AI/BI for Data Analysts

In this course, you’ll learn how to use the features Databricks provides for business intelligence needs: AI/BI Dashboards and AI/BI Genie. As a Databricks Data Analyst, you will be tasked with creating AI/BI Dashboards and AI/BI Genie Spaces within the platform, managing the access to these assets by stakeholders and necessary parties, and maintaining these assets as they are edited, refreshed, or decommissioned over the course of their lifespan. This course intends to instruct participants on how to design dashboards for business insights, share those with collaborators and stakeholders, and maintain those assets within the platform. Participants will also learn how to utilize AI/BI Genie Spaces to support self-service analytics through the creation and maintenance of these environments powered by the Databricks Data Intelligence Engine.

Skill Level

Associate

Duration

Prerequisites

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

A basic understanding of SQL for querying existing data tables in Databricks.
Prior experience or basic familiarity with the Databricks Workspace UI.
A basic understanding of the purpose and use of statistical analysis results.
Familiarity with the concepts around dashboards used for business intelligence.

Outline

Introduction and OverviewAI/BI Dashboards

Just enough SQLDesigning Datasets for DashboardsCreating Visualizations and adding Summary Statistics to DashboardsAI Enhanced FeaturesFilters and ParametersSharing Dashboards with Stakeholders and OthersManaging Dashboards in ProductionDashboard and Visualization Lab Activity

AI/BI Genie
Introduction and OverviewAI/BI GenieDeveloping Genie SpacesSharing Genie SpacesMaintaining Genie SpacesAI/BI Genie Development Activity Lab

Upcoming Public Classes

Date	Time	Language	Price
Date	Time	Language	Price	Mar 19	01 PM - 05 PM (America/New_York)	English	$750.00

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Upcoming Public Classes

Data Engineer

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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.

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

Generative AI Engineer

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