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Book Overview & Buying
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Table Of Contents
Building Modern Data Applications Using Databricks Lakehouse
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The DLT framework automatically manages task orchestration, cluster creation, and exception handling, allowing data engineers to focus on defining transformations, data enrichment, and data validation logic. Data engineers will define a data pipeline using one or more dataset types. Under the hood, the DLT system will determine how to keep these datasets up to date. A data pipeline using the DLT framework is made up of the streaming tables, materialized views, and views dataset types, which we’ll discuss in detail in the following sections. We’ll also briefly discuss how to visualize the pipeline, view its triggering method, and look at the entire pipeline data flow from a bird’s-eye view. We’ll also briefly understand the different types of Databricks compute and runtime, and Unity Catalog. Let’s go ahead and get started.
Streaming tables leverage the benefits of Delta Lake and Spark Structured Streaming...