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Unleashed the Dynamics of Delta Live Tables

Delta Live Tables


Delta Live Tables is a tool that helps you create and manage data processing pipelines. It makes it easy to transform your data by specifying the changes you want to make. It organizes tasks, manages clusters, monitors progress, ensures data quality, and handles errors.


Instead of manually writing code for each step in your pipeline, you can define tables and views that represent your data. Delta Live Tables will automatically update them as needed. It understands the queries you define and applies the transformations accordingly.


With Delta Live Tables, you can also set expectations for your data quality. This means you can define the standards your data should meet and decide what to do with records that don’t meet those standards.

Types of Delta Live Tables

Currently, there are three types of Delta Live Tables datasets available as below:

Streaming table: Records are processed exactly once.

A streaming table is a unique table that is great for handling continuously growing datasets. It allows you to process each row of data only once. Streaming tables are perfect for situations where you need fresh and up-to-date data quickly. They are also useful for performing large-scale transformations because they can update the results as new data arrives without needing to redo everything. Streaming tables work best when the data source only allows new data to be added and not modified.

Materialized views: Records are processed to generate snapshots at the time.

A materialized view is like a snapshot of pre-calculated results. It automatically updates based on the schedule of the data pipeline it belongs to. Materialized views are powerful because they can handle any changes in the input data. Whenever the pipeline updates, the view is refreshed to reflect any changes in the original data. Delta Live Tables makes it easy to work with materialized views by simplifying the process of updating them, so you can focus on writing queries without worrying about the technical details.

Views: Records are processed each time the view is queried.

In Databricks, views are used to calculate results when data is queried. Delta Live Tables keeps views within the pipeline and doesn’t publish them for general access. Views are useful for intermediate steps in the pipeline, like enforcing data quality or transforming datasets for multiple queries. They are not meant for direct use by end users or external systems.

Overall, Delta Live Tables (DTL) is a Databricks integrated framework that makes it easier to design data pipelines and control the data quality.


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FAQs

What is the difference between Delta tables and Delta live tables?

Delta tables provide a means of storing data in a structured format, whereas Delta Live Tables introduce a declarative approach to managing the data flow between these tables. Delta Live Tables is a framework that allows you to describe the relationships and operations among multiple delta tables, enabling their creation and ensuring they remain synchronized.

What is a delta live table?

Delta Live Tables is a tool that helps you create and manage data processing pipelines. It makes it easy to transform your data by specifying the changes you want to make. It takes care of organizing tasks, managing clusters, monitoring progress, ensuring data quality, and handling errors.

What is the difference between streaming live tables and live tables in Databricks?

A live streaming table or view operates on data that has been recently added since the previous pipeline update. These streaming tables and views maintain their state, meaning that if the defining query is modified, new data will be processed according to the updated query, while existing data remains unchanged and doesn’t need to be recomputed.

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