What is Data Pipeline and how it's used in business?
Apr 14, 2021 ● 9 min read
Anyone who spends some time in a corporate environment is bound to hear a term like data pipeline. It is also used frequently in relation to data analysis or business analysis, so it clearly indicates something important. Actually, a data pipeline has an important role to play when it comes to gaining business intelligence and creating a valuable database. Here we will explore the topic of the data pipeline, explain what it is, how the process works, and what are tools or applications used for creating a meaningful database.
Table of Contents
What is Data Pipeline?
In order for your business to grow or to make impactful improvements to your product and services, you need relevant data. This can be user feedback, sales numbers, etc. In order to obtain relevant information, you will have to aggregate data from multiple sources, and that's where the data pipeline comes into the picture.
A data pipeline is a process or set of actions that involve gathering raw data from multiple databases and sending it to a specific destination system or storage. The gathered data then undergoes cleansing, or data transformation, in order to be adapted or adjusted for data analysis. To that end, a pipeline might also include features that filter the data or isolate specific bits of information prior to sending it to the destination system. Some can also refer to pipeline as ETL since some streaming data solutions can also perform data transformations prior to loading data to its destination.
Data Pipeline Process
To understand how the data pipeline works or how it can be useful, we will have to break down its process. You can think of it as a tube or tunnel that carries information from one or multiple sources into specific storage for later use.
Based on the data pipeline software or methods you are using, the information can be altered before it reaches its destination. So it can be a simple approach that just involves extracting data from one source and loads it into another database. Or there can be some filtering involved throughout transaction processing. So here are the key elements in this equation.
Source - Data sources are relational databases that the pipeline draws upon via a push mechanism or using an API call. If someone is constantly relying on those data sources for their content, it needs to be synchronized in either real-time or using frequently scheduled intervals. We can see this in dropshipping practices when an e-commerce website displays content from a different website as available merchandise.
Destination - this is a system or storage where data is displayed or stored after extracting information from source systems. It can be cloud-based storage or data lakes, or it can be its own application that immediately uses transferred inputs.
Transformation/data cleansing - this can indicate a variety of different processes like standardization, deduplication, validation, removal of irrelevant inputs, and formating. This is an integral process that allows for data to be accurately analyzed and displayed. It is basically a set of activities that have to occur in order for raw data to become processed data.
Processing - although it may sound like it's exactly the same as data transformation, this action indicates something a bit different. It refers to either batch or stream processing. Batch processing is when collected data is processed or transformed periodically or in batches. Stream processing is when a data stream is processed while in the pipeline or before it is loaded in a new database.
Workflow- workflow indicates the level of dependency in the data processing. This dependency can be technical or corporate. In other words, it details how the collection and validation of data are streamlined. If the process is automated or technical, that means that system needs to verify data or some of its components before it is released into a new data storage. Corporate or business dependency is when information needs to be cross-verified by personnel before it is released.
An example of technical dependency would be uploading images and using a format that is not supported by the platform or exceeding the size limit of the file you can upload. So the data is validated before it can be uploaded.
An instance of business dependency can be found in banks. For example, a bank might have a policy that two payment officers need to validate a certain payment before it is released for processing, so only after those actions are approved manually can the information be released. Here we can also find an example of combined dependency. Payments that don't go over a certain threshold are automatically processed by the system, whereas larger transfers might need approval by payment officers or a compliance team.
Monitoring - these are mechanisms that were set up in order to ensure data integrity. The purpose is to allow administrators to monitor data transfers and to become alerted if there is a data transfer that does not comply with the established ruleset.
Data Pipeline Solutions
There are multiple data pipeline solutions, and they are mainly tailored to fit the needs of different types of businesses. So depending on your business model and how you wish to leverage data, you dictate the type of data pipeline solution you will use.
Whatagraph is a great example of a data pipeline tool. It is used for monitoring and transforming data into a summary with great visuals. This is an amazing way to share business insights, and in the event, you need your project report to look even more professional, you can white label it. Whatagraph also has numerous integration options, so streaming data will be quite easy. It's a solution you can use for big data analysis or monitor data flow, and it gives the option for designing impressive analytics since visuals and presentation are also important.
