Delta Live Tables
A declarative data pipeline framework provided by Databricks for defining streaming tables and materialized views. It supports data quality monitoring and dependency management.
Timeline (last 90 days)
Databricks' Spark Declarative Pipelines was formerly known as Delta Live Tables.
Streaming Tables are a table type in Databricks' lakehouse architecture for continuously ingesting and processing streaming data, typically used together with Delta Live Tables or Apache Spark Structured Streaming.
Combining event triggering with incremental computation is the typical form of modern real-time data pipelines (such as those built on Kafka, Spark Structured Streaming or Delta Live Tables).
Delta Live Tables can be used to define data quality expectations, automatically flagging and isolating problematic data.
All Facts (4)
Databricks' Spark Declarative Pipelines was formerly known as Delta Live Tables.
50%UnverifiedStreaming Tables are a table type in Databricks' lakehouse architecture for continuously ingesting and processing streaming data, typically used together with Delta Live Tables or Apache Spark Structured Streaming.
50%UnverifiedCombining event triggering with incremental computation is the typical form of modern real-time data pipelines (such as those built on Kafka, Spark Structured Streaming or Delta Live Tables).
50%UnverifiedDelta Live Tables can be used to define data quality expectations, automatically flagging and isolating problematic data.
50%