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Learning path

Databricks learning path: everything to study, topic by topic

Comprehensive Databricks assessment covering lakehouse fundamentals, Apache Spark and PySpark, Delta Lake and Unity Catalog, SQL and analytics, ML and feature engineering, and cluster management and operations.

Short answer

To learn Databricks, work through 6 modules and 28 topics in the order they build on each other. It starts with Lakehouse Fundamentals, then moves on to Spark & PySpark and Delta Lake & Unity Catalog. Use the map as a checklist, or measure your level and study only what is missing.

6
modules
28
topics
50
min to assess

What you will study

Topics are grouped by module. Open one to see its objectives and key concepts.

1 · Lakehouse Fundamentals4 topics
  • Lakehouse Architecture

    Databricks lakehouse concept, combining data warehouse and data lake, benefits over traditional architectures, and core principles

    Objectives

    • Explain the lakehouse architecture and its advantages
    • Compare lakehouse with traditional data warehouse and data lake approaches

    Key concepts

    • lakehouse paradigm
    • data lake vs warehouse
    • unified analytics
    • ACID transactions
    • schema enforcement
  • Databricks Workspace Setup

    Workspace organization, clusters, notebooks, jobs, and workspace administration

    Objectives

    • Navigate and configure a Databricks workspace
    • Create and manage notebooks and jobs

    Key concepts

    • workspace hierarchy
    • notebooks
    • clusters
    • jobs
    • workspace management
    • permissions
  • Data Governance

    Data quality, metadata management, data lineage, and governance best practices

    Objectives

    • Implement data governance patterns in Databricks
    • Track data lineage and quality metrics

    Key concepts

    • data quality
    • metadata management
    • data lineage
    • PII handling
    • retention policies
  • Medallion Architecture

    Bronze-Silver-Gold layers, medallion medallion pattern, data transformation flow, and quality gates between layers

    Objectives

    • Design a medallion architecture for data processing
    • Define transformation logic for each layer

    Key concepts

    • bronze layer
    • silver layer
    • gold layer
    • data transformation
    • quality gates
    • ELT pipelines
2 · Spark & PySpark5 topics
  • Apache Spark Core Concepts

    RDDs, partitions, executors, drivers, DAGs, lazy evaluation, and distributed computing principles

    Objectives

    • Explain Spark's distributed computing model
    • Understand RDDs and partitioning

    Key concepts

    • RDD
    • partitions
    • executor
    • driver
    • DAG
    • lazy evaluation
    • shuffle
  • PySpark Fundamentals

    PySpark API, session initialization, creating RDDs and DataFrames, transformations, and actions

    Objectives

    • Write basic PySpark scripts
    • Understand the difference between transformations and actions

    Key concepts

    • SparkSession
    • RDD creation
    • DataFrame creation
    • transformations
    • actions
    • PySpark context
  • DataFrame API & Operations

    DataFrame schema, column operations, filtering, selecting, grouping, and window functions

    Objectives

    • Perform complex DataFrame operations
    • Use window functions for advanced analytics

    Key concepts

    • schema
    • column operations
    • filtering
    • select
    • groupBy
    • aggregation
    • window functions
  • Spark SQL & Catalyst Optimizer

    Registering temporary views, writing SQL on DataFrames, Catalyst query optimization, and execution plans

    Objectives

    • Write Spark SQL queries on DataFrames
    • Understand query optimization via Catalyst

    Key concepts

    • temporary view
    • table registration
    • SQL queries
    • Catalyst optimizer
    • execution plan
    • query optimization
  • Spark Performance & Tuning

    Partitioning strategies, shuffle optimization, memory management, caching, and broadcast variables

    Objectives

    • Optimize Spark job performance
    • Manage memory and reduce shuffle operations

    Key concepts

    • partitioning
    • shuffle optimization
    • memory management
    • caching
    • broadcast variables
    • spillover
3 · Delta Lake & Unity Catalog5 topics
  • Delta Lake Fundamentals

    ACID transactions, schema enforcement, time travel, versioning, and Delta table properties

    Objectives

    • Create and manage Delta tables
    • Leverage ACID guarantees in ETL workflows

    Key concepts

    • ACID properties
    • schema enforcement
    • time travel
    • versioning
    • transaction log
    • schema evolution
  • Delta Lake Optimization

    OPTIMIZE command, Z-order clustering, vacuum, compaction, and performance tuning

    Objectives

    • Optimize Delta table performance
    • Implement efficient compaction strategies

    Key concepts

    • OPTIMIZE
    • Z-order
    • clustering
    • vacuum
    • compaction
    • file pruning
  • Delta Sharing

    Sharing Delta tables with external partners, share recipients, and cross-workspace collaboration

    Objectives

    • Set up Delta sharing for external collaborators
    • Manage access control for shared tables

    Key concepts

    • Delta sharing
    • share recipients
    • cross-workspace sharing
    • partner collaboration
    • access control
  • Unity Catalog

    Unified data governance, metastore, catalogs, schemas, table management, and access control

    Objectives

    • Organize data using Unity Catalog structure
    • Implement unified governance across workspaces

    Key concepts

    • metastore
    • catalog
    • schema
    • table management
    • access control
    • lineage tracking
  • Governance Policies & Access Control

    Data access policies, row-level security, column-level masking, audit logging, and compliance enforcement

    Objectives

    • Implement fine-grained access control policies
    • Ensure compliance through audit and masking

    Key concepts

    • access policies
    • row-level security
    • column masking
    • audit logging
    • compliance
    • RBAC
4 · SQL & Analytics5 topics
  • SQL Fundamentals

