Explain the concept of partition

Partitions in data engineering are a technique used to organize data and the computations that produce that data. By partitioning, data pipelines can operate on subsets of data instead of processing all of it at once, which makes them more performant and cost-efficient. The idea behind partitioning is that a data asset can be divided into a collection of partitions that are tracked and computed independently. Typically, each partition corresponds to a separate file or a slice of a table in a database. For instance, you might have a daily-partitioned table where each partition corresponds to the set of event records that arrived on a particular day. At the end of each day, you would fill out the partition for that day. Partitioning can also be used to process data separately for different entities, such as customers. Each customer might submit their data as a file on an object storage service like S3, and you would process each file to derive refined datasets or machine learning models, with each derived data asset being partitioned by customer. In summary, partitioning is a fundamental abstraction in data engineering that allows for scoping operations to known subsets of data that are relevant in a particular context. This approach helps in writing more efficient data pipelines and structuring them in a way that limits risk and enhances observability.