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CoursesData Engineering
Databricks Data Engineering
Data Engineering

Databricks Data Engineering

Learn Databricks Data Engineering with Apache Spark, PySpark, Delta Lake, and data pipelines. This Databricks Training helps aspiring data engineers and working professionals develop practical data processing skills and prepare for Databricks certification.

5/5(4,890 Reviews)

Level

Advanced

Duration

8 Weeks

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About Databricks Data Engineering

Databricks Data Engineering Training helps learners develop practical skills in building, managing, and optimizing data pipelines using the Databricks Lakehouse Platform. This Databricks Data Engineering Course covers Apache Spark, PySpark, Spark SQL, Delta Lake, data ingestion, and data transformation. Learn to process large datasets, develop ETL pipelines, implement batch and streaming workflows, and organize data using Bronze, Silver, and Gold layers. The course introduces lakehouse architecture, data quality, workflow orchestration, and performance optimization for modern data engineering environments. Databricks Online Training is suitable for aspiring data engineers, IT professionals, data analysts, and working professionals seeking to expand their technical skills. Learners also explore concepts relevant to the Databricks Certified Data Engineer Associate certification through practical exercises and projects. Build a foundation in Data Engineering with Databricks and understand how scalable data pipelines support analytics and business intelligence.

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Training Plan

01
About trainer

About trainer

Working professional who is carrying more than 10 years of industry experience.

02
Decks & Updated Content

Decks & Updated Content

Access to updated presentation decks shared during live training sessions.

03
e-Book

e-Book

E-book provided by TechPratham. All rights reserved.

04
Assignments & MCQs

Assignments & MCQs

Module-wise assignments and MCQs provided for practice.

05
Video Recording

Video Recording

Daily Session would be recorded and shared to the candidate.

06
Projects

Projects

Live projects will be provided for hands-on practice.

07
Resume Building

Resume Building

Expert-guided resume building with industry-focused content support.

08
Interview Preparation

Interview Preparation

Comprehensive interview preparation with real-time scenario practice.

Databricks Data Engineering Course Curriculum

Module 1: Introduction to Databricks and Data Engineering

Understand data engineering fundamentals and the Databricks Lakehouse Platform.

Introduction to Data Engineering
Databricks Workspace and Notebooks
Lakehouse Architecture
Data Lakes and Data Warehouses
Databricks Compute Fundamentals

Module 2: Apache Spark Fundamentals

Learn distributed processing and Spark architecture.

Spark Architecture
Driver and Executors
RDDs and DataFrames
Transformations and Actions
Jobs, Stages, and Tasks

Module 3: PySpark Programming

Process and transform structured data using PySpark.

PySpark DataFrames
Schemas and Data Types
Filtering and Selecting
Joins and Aggregations
Handling Missing Values

Module 4: Spark SQL

Use SQL for data querying and transformation.

Spark SQL Fundamentals
Views and Tables
Joins and Subqueries
Window Functions
Query Execution

Module 5: Data Ingestion and ETL

Build workflows to ingest, clean, and transform data.

CSV, JSON, and Parquet
Batch Data Ingestion
ETL and ELT Concepts
Incremental Data Loading
Data Cleaning and Validation

Module 6: Delta Lake

Manage reliable data tables and transactions with Delta Lake.

Delta Tables
ACID Transactions
Delta Transaction Log
MERGE, UPDATE, and DELETE
Schema Evolution and Time Travel

Module 7: Lakehouse and Medallion Architecture

Organize data pipelines using layered data architecture.

Bronze, Silver, and Gold Layers
Data Modeling
Data Quality
Data Lineage
End-to-End Pipeline Design

Module 8: Lakeflow and Pipeline Development

Understand Databricks pipeline development and orchestration concepts.

Lakeflow Fundamentals
Pipeline Development
Incremental Processing
Pipeline Dependencies
Monitoring and Troubleshooting

Module 9: Streaming Data Processing

Explore real-time and near-real-time data processing.

Structured Streaming
Streaming Sources and Sinks
Checkpointing
Event-Time Processing
Streaming Aggregations

Module 10: Jobs and Workflow Orchestration

Schedule, execute, and monitor data engineering workflows.

Databricks Jobs
Task Dependencies
Workflow Scheduling
Retry and Failure Handling
Job Monitoring

Module 11: Optimization, Governance and Security

Improve pipeline performance and understand data governance.

Partitioning and Shuffle
Join Optimization
Query Execution Plans
Delta Table Maintenance
Unity Catalog and Permissions

Module 12: Capstone Project and Certification Preparation

Apply data engineering concepts through projects and certification-focused learning.

