applya)Scoping the pipeline on real business data
b)Ingestion, transformation, and modeling build
c)Performance tuning and testing
d)Demo, review, and handover documentation
Customize this program tailored to your team?
Tell us your training needs: what your team already works with, the level they’re at, and the topics you want them to focus on. We’ll adjust the modules, duration and hands-on labs, and send you a customized outline for your group.
Key Skills and Outcomes Your Team Will Gain
Seven job-ready outcomes your team will demonstrate by the end of the program. Each competency is practiced in guided labs and reviewed in the capstone build.
Provision Spark and Databricks workspaces
Provision clusters and notebooks and connect to common data sources.
Build DataFrames and Spark SQL pipelines
Ingest, clean, and transform data with DataFrames and Spark SQL.
Tune Spark job performance
Diagnose slow stages and apply partitioning, caching, and join strategies.
Model data in Delta Lake
Design bronze, silver, and gold Delta tables with quality constraints.
Build streaming pipelines
Run Structured Streaming jobs into Delta with watermarks and windowing.
Orchestrate and monitor workflows
Schedule jobs, handle retries, and monitor pipeline health end to end.
Deliver a production lakehouse pipeline
Combine ingestion, modeling, and tuning into a reviewed capstone pipeline.
Teams that Benefit from this Data Engineering with Apache Spark and Databricks Group Training
This group training is built for data and analytics teams that own pipelines, reports, and data platforms and want production-grade Spark and Databricks skill. Content adapts to each cohort's roles, tools, and experience, so every batch practices the workflows it owns, from ingestion to delivery.
Ship production Spark pipelines with confidence.
Data engineers practice ingestion, transformation, and tuning in labs that mirror production pipeline work.
Workflow friction
- Slow jobs with unclear bottlenecks
- Brittle pipelines on messy sources
- Unmodeled data nobody trusts
How the program helps
- Reliable ingest and transform patterns
- Faster jobs through proven tuning
- Reusable lakehouse templates
Practice use cases
- Ingestion labs
- Tuning drills
- Delta modeling labs
Feed dashboards with trusted modeled data.
BI engineers learn to build the clean Delta tables and models that keep dashboards accurate.
Workflow friction
- Manual spreadsheet-heavy prep
- Broken joins and bad measures
- Dashboards leaders question
How the program helps
- Accurate models for reporting
- Consistent metrics across teams
- Faster refresh and delivery
Practice use cases
- Modeling labs
- Metric definition drills
- Pipeline handoff labs
Get reliable features for models.
Data science teams practice building the feature pipelines and Delta tables that models depend on.
Workflow friction
- Inconsistent feature definitions
- Stale or untracked datasets
- Ad-hoc notebooks not production
How the program helps
- Clean, versioned training data
- Reproducible feature pipelines
- Faster experimentation cycles
Practice use cases
- Feature pipeline labs
- Versioning drills
- Experiment data labs
Cohorts train as a cross-functional workforce, with each small group aligned on shared pipeline standards and code quality.
Prerequisites: This program benefits data engineers, BI engineers, and analytics teams who work with pipelines and data platforms. Basic SQL comfort helps but is not required, and no prior Spark experience is needed. Content adapts to each cohort's data sources, platform, and experience level.
Data engineer to senior data engineer
A program that grows with data engineering careers, from first Spark job to confident production delivery.
A program that grows with data engineering careers, from first Spark job to confident pipeline delivery.
Mixed cohort, or several teams at once?
We can tailor the training to different teams, roles, and experience levels within the same program.
Certificate of Training Completion.
On successful completion, employees receive an Edstellar course completion certificate, recognizing their commitment to ongoing learning and professional development. It validates the skills they have built and motivates them to keep growing and contribute to organizational success.
Earned, not attended. Awarded on completion of all hands-on labs and the capstone project.
Training Delivery Modes: Virtual, Onsite and Hybrid
Live instructor-led delivery with guided labs in every module, scheduled around your team's calendar in flexible batches.
Hybrid Training
Instructor-led sessions mix onsite and virtual delivery with guided labs throughout. Teams build pipelines on their own data while the instructor coaches and reviews work. Hybrid keeps scheduling flexible without losing live interaction.
- Live instruction across onsite and virtual sessions
- Guided labs with instructor coaching and reviews
- Real business data in every practice exercise
- Flexible scheduling without losing live interaction
Onsite Training
Instructor-led training at your offices, with labs set up on your platform and data. The instructor works with the team in person, reviews pipeline drafts together, and tunes exercises to your data and workflows.
- Instructor-led training at your offices
- Labs set up on your platform and data
- In-person reviews of pipeline drafts
- Exercises tuned to your data and workflows
Live Virtual Training
Live virtual sessions with screen-shared instruction and guided labs in your own environment. The instructor coaches through chat and breakout reviews, keeping every participant building.
- Live virtual instruction with screen sharing
- Guided labs in your own environment
- Breakout reviews with the instructor
- Same curriculum and coaching as onsite
Need a training format that works for your team?
