Foundations of Data Pipelines

The foundations of data pipelines include:

Data governance: Sets the framework for data observability, which helps determine what data to monitor and how often.
Data source: The origin of the data, which is a critical component of any data pipeline.
Goals: Defining the end product of the pipeline helps build the pipeline and make decisions along the way.
Data security: Secure data is important for protecting against privacy and data protection legislation, and for preventing unauthorized access to sensitive information.
Pipeline architecture: Anticipating common sources of change and growth is important, as a successful project will likely expand and become more complex.

Other considerations for data pipelines include:

Batch processing pipelines: Handle large chunks of data at scheduled intervals, and are suitable for processing large volumes of data that don't need to be analyzed in real-time.
Open-source pipelines: Free for public use, but some features may not be available.

A data pipeline has five key components: storage, preprocessing, analysis, applications, and delivery.

Some research topics in data pipelines include:

Data storage: Systems that preserve data as it moves through the pipeline, such as data lakes, data warehouses, databases, cloud storage, and Hadoop Distributed File System (HDFS)
Data monitoring: Detects issues like missing data, latency, and inconsistent datasets
Data governance and security: Essential aspects of any data pipeline, especially for big data analytics
Data processing: A critical part of the data pipeline, which is the flow of data from its collection point to a data lake
Formal pipeline framework: The ability to find the right data, manage data flow and workflow, and deliver the right data for analysis
Processing: The workflow of extracting data, transforming it into usable formats, and presenting it

Other topics related to data pipelines include: addressing pipeline complexities and monitoring.

[More to come ...]