Processing Modern Data Pipelines
November 12, 2021
Data is central to how we run our businesses, establish our institutions, and manage our personal and professional lives. Nearly every interaction generates data—whether from software applications, social media connections, mobile communications, or many types of digital services. Multiply those interactions by a growing number of connected people, devices, and interaction points, and the scale is overwhelming—and growing rapidly every day.
Although all this data holds tremendous potential, it is often difficult to mobilize for specific purposes. Today, the rise of affordable and elastic cloud services has enabled new data management options—and necessitated new requirements for building data pipelines to capture all this data and put it to work.
However, not all data pipelines can satisfy today’s business demands, so choose carefully when you design your architecture and select data platform and processing capabilities. 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 these challenges can be solved by automating performance with near-zero maintenance.