Getting Started with the Databricks Lakehouse Platform

What you'll learn

Organizations have long collected data in a variety of formats, structured, unstructured, and semi-structured data. However, working with data in different formats for different use-cases requires multiple platform data warehouses for structured data needed for business intelligence and data lakes for unstructured data needed for data science and machine learning. The Databricks data lakehouse architecture is an innovative paradigm that combines the flexibility and cost efficiency of a data lake with the reliability and features of a data warehouse.

In this course, Getting Started with the Databricks Lakehouse Platform, you will learn the importance of storing data in a centralized repository and how data lakes and data warehouses serve to solve different data-related problems. First, you’ll explore a variety of technologies in the analytics space and how the lakehouse platform encompasses their strengths while mitigating their limitations.

Next, you will understand the basic components that make up the architecture of a data lakehouse and how the Databricks Lakehouse Platform uses Delta Lakes to enable both SQL analytics and data science and machine learning using the same underlying data lake storage.

Finally, you will explore the Databricks Data Lakehouse on Microsoft Azure. You will build the data lakehouse, store data in Delta Tables, and access the same data using Apache Spark and SQL queries.

When you are finished with this course, you will be able to clearly articulate how the data lakehouse platform helps mitigate challenges with current data architectures and will know hands-on how you can set up and use the lakehouse on Databricks.

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