New Capability Overview: UniForm

UniForm (Universal Format) is a new 2023 offering from Databricks that expands the functionality of Delta Lake. UniForm simplifies and enhances table storage formats by adding support for Iceberg and Hudi formats. With UniForm, data stored in Delta can be read-from and written-to as if it were Iceberg or Hudi. The content contained in this learning plan aims to serve as an introduction to UniForm on the Databricks Data Intelligence Platform.

Skill Level

Introductory

Duration

30m

Prerequisites

At a minimum, you should be familiar with the following before attempting to take this content:

Fundamental concepts about the Databricks Data Intelligence Platform
Intermediate level-knowledge of Delta Lake (consider completing the Manage Data with Delta Lake course)
Programming experience in SQL or Python

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Data Engineer

Deploy Workloads with Databricks Workflows

By scheduling tasks with Databricks Jobs, applications can be run automatically to keep tables in the Lakehouse fresh. Using Databricks SQL to schedule updates to queries and dashboards allows quick insights using the newest data. In this course, students will be introduced to task orchestration using the Databricks Workflow Jobs UI. Optionally, they will configure and schedule dashboards and alerts to reflect updates to production data pipelines.

In this course, you’ll learn how to orchestrate data pipelines with Databricks Workflow Jobs and schedule dashboard updates to keep analytics up-to-date. We’ll cover topics like getting started with Databricks Workflows, how to use Databricks SQL for on-demand queries, and how to configure and schedule dashboards and alerts to reflect updates to production data pipelines.

Note: This course is part of the 'Data Engineering with Databricks' course series.

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Machine Learning Practitioner

Machine Learning Operations

This course will guide participants through a comprehensive exploration of machine learning model operations, focusing on MLOps and model lifecycle management. The initial segment covers essential MLOps components and best practices, providing participants with a strong foundation for effectively operationalizing machine learning models. In the latter part of the course, we will delve into the basics of the model lifecycle, demonstrating how to navigate it seamlessly using the Model Registry in conjunction with the Unity Catalog for efficient model management. By the course's conclusion, participants will have gained practical insights and a well-rounded understanding of MLOps principles, equipped with the skills needed to navigate the intricate landscape of machine learning model operations.

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Data Engineer

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.

Note: This course is part of the 'Advanced Data Engineering with Databricks' course series.

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