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Understanding the Data Pipeline



When data needs to be moved from one location or another, there not only needs to be an infrastructure that will allow such movements, but also the processes in place to ensure the smooth and efficient transport of the data. In such instances, there is a need for what is called a data pipeline.


Definition


A data pipeline is an end-to-end sequence of digital processes that are used to collect, modify, and deliver data from one location to another so it can be stored, used for analytics, or combined with other data. As such, the data pipeline is a critical element for data integration to happen- that is the ingestion, processing, preparation, transformation, and enrichment of structured, unstructured, and semi-structured data in a governed manner.


As the name suggests, data pipelines act as the “piping” for data-heavy tasks, usually data science projects or business intelligence dashboards. Well-organized data pipelines provide the foundation for a range of data projects, including exploratory data analyses, data visualizations, and machine-learning tasks.


How data travels through the data pipeline


The process the data goes through within the data pipeline goes through the following stages:


  1. Collection/Extraction – Data is pulled from any number of sources and can come in a wide range of formats from database tables, file names, topics, and queues, to file paths (HDFS). At this stage, there is no structure or classification of the data, making it an actual data dump in which no sense can be made from it in this form.

  2. Governance - Once the data is collected, it then has to be organized at scale, what is otherwise known as data governance. It starts with linking the raw data to its business context so that it becomes meaningful, then taking control of its data quality and security, and fully organizing it for mass consumption.

  3. Transformation – Data then undergoes transformation wherein the dataset is cleansed and changed to reflect the correct reporting formats. Unnecessary or invalid data is eliminated, and the remaining data is enriched in accordance with a series of rules and regulations determined by the business needs for the data.

The standards that ensure data quality and accessibility during this stage should include:

  • Standardization: Defining what data is meaningful and how it will be formatted and stored.

  • Deduplication: Reporting duplication, as well as excluding and/or discarding redundant data.

  • Verification: Running automated checks to verify data and prune unusable data and other red-flag anomalies.

  • Sorting: Categorizing data to maximize data efficiency.

  • Sharing: With data fully transformed, it is finally ready to be shared with other users in a secure manner.


Types of data pipelines


Data pipelines are classified according to their usage, either through batch processing or through real-time processing or streaming.


Batch processing pipeline – This pipeline loads “batches” of data into a repository during scheduled periods. Batch processing jobs form a workflow of sequenced commands, where the output of one command becomes the input of the next command, with minimal human intervention required, thus allowing the management of large amounts of data in an effective manner. Batch processing is usually the optimal data pipeline when there is no immediate need to analyze a specific dataset and is primarily used for traditional analytics use cases where data is being periodically collected, transformed, and transported for conventional business intelligence use cases and functions.


Streaming pipeline – This pipeline makes use of a streaming processing engine to process real-time analytics needed by the business for inventory tracking, fraud detection, predictive maintenance, proactive customer care, or other use cases. It also enables users to gather structured and unstructured data from a wide range of streaming sources such as the Internet of Things (IoT), connected devices, social media feeds, and mobile applications using a high-throughput messaging system making sure that data is captured accurately. Streaming processing has lower latency than batch processing though not as reliable since messages can be unintentionally dropped or get stuck in queue at times.


Benefits of a data pipeline


By consolidating data from all disparate sources into one common destination, the data pipeline offers numerous benefits for the enterprise. For one, it enables a more efficient and rapid data analysis for business insights. Since the process is automated from the get-go and in succeeding iterations, this also saves critical time for the business, allowing users to focus on more intense or critical workflows.


There is also the benefit of data standardization as it allows for the establishment of a common and uniform format, allowing for a more consistently comprehensive and effective data analysis. In turn, this allows for trends and other patterns to be easily identified, paving the way for more predictive decision-making to be implemented.


Overall, the data pipeline allows for more efficient utilization of data, facilitating improved resource allocation, decision-making, and usage by the business and its users.

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