Data pipeline works by a series of actions or steps of processing data. The process involves the ingestion of data from different sources then move them to a destination in step by step manner. In each step, the output is formulated the goes on until completed.
How does it work? As its name suggests, it works likes how a pipeline runs. It carries data from sources then delivers it to a destination. It allows disparate data to be automatically processed, then delivers and centralize into a data system.
The key elements of a data pipeline can be categorized into three: an origin or a source, a step-by-step procedure or flow of data, and a destination.
Components of Data Pipeline
- Origin or Source. It is the point of origin of the data that will be processed. Data pipeline gets data from disparate sources, including SaaS applications data, API applications, a webhook, social media, IoT devices, and storage systems such as data warehouses of companies reports and analytics.
- Dataflow. It involves data movement from sources to the destination. It includes the various changes that happened along the process and the storages of data it went through. ETL (extract, transform, load) is one of the ways to a data flow. It is a specific data pipeline type.
Extract- is the process of ingestion of data from the sources.
Transform- refers to the preparation of data for analysis such as sorting, verification validation, and so on.
Load- refers to the final output loading to the destination.
- Destination. It is the final place where the data will be stored, such as a data warehouse, data lake, and the like.
- Processing. This involves taking actions and steps while the data pipeline is being done, from the ingestion of data until delivered to the destination.
- Workflow. It is defined by the order of actions and their dependencies independencies of them in the process.
- Monitoring. Ensuring the accuracy and efficiency of the process is relevant to data pipeline ad network congestion, and failure may occur.
Organizations rely a lot on data; there as time goes on, their data keeps on filing and increasing the more the demand of efficiency requirement. Hence, data transfer and transactions happen from time to time. So, in order to keep up with the volume of data, data pipeline tools are needed.
What is a Big Data Pipeline?
Drastic of increase of data regularly increases, therefore as a countermeasure, big data adaptation was developed. As its name suggests, big data is a data pipeline that works on a massive volume of information. It functions the same as the smaller ones but on a bigger scale. Extracting, transforming, and loading (ETL) of data can be done on a large scale of information in this pipeline, which can be used on real-time reporting, alerting, and predictive analysis.
The same with lots of data architecture components, in order to process huge data scale innovation of data pipeline, these are necessary. Production of data with the help of a big data pipeline becomes much flexible than the small ones. Hence, to accommodate a tremendous amount of data is how it came to life. It can process streams, a batch of data, and many more. Varying formats of data can be operated like structured one, unstructured and semi-structured information unlike the regular. But scalability of a data pipeline based on an organization’s necessity is very significant to be an efficient big data pipeline. The absence of scalable property of a pipeline could affect the variable of time for the system to complete the process.
There are industries or organizations that require big data pipelines than the others. Some of those are the following;
- Finance and banking institutions analyze big data for the improvement of services
- Healthcare organizations that work on a variety of data related to health
- Educational Institutions which work on many student information
- Government organizations employ big data pipeline on a large scale as they cover data analysis of various data that concern government affairs
- Manufacturing companies use pipelines on a huge scale to streamline their transactions
- Communication, media, and entertainment organizations apply big data in real-time updates, improvement of connection and video streaming quality, and many more
- Huge corporate businesses that evaluate and analyze a large amount of information. They use a big data pipeline to streamline company transactions, processes, and productions
Considerations in Data Pipeline Architecture
Architectures of data pipelines require a lot of considerations before building one. Some of these can be answered by the following questions:
- What are the pipelines for? What is the purpose of it? Why would you need to create one? What accomplishment do you want to achieve with it?
- What amount of data do you wish? What are data will you work on? Is it streaming, structured or not?
- How will the pipeline function? What will be the scope of the data that will be processed? Will it be used for gathering reports, demographic files, general education information, and so forth.
What is Data Pipeline Architecture?
It is the strategy of designing a data pipeline that ingests, processes, and delivers data to a destination system for a specific result.
Data Pipeline Architecture examples
Batch-Based Data Pipeline
In this example, it involves processing in a batch of data that has been stored, such as company revenues for a month or a year. This process does not need real-time analytics as it processes volumes of data stored. Use of point-of-sale (POS) system, an application source generating huge data points amount to be carried or transferred to a database or data warehouse.
Streaming Data Pipeline
This example, unlike the first one, involves real-time analytics operations. Data coming from the point-of-sale system is being processed while being prompted. Besides carrying outputs back to the POS system, streams processing machine delivers products from the pipeline to marketing apps, data storages, CRM’s, and the likes.
This data pipeline is a combination of batch-based and streaming data pipelines. Lambda Architecture can do both stored or real-time data analysis. Big data entities often use this example.