    SELECT, WHERE, ORDER BY, LIMIT, GROUP BY basics, and single-table queries

    Objectives

    • Write basic SQL queries
    • Filter and sort data effectively

    Key concepts

    • SELECT
    • WHERE
    • ORDER BY
    • LIMIT
    • GROUP BY
    • HAVING
    • filtering
  • SQL Joins & Multi-Table Queries

    INNER JOIN, LEFT JOIN, RIGHT JOIN, FULL OUTER JOIN, CROSS JOIN, and join optimization

    Objectives

    • Write complex multi-table queries using joins
    • Optimize join operations for performance

    Key concepts

    • INNER JOIN
    • LEFT JOIN
    • RIGHT JOIN
    • FULL OUTER JOIN
    • CROSS JOIN
    • join conditions
    • join optimization
  • SQL Aggregations & Analytics

    Aggregate functions, GROUP BY, HAVING, window functions, CTEs, and analytical queries

    Objectives

    • Use aggregate functions and window functions
    • Write advanced analytical queries

    Key concepts

    • SUM, AVG, COUNT, MIN, MAX
    • GROUP BY
    • HAVING
    • window functions
    • CTEs
    • PARTITION BY
    • ROW_NUMBER
  • Dashboards & Reporting

    Creating visualizations in Databricks SQL, dashboard design, KPI tracking, and reporting best practices

    Objectives

    • Design effective dashboards and reports
    • Create automated reporting pipelines

    Key concepts

    • SQL dashboard
    • visualization
    • KPI
    • alerting
    • data storytelling
    • report automation
  • Query Optimization & Performance

    Query plans, indexing strategies, statistics, table statistics, and SQL performance tuning

    Objectives

    • Analyze and optimize slow-running queries
    • Use statistics to improve query performance

    Key concepts

    • execution plan
    • query optimization
    • statistics
    • indexing
    • hints
    • bottleneck identification
5 · ML & Feature Engineering5 topics
  • ML Fundamentals

    Supervised vs unsupervised learning, training and test splits, cross-validation, and evaluation metrics

    Objectives

    • Understand core ML concepts
    • Evaluate model performance correctly

    Key concepts

    • supervised learning
    • unsupervised learning
    • train-test split
    • cross-validation
    • evaluation metrics
    • overfitting
  • Feature Store

    Feature engineering, feature store architecture, feature management, serving, and time-series features

    Objectives

    • Design a feature store for ML workflows
    • Manage features consistently across training and serving

    Key concepts

    • feature engineering
    • feature store
    • online store
    • offline store
    • feature registry
    • feature serving
  • Model Training at Scale

    Distributed model training, hyperparameter tuning, Spark ML pipelines, and parallel training strategies

    Objectives

    • Train models at scale using Spark ML
    • Perform hyperparameter tuning efficiently

    Key concepts

    • Spark ML
    • pipeline
    • hyperparameter tuning
    • distributed training
    • GridSearchCV
    • RandomSearchCV
  • MLflow & Model Management

    MLflow tracking, logging, model registry, versioning, and model promotion workflow

    Objectives

    • Set up MLflow for experiment tracking
    • Manage model lifecycle using model registry

    Key concepts

    • MLflow
    • experiment tracking
    • model logging
    • model registry
    • versioning
    • stage transitions
  • Model Deployment & Serving

    Model serving infrastructure, batch predictions, real-time endpoints, A/B testing, and monitoring in production

    Objectives

    • Deploy models for batch and real-time inference
    • Monitor model performance in production

    Key concepts

    • batch inference
    • real-time serving
    • model endpoints
    • A/B testing
    • monitoring
    • drift detection
6 · Cluster Management & Operations4 topics
  • Cluster Configuration

    Cluster types, node types, worker sizing, driver configuration, and initialization scripts

    Objectives

    • Configure clusters for specific workloads
    • Select appropriate node types and sizes

    Key concepts

    • cluster type
    • node type
    • auto-scaling
    • driver sizing
    • worker configuration
    • initialization scripts
  • Cluster Optimization & Cost Management

    Right-sizing clusters, auto-scaling policies, spot instances, cost optimization, and resource pooling

    Objectives

    • Optimize cluster costs while maintaining performance
    • Implement effective auto-scaling policies

    Key concepts

    • auto-scaling
    • spot instances
    • cost optimization
    • resource allocation
    • pooling
    • scaling policies
  • Monitoring & Troubleshooting

    Cluster metrics, job monitoring, logs analysis, debugging techniques, and alert setup

    Objectives

    • Monitor cluster health and job performance
    • Debug and troubleshoot issues effectively

    Key concepts

    • cluster metrics
    • job monitoring
    • logs
    • debugging
    • alerting
    • event logs
  • Production Operations & Best Practices

    Production workload management, high availability, disaster recovery, security hardening, and operational excellence

    Objectives

    • Design and manage production Databricks environments
    • Implement HA/DR and security best practices

    Key concepts

    • HA/DR
    • disaster recovery
    • security hardening
    • SLA management
    • backup strategies
    • operational excellence

Levels: Junior, Mid, Senior, and Expert

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Frequently asked questions

Where should I start studying Databricks?

Start with Lakehouse Fundamentals: Lakehouse Architecture, Databricks Workspace Setup, and Data Governance. The map orders topics by what they depend on, so each one prepares the next.

How long does a full Databricks assessment take?

A full assessment of this path takes about 50 minutes. Study time depends on where you start; a diagnostic shows which topics you can skip.

Do I need an account to use this page?

No. The map, objectives and key concepts are open. You only need an account to be measured, get your gap by topic and receive a plan.