Databricks announced its contribution of Delta Live Tables technology to the Apache Spark project, rebranding it as Spark Declarative Pipelines. This advancement simplifies the process for developers to create and maintain streaming pipelines without traditional imperative commands. Users can define their pipelines using SQL syntax or a Python SDK, facilitating a more intuitive development approach. The new framework efficiently manages dependencies and offers automatic updates for materialized views as new data arrives from streaming sources like Apache Kafka. However, familiarity with Spark's runtime behavior remains vital for effective troubleshooting.
Databricks is contributing the technology behind Delta Live Tables (DLT) to the Apache Spark project as Spark Declarative Pipelines, simplifying the development of streaming pipelines.
This feature allows developers to define streaming pipelines using SQL syntax or a Python SDK, eliminating the need for imperative commands in Spark.
Declarative Pipelines supports streaming tables from data sources like Kafka and automatically updates materialized views as new data arrives, reducing reliance on orchestrators.
Users must still understand Spark's runtime behavior to troubleshoot issues, even with the new declarative approach simplifying pipeline code creation.
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