Data Processing Pipeline

A Data Processing Pipeline is a computing system that processes data.

• Context:
  • It can range from being a Batch Data Processing Pipeline to being a Real-Time Data Processing Pipeline.
  • It can handle large volumes of data efficiently by processing it in stages.
  • It can integrate various data sources such as databases, APIs, and file systems.
  • It can perform tasks such as data extraction, transformation, loading, and validation.
  • It can be implemented using different technologies like Apache Kafka, Apache Spark, or AWS Data Pipeline.
  • It can support various applications including analytics, reporting, and machine learning.
  • It can be designed to ensure data quality and consistency through validation and error handling mechanisms.
  • It can enable scalability and flexibility by allowing for the addition or modification of stages without disrupting the overall workflow.
  • It can be monitored and managed through tools that provide insights into pipeline performance and data flow.
  • It can be automated to run at scheduled intervals or triggered by specific events to ensure timely data processing.
  • ...
• Example(s):
  • An Extract-Transform-Load (ETL) Pipeline (for a data warehousing system) that extracts data from multiple sources, transforms it into a suitable format, and loads it into a data warehouse.
  • An Extract-Load-Transform (ELT) Pipeline (for a data integration system) that extracts data, loads it into a target system, and then transforms it as needed.
  • A Machine Learning Pipeline (for a machine learning system) that preprocesses data, trains models, and evaluates performance before deployment.
  • An Instruction Pipeline (for a CPU architecture) that fetches instructions, decodes them, executes operations, and writes back results.
  • A Graphics Pipeline (for a GPU) that processes graphical data, applies transformations, and renders images.
  • An LLM Pipeline (for an LLM-based system) that preprocesses text, generates responses using a large language model, and post-processes output for various applications.
  • A Stream Processing Pipeline (for a real-time analytics system) that ingests data streams, processes events, and outputs results in real-time.
  • A Data Cleaning Pipeline (for a data quality system) that detects and corrects errors, removes duplicates, and standardizes formats.
  • A Data Aggregation Pipeline (for a business intelligence system) that collects data from different sources, summarizes metrics, and generates reports.
  • A Data Migration Pipeline (for a system upgrade) that extracts data from legacy systems, transforms it for compatibility, and loads it into new systems.
  • ...
• Counter-Example(s):
  • A Computing Platform, which provides the infrastructure and environment for running applications but does not itself process data in stages.
  • A RISC Pipeline, which is specific to the instruction processing stages in reduced instruction set computing (RISC) architectures and not a general data processing system.
  • An HTTP Pipeline, which deals with multiple HTTP requests in a web context rather than processing data through multiple stages.
• See: Computing Buffer, Message Queue.

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References

2021

• (Densmore, 2021) ⇒ James Densmore. (2021). “Data Pipelines Pocket Reference.” O'Reilly Media.
  • QUOTE: ... Data pipelines are sets of processes that move and transform data from various sources to a destination where new value can be derived. They are the foundation of analytics, reporting, and machine learning capabilities. The complexity of a data pipeline depends on the size, state, and structure of the source data as well as the needs of the analytics project. In their simplest form, pipelines may extract only data from one source such as a REST API and load to a destination such as a SQL table in a data warehouse. In practice, however, pipelines typically consist of multiple steps including data extraction, data preprocessing, data validation, and at times training or running a machine learning model before delivering data to its final destination. Pipelines often contain tasks from multiple systems and programming languages. What’s more, data teams typically own and maintain numerous data pipelines that share dependencies and must be coordinated. Figure 1-1 illustrates a simple pipeline.

    SERVER LOGS ==> S3 BUCKETS ==> PROCESS AND STRUCTURE ==> Amazon Redshift

2019

• (Wikipedia, 2019) ⇒ https://en.wikipedia.org/wiki/Pipeline_(computing) Retrieved:2019-4-8.
  • In computing, a pipeline, also known as a data pipeline, is a set of data processing elements connected in series, where the output of one element is the input of the next one. The elements of a pipeline are often executed in parallel or in time-sliced fashion. Some amount of buffer storage is often inserted between elements.

    Computer-related pipelines include:

    • Instruction pipelines, such as the classic RISC pipeline, which are used in central processing units (CPUs) and other microprocessors to allow overlapping execution of multiple instructions with the same circuitry. The circuitry is usually divided up into stages and each stage processes a specific part of one instruction at a time, passing the partial results to the next stage. Examples of stages are instruction decode, arithmetic/logic and register fetch. They are related to the technologies of superscalar execution, operand forwarding, speculative execution and out-of-order execution.
    • Graphics pipelines, found in most graphics processing units (GPUs), which consist of multiple arithmetic units, or complete CPUs, that implement the various stages of common rendering operations (perspective projection, window clipping, color and light calculation, rendering, etc.).
    • Software pipelines, which consist of a sequence of computing processes (commands, program runs, tasks, threads, procedures, etc.), conceptually executed in parallel, with the output stream of one process being automatically fed as the input stream of the next one. The Unix system call pipe is a classic example of this concept.
    • HTTP pipelining, the technique of issuing multiple HTTP requests through the same TCP connection, without waiting for the previous one to finish before issuing a new one.
  • Some operating systems may provide UNIX-like syntax to string several program runs in a pipeline, but implement the latter as simple serial execution, rather than true pipelining — namely, by waiting each program to finish before starting the next one.

2016

• https://medium.com/the-data-experience/building-a-data-pipeline-from-scratch-32b712cfb1db
  • QUOTE: ... a data pipeline is a set of actions that extract data (or directly analytics and visualization) from various sources. It is an automated process: take these columns from this database, merge them with these columns from this API, subset rows according to a value, substitute NAs with the median and load them in this other database. This is known as a “job”, and pipelines are made of many jobs. ... On the internet you’ll find countless resources about pipeline and warehouse infrastructure possibilities. You won’t find as many resources on the process to follow or on best practices. …

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