Data Dynamics
Training · Databricks Quickstart

Databricks Training

Databricks Quickstart is a 5-day intensive training package that accelerates Databricks adoption through best-practice sessions grounded in real-world project experience and reusable code templates.

Quickstart package overview

Education is the heart of the Quickstart package. The goal is to remove blockers and expand Databricks adoption, delivered as advisory and hands-on sessions measured against the Well-Architected Lakehouse framework.

  • Based on a Well-Architected Lakehouse covering all essential pillars
  • Databricks best-practice architecture review
  • Deep best-practice sessions on agreed topics
  • Reusable sample code templates

5-day delivery structure

Quickstart support for new teams — including architecture review, best-practice sessions, and demo code.

01

Current state review

In the first workshop we walk through key business requirements and pain points, the broader data context, the current Databricks platform state, and the supporting documentation.

02

Deep-dive sessions

Best-practice-driven deep dives on the agreed topics, with Unity Catalog, data architecture, Delta Lake, and MLOps at the core.

03

Best practices & sample code

Standard demos and reusable code templates built on Databricks Asset Bundles (DABs), co-designed for the customer's environment.

04

Handover & knowledge transfer

Transfer Well-Architected review summaries, reference architectures, and code templates so the customer team can take over operations.

Deep-dive topics

Sessions are selected from 7 core topics based on the customer's environment and goals.

01
Account & Workspace
Account, workspace, identity, access, and SSO design
02
Unity Catalog
3-level namespace, isolation, Volumes, lineage
03
Data Architecture
Medallion (Bronze/Silver/Gold) design
04
Delta Lake
Deletion Vectors, Liquid Clustering, data skipping
05
Data Engineering
Lakeflow Connect, DLT, Workflows
06
Compute · Photon
Jobs/All-purpose/SQL Warehouse, Serverless, Photon
07
MLOps
Unity Catalog-based ML lifecycle management

7 principles of the Well-Architected Lakehouse

Reviews are aligned with the 7 pillars of the Databricks Well-Architected Lakehouse framework.

Reliability

Ability to recover from failures and keep running

Operational Excellence

All operational processes for running the Lakehouse in production

Security, Privacy & Compliance

Protecting applications, workloads, and data from threats

Performance Efficiency

Ability to adapt to changing load

Cost Optimization

Managing costs to maximize delivered value

Interoperability & Usability

Ability to interact with users and other systems

Data & AI Governance

Ability to centrally manage data and data access

Sample deliverables

Actual deliverables depend on the agreed Quickstart contract. Typical outputs include:

  • Well-Architected review summary
    Prescriptive guidance based on the Well-Architected Lakehouse framework (docs and slides)
  • Reference architecture
    Customer-specific architecture based on the Databricks Data Intelligence Platform
  • Code templates
    Standard demos and deployment templates based on Databricks Asset Bundles (DABs)
  • Custom materials
    Artifacts tailored to the agreed Quickstart topics

Prerequisites

Four conditions must be met before Quickstart begins.

  • SMEs & experts
    Customer's technical, business, and domain experts must be available during delivery
  • Environment & access
    Reasonable access to environment, data, and artifacts, with at least one Databricks workspace ready
  • Executive sponsorship
    Active executive backing to sustain customer engagement
  • Knowledge transfer
    Customer tech team participates throughout and is ready to take over deliverables at the end

Core principles

Our goal is to transfer Databricks best practices throughout the entire training.

Use Delta Lake
Use Databricks Workflows and Databricks Asset Bundles
Use Unity Catalog, including for MLOps
Use Photon
Use the medallion architecture
Use unified login (SSO)

Multi-cloud support

The same curriculum and reference architectures are available on Azure and AWS.

Azure Databricks

Public Access, Backend Private Link, and Frontend + Backend Private Link architectures

AWS Databricks

Backend Private Link, Network ACLs, Security Groups, and Cross-Account Role setup

Training · Databricks Quickstart
Contact us