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DLT language references

This article has information on the programming interfaces available to implement DLT pipelines and has links to documentation with detailed specifications and examples for each interface.

Data loading and transformations are implemented in a DLT pipeline by queries that define streaming tables and materialized views. To implement these queries, DLT supports SQL and Python interfaces. Because these interfaces provide equivalent functionality for most data processing use cases, pipeline developers can choose the interface that they are most comfortable with. The articles in this section are detailed references for the SQL and Python interfaces and should be used by developers as they implement pipelines in their interface of choice.

DLT SQL​

For pipeline developers familiar with writing queries in SQL, DLT has a simple but powerful SQL interface designed to support the loading and transformation of data.

  • To understand the basics of working with DLT SQL, see Develop pipeline code with SQL.
  • To learn about the details of the SQL interface, including how to define streaming tables for tasks such as loading data and materialized views for transforming data, see DLT SQL language reference.

DLT Python​

For Python developers, DLT has a Python interface designed to support the loading and transformation of data. For tasks that require processing not supported by SQL, developers can use Python to write pipeline source code that combines DLT queries with Python functions that implement the processing not supported by the DLT interfaces.

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