The Business Benefits of Data Pipelines

Concept of Data Pipelines

The concept of data pipelines is not new; many businesses and organizations have used this technology for decades to arrange and manage their valuable data.

The reason data pipelines are becoming more imperative is the rapid growth in business data every year. They are a necessity in the current world of business.

What is a Data Pipeline?

A data pipeline can be explained as a series of steps that move raw data from its original source to a final destination. It combines different software technologies to automate functionality like unification, visualization, and management. This helps to manage disparate data structures in a strategic way.

The source is a transactional database and the destination is referred to as the data warehouse or data lake. The destination is where the data is analyzed and business insights are formed.

But, a few elements that affect the complexity of the data pipeline are:

• Number of different data sources (business systems)
• Type of connectivity to the data sources
• Volume of data
• Types of data sources, whether they are complex or straightforward
• Velocity of data

Why do You Need Data Pipelines?

To drive faster business outcomes, we generally rely on data pipelines. The best part about the modern data pipeline method is that you can enable your business to efficiently unlock the data in your organization’s range.

It allows you to extract essential information from the source and transform it into a usable form. This method also allows you to load the transformed data into your systems, where you can easily access it.

When used by a professional, data pipelines can dramatically change your business growth and how you are running your business. The technology alone confirms that it’s sufficient in providing business wins after the right implementation.

What Kind of Data Pipeline Tools are there?

Data pipelining tools come in various types depending on the requirements and use cases, including:

• Batch Processing — This is the best data pipelining tool for moving numerous large data files. However, this is not the right method for real-time data file transfer.
• Cloud-native — Best tools for cloud-based data, like AWS Lambda for serverless, compute Amazon Web Services (AWS), or Microsoft Azure.
• Open Source — Several organizations demand open-source data pipelining tools. One popular option is Apache Kafka.
• Real-time — Best for streaming data sources, such as finance, the Internet of Things (IoT), and healthcare.

Data Pipeline Process

To learn how a data pipeline functions, think of a pipe that receives input from a stream and delivers it to a destination. The path of the information depends on the business utilization case and the end itself.

An information pipeline is a modest method of information extraction and processing. It can be designed to regulate information in a refined way, like exercising datasets for machine learning.

• Source: Data references include relational databases and information from SaaS apps. More pipelines gather raw information from numerous references through a push mechanism, an API alarm, a counterpart engine that heaves information at frequent periods, or a webhook. Furthermore, the information might be harmonized in real-time at a slated duration.
• Destination: An end might be a data stock — like an on-premises and cloud-based information storage, an information lake, or a data store — or a BI or analytics app.
• Transformation: Transformation pertains to functions that change information. This may be data standardization, organizing, eliminating duplicate data, assurance, and confirmation. The objective is to make it feasible to evaluate the information.
• Processing: There are a couple of information induction models: bunch processing, in which base information is compiled occasionally and mailed to its final network, and stream computing, in which information is based on, altered, and stacked when established.
• Workflow: Workflow pertains to organizing and reliable administration of methods. Workflow dependencies could be technological or business-based.
• Monitoring: It’s mandatory to have a monitoring component in data pipelines. Having a monitoring component in data pipelines ensures data integrity.

Benefits of a Data Pipeline

1. Replicable patterns

Data pipelines introduce a new way of thinking regarding data processing as a pipeline network where each path is part of a wider architectural pattern. In this way, new data flows can be revived, repurposed, and reused.

2. Faster timeline for integrating new data sources

New data sources can be integrated into an existing pipeline network quickly and easily.

Furthermore, there is a significant reduction of time and cost when incorporating data pipelines into your business.

3. Confidence in data quality

Data pipelines greatly improve end-user productivity and overall data integrity, reducing lost time and money caused by errors in your data.

4. Incremental build

Integrating your data with data pipelines enables you to grow your dataflows incrementally. You can start early and gain value quickly by starting with a small manageable slice from a data source to a user.

5. Flexibility and agility

Data pipelines provide a framework so you can respond quickly and easily to changes made in the source.

6. Business Autonomy

All business data is organized and normalized in one unified location for easy access and streamlined workflow.

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Visual COGS is an e-commerce analytics tool that converts your raw data into actionable insights by implementing data pipelines.

We extract your data from e-commerce platforms like Costco and Amazon and translate them into information that will help you skyrocket your business’s growth.

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