Overview of Data Pipeline

A data pipeline is a set of tools and processes for collecting, processing, and delivering data from one or more sources to a destination where it can be analyzed and used. Nowadays in the 21st generation, we must cope with each and every piece of information or data we get. When we usually hear about pipelines, 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.

Overall, a well-designed data pipeline is crucial for organizations to leverage their data effectively, support decision-making, and gain insights that drive business success. In this article, we are going to learn Data Pipelines in detail.

What is a Data Pipeline?

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

Data Pipeline Overview

Why Data Pipelines are important?

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

The improvement of the cloud has meant that modern technology for enterprises 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 mostly depend on Salesforce to handle and some might use MongoDB for storing customer approaches. This leads to the waste of data across different tools and results in data silos. Data silos are nothing but they will create it difficult to fetch even business insights, like your most profitable market. It is most 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 transfer it to the destination. Batch transforming and processing are two common methods of development. Then there is a decision on 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, has built its own data pipeline. However, building your own data pipeline is very difficult and time is 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 the main component of a common data pipeline. These are –

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

Types of Data Pipelines

Here are the following types of data pipeline:

Types of Data Pipelines

• Batch Data Pipelines: Interact with large portions of data all at once and at some specific time of the day.
• Real-Time Data Pipelines: Interact with data at the time of its creation for almost real-time outcome.
• Cloud-Native Data Pipelines: Built for running in cloud environments which are more malleable and more flexible.
• Open-Source Data Pipelines: Created with the implementation of the open-source technologies like Apache Kafka, Airflow or Spark.

Data Pipeline Architecture

Here is the representation of data Pipeline Architecture:

Data Pipeline Architecture

• Ingestion Layer: It retrieves data from an assortment of sources ranging from databases to APIs or even event streams.
• Processing Layer: Another operation on data involves the analysis of the said data followed by data cleaning through tools such as spark or Hadoop.
• Storage Layer: Held in data lakes, warehouses or other databases, data was kept for future reference or as a back up to be analyzed later.
• Monitoring Layer: It is the authority that is charged with the responsibility of providing quality data in the right time and increasing the efficiency of the system.
• Consumption Layer: Delivers the final data to BI tools or machine learning models where the data is analyzed at the next level for decision making.

Data Pipeline vs. ETL Pipeline

Here’s a comparison between a Data Pipeline and an ETL (Extract, Transform, Load) Pipeline in a tabular format:

In summary, while both data pipelines and ETL pipelines are used to manage and process data, ETL pipelines are a specific type of data pipeline with a focus on the Extract, Transform, and Load stages. Data pipelines have a broader scope and can include additional processing and delivery stages.

Some of the use cases of data pipelines:

• Real-Time Analytics: Benefits real-time analytics and operational decisions in areas such as finance or healthcare among others.
• Machine Learning: Transmits data obtained after analysis from multiple feeds to the ML models for analysis and automation.
• Data Migration: Move data from one system to another especially when one has been enhanced or in the process of migrating to cloud environment.
• Data Integration: Combines two or more databases to look at the data with one common database.

Future Improvements Needed

In the 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 of the various reasons underlying the growing need for improving data pipelines:

Finally, most 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 businesses can need to utilize high-quality resources to develop it and 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.

Conclusion

In Conclusion, A data pipeline is a mechanism that facilitates the effective transfer of data from its point of collection to its intended destination. It include collecting the data, preparing it for use, storing it securely, processing it to extract insights, and providing it to tools or systems. Through this method, organisations may use their data more efficiently and make better decisions. Data pipelines are crucial in order to maintain uninterrupted flow of data for analytics, machine learning, and business intelligence. They help in the organization of the data so as to facilitate the extraction of insights from a variety of data sets.