In general terms, data pipelines can be divided into four categories, and some of them were already mentioned throughout the text.
- Batch - When you need to move large volumes of data, batch processing is an ideal solution. This is because you don't have to do it in real-time, so you can just extract a range of data, like from a specific time frame, and process it later.
It is something that is done when you need to integrate marketing data, for example, into a larger system. Another example is if you wish to improve customer experience or come up with incentives for customers, you will have to capture data on how they behave. It is a type of data that should not be processed in real-time, as other variables need to be examined for more accurate analysis.
- Real-time - Some tools or algorithms can actually process data in real-time and adjust user experience according to those inputs. We can see this if we watch Netflix since it has a large database of content that users value in accordance with their own personal tastes. So, when we watch a show, Netflix starts recommending similar titles, assuming we have watched the show or movie in its entirety.
- Cloud-based solutions - Data pipelines hosted in the cloud are budget-friendly solutions as they come with existing infrastructure and a large data storage capacity. These are also secure solutions, so it's less likely someone will steal your data if it is backed in the cloud. It's also easy to manage data streams and to integrate with cloud-based solutions, which is why they are so popular.
- Open-source - If you need solutions at the lowest cost possible, then open-source data pipelines are a great option. The downside, however, is that these are not necessarily user-friendly, and you might need an in-house developer who knows how to navigate and modify these tools.
- In-house solutions - One problem with pre-made or commercial data pipelines is that they do not fit some specific business needs. They are made to accommodate a large user base and allow them to monitor their marketing or sales campaign. However, for more complex analytics and data aggregation or transformation solutions, you might have to look inwards.
In other words, the whole data capture and transformation process might require features or filters that need to be specifically designed for your business. In those cases, companies simply cash out the development of such data pipelines. Moreover, there are instances where companies are using in-house platforms or tools that cannot be integrated with the desired data pipeline, so instead of creating their own solution, they simply work developing an API that can integrate with that specific system.
When coming up with data pipelines, there are two main issues that data engineers need to address, and they are speed and scalability. If you want a data pipeline that is swift, you need to focus on a low latency tool that can provide crucial and essential information in a short time period. For a more comprehensive data analysis and business insights, a data scientist might want a more comprehensive tool with lots of entry fields that capture relevant information.
Data Pipelines vs. ETL
ETL - or extract, transform, then load - is often used interchangeably with the term data pipeline. Truth be told, there are minor differences. ETL pipelines are sort of more advanced or fine-tuned data pipelines. ETL is a process of extraction, transformation, and loading, something we already mentioned throughout the examples. The data pipeline is broader and basically refers to any action related to data streaming or moving data from one source to another destination or data lake. Throughout that process, the data is not necessarily transformed - it's just moved there as raw data which needs to be processed afterwards, similar to batch processing.
The idea behind ETL tools is to maximize your efficiency in data analysis. Data science relies on these tools to clean data while it is being transferred. This allows developers or data scientists to replicate raw data from multiple or disparate sources and define what type of transformation data needs to undergo once it is loaded into a new data lake.
What is meant by data pipeline?
Data pipelines are tools that enable the flow of data from one or multiple sources, like applications, platforms, or storage systems, into a designated data warehouse. It is also possible for a data pipeline to have the same source and sink. In this instance, it is mainly used to filter data within the data lake. Social media platforms have algorithms that track your user session and adjust their data pipeline according to those inputs. This is one instance where the source and the sink are the same. To put it bluntly, it is a tool used to help us analyze data more efficiently by isolating targeted chunks of information.
What is the purpose of a data pipeline?
The main purpose of the data pipeline is to establish the flow of information between two or more data sources. Its secondary purpose is to transform the information or adjust it in a way that is easier for data scientists to load it into their template. We can calibrate data pipelines to transfer the information in a way that it is easier for us to manage it afterward.
How does a data pipeline work?
The most basic function is to copy or capture data from one data source into a specified data warehouse. Each data pipeline can work differently depending on our business needs. If we are only targeting specific information, the assimilated data can be qued up and validated automatically before it is deposited into other data stores. An example is website cookies that are used for creating a more personalized experience on a website. They capture information that is specific to your user session, but they filter out your personal information.