Low-Code Pipelines with Dynamic Tables inside Snowflake

  With Dynamic Tables, Snowflake supports a declarative approach to data pipelines. Instead of defining complex ETL jobs with explicit orchestration, you simply describe the desired target state in the form of an SQL definition. Snowflake automatically handles incremental updates, dependency resolution, and execution in the background. This significantly reduces operational overhead: Developers focus on the transformation logic, while scheduling, change tracking, and performance optimization are controlled by the platform. Dynamic Tables thus enable maintainable, transparent, and cloud-native data pipelines without the complexity of traditional ETL tools. In addition, Snowflake provides a visual interface that allows you to monitor and control the loading and refreshing process of Dynamic Tables. This view allows dependencies between tables to be traced, processing status to be viewed, and potential sources of error to be quickly identified. It also provides transparency on key figures such as the number of records processed per table or the duration of individual refresh cycles. This complements the declarative approach with convenient monitoring and control options.

 

An important use case for dynamic tables would be, for example, the regular loading of an SCD-2 dimension in near real time within a data lakehouse with Medallion architecture.

In the bronze layer (or landing zone), the data is transferred 1:1 from the source systems. In the silver layer (or foundation layer), the data from different sources is integrated and transformed. Finally, the gold layer (or business layer) contains the SCD-2 dimension, i.e., a dimension in which every change is versioned with a valid time period (VALID_FROM and VALID_TO). For this purpose, a stream is defined on the corresponding transformation dynamic table. As soon as new or changed data records are available for processing, a task is triggered which creates or updates the corresponding versions in the target dimension. This means that the update is declarative and fully automatic – without complex scheduling logic or human intervention.   Delivery sumIT

sumIT is your partner for modern data architectures in Snowflake. We help you gain a thorough understanding of the structure and characteristics of your source data, model a scalable Medallion architecture, and make optimal use of storage and computing resources. Together, we define declarative data pipelines and implement the necessary components—from streams and tasks to high-performance SQL transformations—so that your data is delivered reliably, efficiently, and according to your needs.