Databricks Performance Optimization

In this course, you’ll learn how to optimize workloads and physical layout with Spark and Delta Lake and and analyze the Spark UI to assess performance and debug applications. We’ll cover topics like streaming, liquid clustering, data skipping, caching, photons, and more.

Languages Available: English | 日本語 | Português BR | 한국어

Skill Level

Associate

Duration

Prerequisites

The content was developed for participants with these skills/knowledge/abilities:

Ability to perform basic code development tasks using Databricks (create clusters, run code in notebooks, use basic notebook operations, import repos from git, etc.)
Intermediate programming experience with PySpark
- Extract data from a variety of file formats and data sources
- Apply a number of common transformations to clean data
- Reshape and manipulate complex data using advanced built-in functions
Intermediate programming experience with Delta Lake (create tables, perform complete and incremental updates, compact files, restore previous versions, etc.)

Outline

Spark Architecture
Designing the Foundation File Explosion

Data Skipping

Code Optimization

Skew

Shuffle

Spill

Join Optimization Lab

Serialization

User-Defined Functions

Fine-Tuning - Choosing the Right Cluster

Fine-Tuning: Choosing the Right Cluster

Pick the Best Instance Types

Upcoming Public Classes

Date	Time	Language	Price
Date	Time	Language	Price	Jan 24	09 AM - 01 PM (Europe/London)	English	$750.00
Feb 18	01 PM - 05 PM (Europe/London)	English	$750.00
Feb 19	01 PM - 05 PM (Asia/Kolkata)	English	$750.00
Feb 20	01 PM - 05 PM (America/New_York)	English	$750.00
Apr 14	09 AM - 01 PM (America/New_York)	English	$750.00
Apr 16	09 AM - 01 PM (Asia/Kolkata)	English	$750.00
Apr 17	09 AM - 01 PM (Europe/London)	English	$750.00

Public Class Registration

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Upcoming Public Classes

Data Engineer

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This content provides a comprehensive guide to managing data privacy within Databricks. It covers key topics like Delta Lake architecture, regional data isolation, GDPR/CCPA compliance, and Change Data Feed (CDF) usage. Through practical demos and hands-on labs, participants learn to use Unity Catalog features for securing sensitive data and ensuring compliance, empowering them to safeguard data integrity effectively.

Paid

Lab

Introductory

Generative AI Engineer

Generative AI Application Deployment and Monitoring

Ready to learn how to deploy, operationalize, and monitor generative AI applications? This content will help you gain skills in the deployment of generative AI applications using tools like Model Serving. We’ll also cover how to operationalize generative AI applications following best practices and recommended architectures. Finally, we’ll discuss the idea of monitoring generative AI applications and their components using Lakehouse Monitoring.

Languages Available: English | 日本語 | Português BR | 한국어

Machine Learning Practitioner

Machine Learning at Scale

In this course, you will gain theoretical and practical knowledge of Apache Spark’s architecture and its application to machine learning workloads within Databricks. You will learn when to use Spark for data preparation, model training, and deployment, while also gaining hands-on experience with Spark ML and pandas APIs on Spark. This course will introduce you to advanced concepts like hyperparameter tuning and scaling Optuna with Spark. This course will use features and concepts introduced in the associate course such as MLflow and Unity Catalog for comprehensive model packaging and governance.