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Overview
This course serves as an appropriate entry point to learn Advanced Data Engineering with Databricks.
Below, we describe each of the four, four-hour modules included in this course.
Prerequisites
Participants should have:
- Experience using PySpark APIs for advanced data transformations.
- Familiarity with implementing Python classes.
- Experience using SQL in production environments such as data warehouses or data lakes.
- Hands-on experience with Databricks notebooks and cluster configuration.
- Understanding of Delta Lake table creation and manipulation with SQL.
These prerequisites can be met by completing the Data Engineering with Databricks and Apache Spark Programming with Databricks courses and by earning the Databricks Certified Data Engineer Associate and Databricks Certified Associate Developer for Apache Spark certifications.
If you do not have one or more of the pre-requisites QA recommends: (can be taken in either order)
Target audience
This course is ideal for:
- Data engineers aiming to optimise and scale data processing in Databricks.
- Big data professionals working with streaming and batch data.
- Cloud engineers focused on Delta Lake architectures and Structured Streaming.
- Individuals preparing for the Databricks Certified Data Engineer Professional exam.
Delegates will learn how to
Databricks Streaming and Delta Live Tables
This course provides a comprehensive understanding of Spark Structured Streaming and Delta Lake, including computation models, configuration for streaming read, and maintaining data quality in a streaming environment.
Databricks Data Privacy
This content is intended for the learner persona of data engineers or for customers, partners, and employees who complete data engineering tasks with Databricks. It aims to provide them with the necessary knowledge and skills to execute these activities effectively on the Databricks platform.
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.
Automated Deployment with Databricks Asset Bundles
This course provides a comprehensive review of DevOps principles and their application to Databricks projects. It begins with an overview of core DevOps, DataOps, continuous integration (CI), continuous deployment (CD), and testing, and explores how these principles can be applied to data engineering pipelines.
The course then focuses on continuous deployment within the CI/CD process, examining tools like the Databricks REST API, SDK, and CLI for project deployment. You will learn about Databricks Asset Bundles (DABs) and how they fit into the CI/CD process. You’ll dive into their key components, folder structure, and how they streamline deployment across various target environments in Databricks. You will also learn how to add variables, modify, validate, deploy, and execute Databricks Asset Bundles for multiple environments with different configurations using the Databricks CLI.
Finally, the course introduces Visual Studio Code as an Interactive Development Environment (IDE) for building, testing, and deploying Databricks Asset Bundles locally, optimizing your development process. The course concludes with an introduction to automating deployment pipelines using GitHub Actions to enhance the CI/CD workflow with Databricks Asset Bundles.
By the end of this course, you will be equipped to automate Databricks project deployments with Databricks Asset Bundles, improving efficiency through DevOps practices.
Outline
Databricks Streaming and Delta Live Tables
- Streaming Data Concepts
- Introduction to Structured Streaming
- Demo: Reading from a Streaming Query
- Streaming from Delta Lake
- Lab: Streaming Query Lab
- Aggregation, Time Windows, Watermarks
- Event Time + Aggregatios over Time Windows
- Lab: Stream Aggregation Lab
- Demo: Windowed Aggregation with Watermark
- Data Ingestion Pattern
- Demo: Auto Load to Bronze
- Demo: Stream from Multiplex Bronze
- Quality Enforcement Pattern
- Demo: Quality Enforcement
- Lab: Streaming ETL Lab
Databricks Data Privacy
- Regulatory Compliance
- Data Privacy
- Key Concepts and Components
- Audit Your Data
- Data Isolation
- Demo: Securing Data in Unity Catalog
- Pseudonymization & Anonymization
- Summary & Best Practices
- Demo: PII Data Security
- Capturing Changed Data
- Deleting Data in Databricks
- Demo: Processing Records from CDF and Propagating Changes
- Lab: Propagating Changes with CDF Lab
Databricks Performance Optimization
- DevOps Spark UI Introduction
- Introduction to Designing Foundation
- Demo: File Explosion
- Data Skipping and Liquid Clustering
- Lab: Data Skipping and Liquid Clustering
- Skew
- Shuffles
- Demo: Shuffle
- Spill
- Lab: Exploding Join
- Serialization
- Demo: User-Defined Functions
- Fine-Tuning: Choosing the Right Cluster
- Pick the Best Instance Types
Automated Deployment with Databricks Asset Bundles
- DevOps Review
- Continuous Integration and Continuous Deployment/Delivery (CI/CD) Review
- Demo: Course Setup and Authentication
- Deploying Databricks Projects
- Introduction to Databricks Asset Bundles (DABs)
- Demo: Deploying a Simple DAB
- Lab: Deploying a Simple DAB
- Variable Substitutions in DABs
- Demo: Deploying a DAB to Multiple Environments
- Lab: Deploy a DAB to Multiple Environments
- DAB Project Templates Overview
- Lab: Use a Databricks Default DAB Template
- CI/CD Project Overview with DABs
- Demo: Continuous Integration and Continuous Deployment with DABs
- Lab: Adding ML to Engineering Workflows with DABs
- Developing Locally with Visual Studio Code (VSCode)
- Demo: Using VSCode with Databricks
- CI/CD Best Practices for Data Engineering
- Next Steps: Automated Deployment with GitHub Actions
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