Suman is a Data Scientist working for a Fortune Top 5 company. His expertise lies in the field of Machine Learning, Time Series & NLP. He has built scalable solutions for retail & manufacturing organisations.
For enquiries call:
+1-469-442-0620
HomeBlogData ScienceData Science Pipeline: Steps, Diagram, Tools
The rate at which data is generated on our planet per hour has increased exponentially over the past few years. This surge in volume, velocity, and variety of data being produced has opened the doors to opportunities that were non-existent before. Leaders, Managers, and Stakeholders from different industries are more excited than ever before to unleash the hidden profits and efficiency gains that they can gain with their data. Data Science pipelines are the channel that bridges together the gap between raw data and intelligent insights. In this article, we’re going to dive deeper into Data Science pipelines and how different industries are using them to gain a competitive edge.
We can think of a data science pipeline as a unified system consisting of customized tools and processes which enable the organizations to get the maximum value out of their data. Depending on the factors like scale, nature of the problem at hand, domain, etc, the data science pipeline can be as simple as a simple ETL process, or in other cases, it could be very complex consisting of different stages with multiple processes working together to achieve the final objective. To get a deep understanding of the Data Science Pipeline, you could refer the Data Science Bootcamp Curriculum.
The data science pipeline of any organization is a fair reflection of how data driven the organization is and what’s the influence of derived insights on business-critical decisions.
Here are some of the points explaining the importance of a data science pipeline for an organization:
Enroll in the best Data Science Courses in India to understand the significance of creating a robust Data Science pipeline.
Get to know more about career with data skills.
Once deployed, the data science pipeline works by triggering a chain of steps called Stages of the pipeline. Each stage is usually responsible for achieving a specialized task e.g. extraction, loading, cleaning, transformation, modeling, evaluation, deployment, etc. The data science pipeline works by ensuring all the steps are triggered in the pre-configured fashion and it’s also responsible for emitting events which helps the external observers to monitor the different stages for debugging or other purposes. The Knowledgehut Data Science bootcamp curriculum explains how a pipeline should work.
Following are the various stages of the Data Science Pipeline :
Know more about role of mathematics in machine learning.
Following are the benefits of the Data Science Pipelines:
Know more about types of probability distributions every data science expert should know.
How different industries use data science pipelines vary by use case. Below are some examples:
There’s already a lot of innovation going on around making the data science pipelines more and more autonomous and driven by continuous manual or automated reinforcement in the form of the feedback based on the past results emitted by the pipeline. It’s very likely that the next generations of data science pipelines would be more and more autonomous with less and less requirement of manual intervention past the initial start.
In the digital era where tons of bytes of data are generated every hour by humans and machines ( IOT ), it’s no more a privilege to have data science pipelines setup in order to gain useful insights rather it has become a necessity and the organizations which are early adopters of the data driven approach are already starting to reap the benefits of their data whereas the other organizations are also either in-progress or heading fast towards building their own pipelines.
A Data Science pipeline generally follows the below steps -
ETL refers to “Extract -> Transform -> Load” pipeline, as the name suggests ETL pipeline refers to a set of tools which help with extraction, transformation and loading of data. A typical example of an ETL pipeline is the ELK stack.
Data Sourcing or Data Acquisition is the first step in any data science pipeline. Here data is made available for further stages.
The purpose of the data pipeline is to contribute to one of the several phases of the journey from raw data to useful business insights.
ETL is a subset of the data pipeline. It is a series of processes extracting data from source, then transforming it and finally loading it to the destination. Whereas data pipeline describes a set of processes which move data from one system to another, sometimes transforming the data sometimes not. It’s a series of steps where data is moving.
Name | Date | Fee | Know more |
---|