How To Productionize Data Pipeline?

How To Productionize Data Pipeline?

Productionizing your data pipeline is the process of turning one-off data ingestion processes into repeatable, automated, and scalable strategies that can be deployed to production at any time. To do this in a way that’s both fast and safe, you need to build a pipeline that takes advantage of the distributed and parallel nature of the processing being done.


It’s one thing to productionize a data pipeline and keeps it working, but it’s another thing to do in a way that makes sure that you can scale it up for growth.


This blog post will walk through the steps involved in building a data pipeline that can be productized. We will also give a brief overview of the technologies involved and the motivation for using each. I hope you enjoyed reading it. Please share it with anyone who might find it interesting as well.




The Data Pipeline: Designed for 100% Efficiency:

To survive efficiently in today’s world, businesses need to create modern data pipelines that ensure easy extracting, combining, transforming, validating, and loading of data for their utmost value and use. 


Data Pipelines unify all the business data from increasingly disparate sources and formats, making the figures and collected information suitable for business and analytics intelligence. Moreover, it also provides all the team members with their required information, keeping the business’s privacy intact. 


Why Opt for Data Pipelines?

Data Pipelines is a term that describes how data is moved from one place to another. For instance, when it comes to business-to-business (B2B) marketing, this usually means moving data from your CRM (customer relationship management) system to your marketing automation system so that you can create campaigns and send out emails.


Businesses need to store all their information for future analysis. But, one wrong step in this storage process can mess up all the present data, leading to a situation where team members are havoc.


On the other hand, Data Pipeline eliminates the manual storing steps, replacing the same with smooth and automated data flow from one station to another.


It starts with segregating which type of data is needed and from which device or folder. It further involves the process of refining, extracting, combining, and loading the required files.


Data Pipelines provide end-to-end velocity to the users, systems, and businesses that eliminates the chances of any errors, combatting latency or bottlenecks. It can also process multiple data streams, an absolute necessity for all data-driven businesses.


Steps To Productionize Data Pipeline:

It can be a simple data extraction process and handle advanced details like the training datasets required for machine learning. Five basic steps for data pipeline include:

1. Source:
Sources for the targeted data can be from SaaS applications or relational databases. Most of the data pipeline process includes collecting the refined data from multiple systems and updating the batch at regular intervals for new additions.


2. Transformations:
After collecting the data, it needs to be transformed according to to set formats, making the identification process more accessible. Data transformations include sorting, validation, standardization, verification, and deduplication.


3. Processing:
There are two processing methods for data pipelines. Steam Processing includes the updates in data when they are manipulated, sourced, and loaded. Batch Processing refers to updates in data in specific periods that have to be sent to the targeted destinations.


4. Workflow:
Dependency for the Workflow process is optional and selected according to business type. It refers to sequencing the data and their dependence on the management process.


5. Monitoring:
No matter how securely you process the data pipeline technique, there are always 10% chances for failure, either due to offline destination/source or network issues. Therefore, the pipeline must have a backup mechanism for these failures, ensuring complete data integrity.


What Is The Difference Between Etl And Data Pipeline?

ETL stands for extract, transform and load. ETL and Data Pipelines are often confused with each other. Clarifying the fundamental differences, ETL is the best practice for extracting data from particularly one system, transforming it according to the new formats, and loading it in the existing data warehouse.


In ETL, all the data is moved at a specific time in large chunks in the defined system, providing the option for scheduling their process when system traffic is lower.


Data Pipeline is an extended term, with ETL being a part of this technique. Data Pipeline refers to the process of moving data from one system to the other, but unlike ETL, it is not transformed and done in specific time batches. Instead, this technique is processed in real-time, defining it with a continuous flow of valuable data requiring constant and frequent updates.


Who Needs Data Pipeline?

Data Pipeline is not necessary for all businesses, but all data-driven enterprises must follow this technique as a ritual for future smooth processing.


The technology is hopeful for all businesses that:

  • Maintain Proper Format for all their Data,
  • Rely on, Store, and Generate their values from multiple data sources,
  • Use Cloud to Store Data,
  • Highly Sophisticated or Real-Time Data Analysis.

Get Started With Data Pipeline:

Data Pipelines can be considered the backbones for all the technical and digital systems, moving, refining, and transforming the data for easy working of the enterprises. But it’s not as simple as it seems to be!


Data Pipelines need to be monitored and modernized according to the complexity and size of the data, requiring enough time and effort for easy completion.


Get Assisted with the best AI and technical data pipeline organizations to eliminate all the chances for failures, assuring easy task completions.

Recent Posts

Subscribe to Newsletter

Stay updated with the blogs by subscribing to the newsletter