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	Oct 16	08 AM - 12 PM (Asia/Kolkata)	English	$750.00
Oct 18	01 PM - 05 PM (America/New_York)	English	$750.00

Public Class Registration

If your company has purchased success credits or has a learning subscription, please fill out the Training Request form. Otherwise, you can register below.

Private Class Request

If your company is interested in private training, please submit a request.

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Registration options

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Comprehensive training offering for large scale customers that includes learning elements for every style of learning. Inquire with your account executive for details

Upcoming Public Classes

Machine Learning Practitioner

Generative AI Application Deployment and Monitoring

Ready to learn how to deploy, operationalize, and monitor generative deploying, operationalizing, and monitoring 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.

Machine Learning Practitioner

Generative AI Application Evaluation and Governance

This is your introduction to evaluating and governing generative AI systems. First, you’ll explore the meaning behind and motivation for building evaluation and governance/security systems. Next, we’ll connect evaluation and governance systems to the Databricks Data Intelligence Platform. Third, we’ll teach you about a variety of evaluation techniques for specific components and types of applications. Finally, the course will conclude with an analysis of evaluating entire AI systems with respect to performance and cost.

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

Machine Learning Practitioner

Generative AI Application Development

Ready for information and practical experience in building advanced LLM applications using multi-stage reasoning LLM chains and agents? You’re in the right place. First, you’ll learn how to decompose a problem into its components and select the most suitable model for each step to enhance business use cases. Following this, we’ll show you how to construct a multi-stage reasoning chain utilizing LangChain and HuggingFace transformers. Finally, you’ll be introduced to agents and will design an autonomous agent using generative models on Databricks.

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

Career Workshop/

March 20

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Questions?

If you have any questions, please refer to our Frequently Asked Questions page.

FAQ