In your data-driven business, you need to move data efficiently from one point to another and convert it to useful information as fast as possible. However, there are many barriers to smooth data flow, including bottlenecks resulting in latency, redundant information, data corruption, and conflicting information from multiple sources. Data pipelines turn the process into a clean, automated workflow by taking all the manual steps needed to solve existing problems.
What is a data pipeline?
A data pipeline is a series of data processing steps. Data is ingested at the beginning of the channel if it is not currently loaded into the system. It is then followed by a series of steps, whereby each step gives output that acts as input for the subsequent stage. Although independent steps may run parallel in some cases, the process continues until the pipeline is complete. Data pipelines are composed of three elements; the source, logical steps, and the destination, also known as the sink. When the channel is entirely about modifying the data, it can have the same source and destination. Some of the benefits of using data pipelines include the following.
- You can create, store and rely on large amounts of data from multiple sources.
- It allows you to tap into cloud storage technology
- Data pipelines make it easier to maintain soiled data
- It is dependable on complicated or real-time data analysis
- They improve security by restricting access to authorized personnel only.
Before you embark on designing your data pipeline system, you need to understand its architecture, the tools required, and how it works. The following tips will guide you on how to build a data pipeline properly.
Data pipeline architecture
A data pipeline architecture is a complete system designed to record, arrange, and send data for productive insights. You need to know what is data pipeline architecture and its importance. The architecture provides the best design to manage all data events, data analysis, making reports, and simplifying usage. A pipeline architecture is also used by data engineers and analysts to improve business intelligence and targeted functionality. Data is essential in business intelligence and analytics in acquiring insight and efficiency in real-time information and trends. You can track customer journeys, automate robotic processes, target customer behavior, and review user experiences through data-enabled functionality. Data pipeline architecture can be broken down into the following parts.
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Sources
The source is usually where all processes begin and where the information originates from. It involves different sources, including the cloud, relational databases, and application APIs.
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Joins
When data flows through the pipeline, it is always combined with data from other sources. Joins are crucial as the criteria and logic that explain how different data components are linked to each other.
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Extraction
In some cases, your business may need multiple values extracted or put together. You can remove specific data fields, such as the prefix of a telephone number.
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Correction and standardization
Almost all data contains errors, which could be something as small as a non-existent zip code. Correction ensures that your data is error-free and also deletes corrupt records. On the other hand, standardization ensures that your information is presented acceptably and uses the exact measurements consistently.
You should know that data pipelines might experience broken connection or dependencies, rate arrival of data, and unreachable external APIs or systems. Various reasons cause the failures. However, you can follow the following tips to alleviate the effects of a data pipeline failure.
Ensure that the single components are small
When you thread your entire workflow as a single giant script, you will find that your data pipeline becomes hard to maintain. In most instances, you will not know the cause of the problem until you analyze your logs. However, you can identify the failed task in the pipeline and concentrate only on fixing it if you organize your data pipeline as a workflow rather than a single script. Another problem with managing workflows as scripts are you need to rerun the entire workflow to repair your data pipeline once you identify the error. Therefore, it becomes costly to manually rerun the whole pipeline if the issue reappears in the future. Workflows are executed in a particular order and run at specific times to guarantee data availability and reliability and ensure that dependencies are met.
Use available or existing tools.
As with most tasks, simplicity is key in data and software engineering. It is always better to use existing tools to maintain your data ecosystem rather than find your solution. In the past, most companies deemed workflow management to be so simple that they built their workflow coordination systems. However, they soon realized that they needed a database for them to store schedules and dependencies information. After the database, they had to incorporate logic and validation rules to avoid errors. If you take that approach, you will be maintaining the system for data engineering rather than keeping your data pipelines. The best option would be to use the existing tools to make your work easier and allow you to focus on other essential aspects of your business.
Minimize dependencies
It is always best to make your data pipeline as independent and small as possible. However, minimizing dependencies can be an issue when you have many downstream projects relying on data from the initial steps in your pipeline. The best solution to the case would be to incorporate parent-child relationships into your channel. In a parent-child setup, the parent pipelines ensure that the child pipelines are executed in the required order. It also prevents the risk of ending up with a single channel with thousands of unrelated tasks triggered in the wrong order.
Monitor your pipeline
Ensure that you have mechanisms in place to notify you in case your data pipeline fails. You can get notified through a method you think is convenient to you, such as through email. Have a system in which you have people constantly checking out the notifications and fixing errors. Build a system whereby everybody feels the responsibility to fix the issues to improve efficiency.
A properly constructed data pipeline is crucial for any organization keen on improving service delivery efficiency. There are many readily available tools in the market to help you build your data pipeline. Ensure that you follow the above tips to guarantee a smooth and reliable system. You must research and find out your organization’s needs before building one. With an intelligent approach, you can alleviate the impact that a data pipeline’s failure may have on your business.
