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Databricks Concepts

BasicSkill Level
4.7+
815 reviews
Updated 02/2025
Learn about the power of Databricks Lakehouse and help you scale up your data engineering and machine learning skills.
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DatabricksData Engineering
4 hr
19 videos
60 Exercises
3,900 XP
21,693
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Course Description


Learn the power of the Lakehouse In today's data-filled world, we need tools that allow us to be as data-driven as possible. This course guides you from start to finish on how the Databricks Lakehouse Platform provides a single, scalable, and performant platform for your data processes. Working through a real-world dataset will teach you how to accomplish various tasks within the Databricks platform. You'll start the course by learning how to administer the Databricks platform and ensuring your environment is set up securely.


Practice scalable data engineering After setting up your workspace, you will learn how to create powerful data pipelines using Databricks. You will apply different transformations to the dataset, moving it from Bronze to Silver and then Gold in a Medallion architecture. You will learn how Databricks clusters provide readily available compute power and scalability. You will set up an end-to-end Databricks Workflow to automate your entire data pipeline.


Use the Lakehouse as your data warehouse A key part of the Lakehouse architecture is that you can query your data storage like a traditional data warehouse. In this section, you will learn how Databricks SQL gives you the data warehousing performance you want on top of your data lake. You will learn how to create queries using standard ANSI SQL, and use those results to create ad-hoc dashboards against your entire dataset.


Implement governed data science and machine learning Finally, you will learn how Databricks provides a complete set of tools for data science and machine learning use cases. You will learn to track and evaluate your models using the fully integrated MLFlow framework for MLOps. You will learn how the Feature Store and Model Registry simplify the process of creating production-quality machine-learning models. Finally, you will learn how to deploy and monitor your models using built-in model serving capabilities.

Prerequisites

Intermediate SQLUnderstanding Data EngineeringUnderstanding Machine Learning
1

Welcome to Databricks

Learn about the new lakehouse paradigm for your cloud data strategy and how the Databricks Lakehouse platform can modernize your data architecture. Understand the foundational components of the Databricks platform and how they all fit together.
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2

Data Engineering

Learn how to process, transform, and clean your data using Databricks functionality. Practice using capabilities such as the Delta storage format, Delta Live Tables, and Workflows together to create an end-to-end data pipeline.
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Databricks Concepts
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FAQs

What is Databricks?

Databricks is a unified data analytics platform that simplifies and accelerates big data and AI projects. Built on Apache Spark, it integrates data engineering, data science, and machine learning tools into one environment.

What is Databricks used for?

Databricks is used for data engineering, data science, data analytics, data governance, and collaboration. It helps ingest, transform, analyze data, build and deploy machine learning models, and manage data access and lineage.

What are the basic features of Databricks?

The basic features of Databricks include workspaces for collaboration, clusters for computing power, notebooks for interactive coding, Delta Lake for reliable data storage, jobs for automated workflows, and Unity Catalog for data governance.

How can Databricks benefit my organization?

Databricks can benefit your organization by providing scalable computing resources, enhancing team collaboration, ensuring secure data management, and integrating seamlessly with various cloud services and third-party tools.

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