When you want to prepare application data for analysis, you need to create a process called a data pipeline. This process collects, prepares, transforms, and transfers the data from an application to a data storage place like a data lake. It's important to carefully think about the requirements for this data pipeline because they involve specific rules.

There are generally three patterns for data pipelines:

1. Extract Transform Load (ETL): In this pattern, data is first collected and filtered if necessary. Then, it's aggregated and processed before being stored in a data warehouse. ETL works well when data consistency is crucial, like historical data. However, it has downsides like slower speed, complexity, and limited scalability. Popular tools for this pattern include Apache Spark, AWS Glue, and Azure Data Factory.

2. Extract Load Transform (ELT): Similar to ETL, data is first collected, but instead of processing it right away, the raw data is stored first. Then, it's transformed within the data warehouse. ELT is suitable for situations that need more flexibility or when the data isn't fully structured. However, it requires a data warehouse with robust transformation capabilities, which adds to management efforts. Most popular solutions support this pattern except AWS Glue.

3. Extract Transform Load Transform (ETLT): This approach is a hybrid, aiming to balance ETL's consistency and ELT's flexibility. Data is partially pre-processed, then stored, and finally processed again to its desired format. While it offers some consistency and speed benefits, it demands more planning and effort during the design stage. It's useful for scenarios requiring complex data transformations.