Data Pipelines in the Cloud

Data pipelines eliminates many of the most manual and error-prone processes involved in transporting data between locations by automating data flow at every step of journey. By automating data validation, ETL, and data combining processes across multiple streams, a high-performance data pipeline can effectively fight data latency and bottlenecks. Like an assembly line for raw data, It acts as a powerful engine that sends data through various filters, apps, and APIs to arrive in usuable form at the final destination, often a data warehouse or a visualization platform.

Data Pipelines vs. ETL (Extract, Transform, Load)

What's the difference between ETL and a data pipeline? ETL is usually a sub-process in the data pipeline (and depending on the nature of the pipeline, may not be included). It really only refers to the (often batch) process of extracting, transforming, and loading data from one system to another. A data pipeline, on the other hand, is the entire process involved in transporting data from one location to another.

Snowflake and Data Pipelines in the Cloud

With the Snowflake Data Cloud, users can use data pipelines to continuously move data into the data lake or data warehouse. Often, raw data is first loaded temporarily into a staging table used for interim storage and then transformed with SQL statements before it is moved to its destination. An efficient workflow for this process transforms only data that is new or modified.

Snowflake provides the following features to enable continuous data pipelines: continuous data loading, change data tracking, and recurring task management. Learn more.