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Building a Data Pipeline

A data pipeline refers to the process of moving data from one system to another. ETL (extract, transform, load) and data pipeline are often used interchangeably, although data does not need to be transformed to be part of a data pipeline. The standard data engineering goal of a data platform is to create a process that can be arbitrarily repeated without any change in results.

Data pipelines are often given short schrift in the heirarchy of business-critical data processes but given the growing importance of data in the enteprise, building data pipelines that can rapidly and efficiently extract info, transform it into something usable, and load it where it is accessible by analysts is of paramount importance.

Building Data Pipelines in the Cloud

Modern data pipelines provide many benefits to the enterprise, including easier access to insights and information, speedier decision-making and the flexibility and agility to handle peak demand. Modern, cloud-based data pipelines can leverage instant elasticity at a far lower price point than traditional solutions. They offer agile provisioning when demand spikes, eliminate access barriers to shared data, and, unlike hardware-constrained pipelines, enable quick deployment across the entire business.

With the Snowflake Data Cloud, organizations can use data pipelines to continuously move data into the data lake or data warehouse. Snowflake provides the following features to facilitate continuous data pipelines: continuous data loading, change data tracking, and recurring task management.

To learn more, read our ebook, 5 Characteristics of a Modern Data Pipeline.