Processing Modern Data Pipelines
The rise of affordable and elastic cloud services has enabled new data management options and necessitated new requirements for building data pipelines that mobilize all the data businesses generate and collect today. Businesses can accumulate years of historical data and gradually uncover patterns and insights or stream data continuously to power up-to-the-minute analytics. However, not all data pipelines can satisfy today’s business demands. Many pipelines add unnecessary complexity to business intelligence (BI) and data science activities due to limitations within the underlying systems used to store and process data.
This white paper describes the technical challenges that arise when building modern data pipelines and explains how Snowflake solves these challenges by automating performance with near-zero maintenance, including:
How Snowflake enables you to aggregate and transform data with capabilities such as micro-partitioning, pruning, materialized views, serverless ingestion, and more
How Snowpipe, Snowflake’s serverless ingestion service, automatically manages capacity for your data pipeline as data ingestion loads change over time
How Snowpark enables you to write code directly with Snowflake in a way that is deeply integrated into the languages you use most, using familiar concepts such as DataFrames
Learn more about the guiding principles of data pipeline modernization and how you can achieve performance, scalability, and efficiency for your modern data engineering and data science workloads. Get started today by downloading our white paper Processing Modern Data Pipelines.