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Overview of Data Pipeline

  • Last Updated : 19 Aug, 2021

Now-a-days in 21st generation we must have cope up with each and every information or data we get. When we usually hear about pipeline, we suddenly think about those natural gas and oil pipelines that carry those resources from one location to another over long distances. But here we are going to know about the data pipelines.

Data Pipeline :

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Data Pipeline deals with information which are flowing from one end to another. In simple words we can say collecting the data from various resources then process it as per requirement and transfer it to the destination by following some sequential activities. It is a set of manner that first extract data from various resources and transforms it to destination means it processes it as well as moves it from one system to another system.

Why Data Pipelines are important ?

Let’s think about a scenario where data pipeline is helpful.

The improvement of the cloud has mean that a modern technology with enterprise uses lots of apps with different features. The retailing team might employ a combination of Hub spot and Market for trading automation. The other retailer teams are mostly depend on Salesforce to handle and some of might use MangoDB for storing customer approach. This leads to the waste of data across different tools and results in data silos. Data silos are nothing but it will create it difficult to fetch even business insights, like your most profitable market. It is mostly important for Business Intelligence(BI) in their day to day life they require  everyday information to work with.  

How to build a Data Pipeline :

An organization can decide the methods of development to be followed just to abstract data from sources and transferring  it to the destination. Batch transforming and processing are two common methods of development. Then there is a decision of what transformation process- ELT(Extract/Load/Transform) or ETL -to use before the data is moved to the required destination.

Challenges to building Data Pipeline :

Netflix, have built their own data pipeline. However, build your own data pipeline is very difficult and time-taken.

Here are some common challenges to creating a data pipeline in-house:

  • Connection
  • Flexibility
  • Centralization
  • Latency

Components of Data Pipeline :

To know deep about how a data pipeline prepares large datasets for deconstruction, we have to know it is main components of a common data pipelines. These are –

  1. Source
  2. Destination
  3. Data flow
  4. Processing
  5. Work flow
  6. Monitoring

Future Improvements Needed :

In future, the world’s data will not be stored. This means in exactly some years data will be collected, processed, and analyzed in memory and in real-time. That indication is just one amongst the various reasons underlying the growing need for improving data pipelines:

Finally, most of the businesses today, have an extremely high volume of data with a dynamic structure. Creating a Data Pipeline from scrap for such data could be an advanced method since business can need to utilize a high quality of resources to develop it then make sure that it will continue with the increased data volume and Schema variations. Many more data engineers offer a bridge between data and business to make everyone’s life easier behind the easier access we get recently data engineers put their hard efforts, besides those people no other group can offer.

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