Full days, half days across weeks, or split by module. Tell us the delivery window and we schedule the cohort around it.
Frequently Asked Questions
Share your team size, timelines, and delivery preference, and we will send a detailed per-group quote with batch options.
Yes. Book a discovery call and we will map your data sources, Spark experience, and goals to a tailored plan.
Yes. Live virtual sessions run with the same guided labs and instructor coaching as onsite delivery.
Yes. The instructor runs the full program at your offices, with labs set up on your tooling and data.
The program runs 24 hours of instructor-led training, usually across three to six days or weekly sessions on a flexible schedule.
Question not answered here?
Put it in the request. A training specialist answers it directly, within one business day.
Where this Training Fits in Your Team's learning path.
Spark and Databricks engineering builds on Python and PySpark, so they come first. Once your team can engineer on Databricks, Delta Lake adds reliable tables and turns the platform into a lakehouse.
Why Choose Edstellar as Your Data Engineering with Apache Spark and Databricks Corporate Training Provider
When comparing providers, Edstellar is the training partner teams choose for data engineering expertise and 5+ years of corporate training experience. Our methodology blends concise instruction with guided labs, and we share each trainer profile before booking. The result is pipelines your business can rely on.
EXPERT TRAINERS
YEARS DELIVERING
ORGANIZATIONS TRAINED
CERTIFIED
Tailored
Every cohort's labs, datasets, and examples are tuned to your data sources, Spark setup, and engineering goals.
Proven Methodology
Concise instruction followed by guided labs and reviews, a repeatable method that turns pipelines into trusted output.
Practice-Based Learning
Six modules of guided Spark and Databricks labs and a capstone pipeline keep your team building, not just watching.
Expert Trainers
Practitioners who build production data pipelines lead every session and review your team's work.
Global Scale
One consistent data engineering program delivered across your offices worldwide, in 10+ languages.
Measurable Outcomes
Lab checkpoints and a capstone review prove pipeline gains you can report to leadership.
Build Databricks Lakehouses With Validated Data and Measured Spark Performance
Edstellar connects distributed-processing foundations with DataFrames, Delta tables, and streaming workflows. Participants clean multi-format data, investigate slow jobs, and manage incremental changes before integrating a lakehouse pipeline. The final review examines correctness, performance evidence, streaming behavior, and handover documentation that explains how the components work together.
- Step 01
Consult
We explore pipeline responsibilities and SQL confidence to identify gaps in distributed processing, data preparation, Delta operations, streaming semantics, and job tuning.
- Step 02
Plan
We establish Spark and workspace foundations before DataFrames and performance, then Delta Lake layers and streaming orchestration within the complete lakehouse pipeline.
- Step 03
Align
We align Spark instruction with data, BI, and analytics roles, providing distributed-computing foundations and appropriate Databricks resources for the cohort's experience.
- Step 04
Customize
We tailor messy source files, incremental updates, and event streams to relevant workloads while retaining quality constraints and explicit performance and lateness assumptions.
- Step 05
Execute
Participants tune joins and partitions, apply Delta upserts, and configure watermarks, receiving feedback on correctness, execution evidence, and handling of incremental data.
- Step 06
Evaluate
We assess the pipeline for validated data layers, justified tuning, coherent streaming and orchestration, and documentation explaining design choices and operational assumptions.
Also from Edstellar
Talent assessments
Psychometric, behavioral, leadership and 360-degree assessment.
→Training needs analysis
Needs analysis, gap identification, and a training roadmap.
→L&D consulting
Learning strategy, content, technology and ROI measurement.
→OD consulting
Org design, succession planning and cultural change.
→Managed training services
Training outsourcing, vendor management, logistics and administration.
→Coaching solutions
Executive and manager coaching that keeps skills in use.
Start an RFP shaped to your needs.
Answer three quick questions about team size, scope, and how often you need the program run, then send the request from the last step. Everything you choose travels with it, so you only fill this in once.
How many team members need training?
Data Engineering with Apache Spark & Databricks Training Training Packages & Pricing
Where L&D holds an annual budget, license packages price the whole year at once instead of raising a purchase order per workshop. Hours and licenses are pooled across teams, programs and locations, so unused capacity is not stranded in one department.
Starter
120
trainee licenses64 hours of group training
Tailored for SMBs
Growth
320
trainee licenses160 hours of group training
Ideal for growing SMBs
Enterprise
800
trainee licenses400 hours of group training
Designed for large corporations
Custom
Unlimited
trainee licensesDuration set by the rollout
Multi-region and multi-year
Training Packages & Pricing
Choose the training format that works for your team. Pricing is customized based on your requirements, cohort size, and delivery needs.
Request Data Engineering with Apache Spark and Databricks Training for Your Team
Share your team size, data sources, platform, and timelines to receive a tailored training plan and quote. Book a discovery call to discuss your goals; we confirm availability and respond with a proposal.