Designing a Modern Application Data Stack

Chapter 3. Efficient Data Processing and Distribution

Successful data applications deliver tangible insights to customers in the most efficient way possible. Modern data applications need to tap into an array of rapidly changing data sets and data formats while supporting a distribution model that delivers a consistent user experience.

It’s important to consider how to ingest and integrate data by building simple data pipelines that are easy to maintain and extend over time. In this chapter, we will look at how a cloud data platform allows you to reduce data movement and improve timeliness to deliver data to your application at scale.

Key Considerations for Data Processing

Processing data at scale presents a significant and complex challenge for many data teams. The primary objective of the data processing layer is to construct pipelines that efficiently and rapidly transfer data from source systems to the cloud data platform. These pipelines should be automated and resilient. They also often trigger subsequent processes that apply transformations to cleanse and standardize data, ensuring the consistent delivery of high-quality data in the required format. This gives your teams fast access to ready-to-use data, allowing them to focus their efforts on application development rather than maintaining data pipelines. 

Let’s take a look at key considerations for efficient data processing, including data format, how to reduce data movement to improve data integrity and timeliness, ...