End-to-End Data Pipeline
PySpark Transformations
Delta Lake Implementation
Data Quality Checks
Databricks Associate Exam Concepts

Data Engineering Courses

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Additional Program Highlights

Learning Materials

Comprehensive study materials and resources

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Resume Writing

Professional resume building session

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Interview Preparation

Master your interview skills

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Live Project Demo

Real-world project demonstrations

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Who Should Take Databricks Data Engineering Course?

IT Professionals

Non-IT Career Switchers

Fresh Graduates

Career Opportunities After Learning Databricks Data Engineering

Data Engineer

Databricks Data Engineer

Cloud Data Engineer

Key Projects

Databricks Data Engineering

Walmart

Walmart – Retail Sales ETL


Scenario: Build a retail data pipeline for sales analytics in a simulated Walmart environment using Databricks.

Live Work:

  • Ingest sales data using Auto Loader.
  • Transform data using PySpark and Delta Lake.
  • Create Bronze, Silver, and Gold tables.
Outcome: Build a retail data pipeline.
Metro Bank

Metro Bank – Banking Data Pipeline


Scenario: Develop a banking data pipeline for secure customer data processing and analytics using Databricks.

Live Work:

  • Ingest customer records into Delta tables.
  • Validate data using PySpark and Spark SQL.
  • Apply access controls using Unity Catalog.
Outcome: Build a secure banking data pipeline.
Deloitte

Deloitte – Customer Analytics ETL


Scenario: Develop a customer analytics pipeline for processing customer and transaction data in a simulated enterprise environment.

Live Work:

  • Load customer data into Databricks.
  • Transform datasets using PySpark and Delta Lake.
  • Schedule and monitor jobs using Databricks Workflows.
Outcome: Build an automated analytics pipeline.
Infosys

Infosys – Supply Chain Data ETL


Scenario: Build a supply chain data pipeline for inventory and shipment analytics using Databricks.

Live Work:

  • Ingest inventory data into Delta tables.
  • Transform shipment data using PySpark.
  • Schedule pipelines using Databricks Workflows.
Outcome: Build a supply chain data pipeline.
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Our Success Mantra

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Commitment

  • Ensuring quality training every day

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Fulfillment

  • Meeting learning goals with confidence

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Accomplishment

  • Students achieving industry-ready expertise

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Beyond Courses:

Additional Support We Provide

24/7 Support

LinkedIn Profile

Resume Writing

Alumni Sessions

Interview Preparation

Live Projects

What is Databricks Data Engineering?

Who can enroll in the Databricks Data Engineering course?

Do I need programming experience to learn Databricks?

What technologies are covered in Databricks Data Engineering training?

Is Databricks Data Engineering suitable for working professionals?

What is the Databricks Lakehouse Platform?

What is Apache Spark, and why is it used in Databricks?

What is the difference between Spark transformations and actions?

What is the difference between a Spark DataFrame and an RDD?

What is Delta Lake, and how does it differ from a regular data lake?

What is the purpose of the Bronze, Silver, and Gold layers?

What is Auto Loader in Databricks?

Databricks Certified Data Engineer Associate

The Databricks Certified Data Engineer Associate is an official vendor certification offered by Databricks. It validates foundational skills in using the Databricks Data Intelligence Platform for data ingestion, transformation, pipeline orchestration, and data engineering tasks.


Certification Details



  • Certification Name: Databricks Certified Data Engineer Associate
  • Certification Provider: Databricks
  • Certification Level: Associate
  • Exam Format: 45 scored multiple-choice questions
  • Exam Duration: 90 minutes
  • Exam Fee: USD 200, plus applicable taxes
  • Exam Delivery: Online proctored or at a test center
  • Prerequisites: No formal prerequisites
  • Recommended Experience: Six months of hands-on Databricks experience is recommended
  • Certification Validity: Two years
  • Passing Score: Databricks does not publish a numeric passing score; candidates must meet the vendor's passing criteria.


TechPratham Course Completion Certificate

Learners who successfully complete the Databricks Data Engineering training requirements receive a TechPratham course completion certificate, subject to the institute's applicable completion criteria.

The TechPratham certificate is separate from the Databricks Certified Data Engineer Associate credential. The official Databricks certification is awarded by Databricks only to candidates who pass its certification examination.

The training provides learning and practical project experience relevant to the certification objectives. Enrollment or completion of the TechPratham course does not guarantee passing the official Databricks examination or receiving the vendor certification.


Industry-Recognized Certification

Certificate
Databricks Data Engineering Certificate

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