This data pipeline consists of the following steps:

1. Collecting the raw data from many remote machines or devices.

2. Loading data into HDFS, often a continuous process from diverse sources (e.g., application logs), and event streams.

3. Performing real-time analysis on the data as it moves through the system and is loaded into HDFS.

4. Data cleansing and transformation of the raw data in order to prepare it for analysis.

5. Selecting a framework and programming model to write data analysis jobs.

6. Coordinating the execution of many data analysis jobs (e.g., workflow). Each in- dividual job represents a step to create the final analysis results.

7. Exporting final analysis results from HDFS into structured data stores, such as a relational database or NoSQL databases like MongoDB or Redis, for presentation or further analysis.