Nowadays, in the field of data, the term "data pipeline" has become quite popular.
If you are one of those people who doesn't understand it at first, you’ve come to the right place.
This article will discuss its foundations and explain some of its fundamental concepts, such as what a data pipeline is, its inner workings, components, and required tools.
OK, let's get started then.
Hold your horses! Before we answer what a data pipeline is, I think it is better to visualize it first and then answer the question.
OK, I know most of us are familiar with water. That’s right - water that comes out of your faucet inside your kitchen.
And you might be wondering, what's the relationship between water and a data pipeline?
Not exactly the water, but we can compare a water supply distribution system to a data pipeline.
See the diagram below.
Both a water supply distribution system and a data pipeline, have a source, repositories, and consumers.
Moreover, as you can see, water comes from a particular source.
It goes to a warehouse where it is stored, and this warehouse has a pipeline where the process of cleansing occurs and then goes to everyone's house, ready to be consumed as potable water.
In the world of data, a data pipeline is a form of transfer from point A to point B, and somewhere in the middle, processing occurs.
This processing can be any or all of these, such as data staging, cleansing, and conforming.
Now that we have an idea of how data pipelines work, let's get into more detail in the next section.
An organization needs good quality data.
These quality data results come from the data pipeline; once sent to data consumers, other applications, like a visualization tool, machine learning models, reporting, and business analytics, will eventually consume it or analyze it.
In simple terms, the organization needs a data pipeline because it is expected to produce good quality data that consumers will use to give the organization the data needed to have sound decisions based on data gathered.
As you can see, it is essential to have a data pipeline for an organization because of its different reasons for usages.
Therefore, an efficient flow of data is vital because there could be possible errors along the way between steps. It could be bottlenecks, corrupted data, invalid data, or other errors.
The larger the dataset may cause it likely is to have issues, and the errors can be harmful overall.
Data Pipeline Types
The batch-based data pipeline is the simplest of the 3 data pipeline architectures because it only has a few steps that data goes through to reach its destination.
An excellent example of a batch-based data pipeline is using a traditional database, mainframe, CSV, etc.
We can take a look at the sample diagram below.
The streaming data pipeline is versatile and used for real-time streaming. Lastly, it feeds the output to multiple applications at once.
An excellent example of a streaming data pipeline is when you wanted to analyze a vast pool of in motion data through continuous queries, called event streams.
We can take a look at the sample diagram below.
Lambda data pipeline is the best of both worlds' combination of batch and streaming data pipeline.
We can take a look at the sample diagram below.
What are the Components of a Data Pipeline?
Staging (Ingest)
This is when the storage of raw/unprocessed data occurs from different data sources before processing it.
The location may be file storage or database tables.
Cleansing (Prepare)
This is the stage where data cleaning occurs, such as removing redundant and insufficient data, checking for missing data, extra white space around the text, etc.
In simple terms, this is the stage where the manipulation occurs. Not only that, but this stage is also where the notification and error logging occur.
Confirming (Transform & Analyze)
This is the go-or-no-go stage and checks whether all data are good quality and double-checks the data if safe to proceed to the next step.
Delivering Analytical Data Sets (Publish)
This is the last stage, but in this stage, we still need to run some additional tests of data, and if something went wrong, we could run an automated rollback.
Else whenever everything is successful, the published data can be utilized by a visualization tool, machine learning models, reporting, business analytics, etc.
What is a Data Lake?
It is unavoidable that every organization's data will grow sooner or later, and it isn't easy to manage.
Organizations will start to store and process this unstructured data such as images or logs along the way.
Thus, this is where data lakes come in, and it helps the organization centralize their structured and unstructured data by creating a single source of a repository of all the data. Yes, all of it.
A data lake acts as a central repository that holds an extensive amount of structure, raw, and unstructured data in its native form; this storage capability gives an organization the flexibility to keep valuable data and remove non-essential data in the future.
Data Lake Management Platform
To most organizations, it is useful to consider a data lake management platform like lakeFS. They can manage object-based storage data lakes, which can feasibly integrate into your existing tools.
Not only that, but it is also compatible with Amazon S3 and Google Cloud Storage.
Object storage, for the uninitiated, stores your data in the form of objects so that they are easier and faster to retrieve thanks to a non-hierarchical structure.
Let us see what frameworks are supported by lakeFS by looking at the diagram below.
Summary
This post answered what a data pipeline is and why a data pipeline is vital for an organization, its types, and its components.
Not only that, we have introduced a data management platform, which, in our case, we have discussed lakeFS as the platform.
I hope you have enjoyed this article, as much as I have enjoyed writing it. Stay tuned for more. Until next time, happy programming!
Please don't forget to bookmark, like, and comment. Cheers! And Thank you!
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