Databricks Courses By SQL SCHOOL

SQL School offers three core Databricks programs:

• Databricks Data Engineer: hands-on pipeline and workflow development on the Lakehouse platform
• Databricks Data Analyst: SQL analytics, dashboards, and data exploration using Databricks SQL
• Databricks Data Engineer Associate Certification:  exam-focused prep for the official Databricks Associate badge

All programs are job-oriented with real-time project experience.

These courses are designed for:

• Aspiring or working Data Engineers who want to build ETL/ELT pipelines on Spark and Delta Lake
• Data Analysts moving to cloud-based SQL analytics
• Professionals targeting the Databricks Certified Data Engineer Associate exam
• IT graduates looking to enter the data engineering domain

Basic SQL knowledge is helpful; prior Spark experience is not required for beginner tracks.

For the Data Engineer course, it helps to have:

• Basic understanding of SQL and relational databases
• Familiarity with Python (optional but beneficial)
• Some exposure to cloud concepts (Azure, AWS, or GCP)

The Data Analyst course requires comfort with SQL queries. The Associate Certification prep course is best taken after completing the Data Engineer training or with equivalent hands-on experience.

Yes. SQL School’s job-oriented model includes:

• Resume building with Databricks project highlights
• LinkedIn profile optimization
• Mock interviews — technical and HR rounds
• Job referrals through the SQL School alumni and recruiter network
• Interview Q&A preparation specific to Databricks roles

Placement assistance continues until you secure a relevant position.

SQL School offers

• LIVE Online training — instructor-led sessions on Zoom/Teams with real-time Q&A, recordings, and lifetime access to materials

Weekend and weekday batches are available to suit working professionals and freshers alike.

Key topics include:

• Databricks workspace setup and cluster management
• Apache Spark fundamentals — DataFrames, transformations, actions
• Delta Lake architecture — ACID transactions, time travel, schema evolution
• Building and orchestrating ETL pipelines with Delta Live Tables
• Databricks Workflows and job scheduling
• Unity Catalog and data governance