Data isn’t useful to an organization in its raw state. To analyze and glean insights from the data, companies need to get data from various sources and bring it to a destination database. Custom data pipelines are the way to do that seamlessly.

A custom data pipeline is a set of actions that take raw data from various sources and move it to the target repository for storage and analysis. It has three key parts:

1. Data sources: Your pipeline will start by extracting data from your connected data sources. 
2. Processing steps: Generally, these pipelines include some form of data processing such as filtering and masking to ensure the data is ready for use once it arrives at the repository. 
3. Destination: Once your data has gone through all the processing steps you specify, the pipeline will bring it to your selected destination such as a data lake or lakehouse. 

The number and types of different data sources, the velocity of data, and the connectivity to your data sources all affect the complexity of your data pipeline. 

Organizations use pipelines to ensure that all the raw data they collect is available for convenient analysis. Once the data goes through a company’s custom pipeline, it can serve as the foundation for a variety of data projects to give the company a competitive advantage.

Advantages of Data Pipelines

A data pipeline doesn’t just organize and move your raw data to your target destination — it also provides a number of other advantages to your organization. 

1. Reduces Manual Effort
   Without a well-designed custom data pipeline, your company will need to devote valuable manpower to tedious manual tasks related to processing data. Instead, these pipelines offer a way to automate those manual tasks so your team can shift focus to more important work. 

2. Easy to Scale
   In the past, companies struggled to scale their pipelines as their businesses grew or they wanted to add additional data sources. This is no longer an issue today. These pipelines are agile and easy to scale, meaning your business won’t be held back by a pipeline that can’t keep up. 

3. Greater Data Reliability
   It’s not enough to have integrated data from various sources ready for analysis. If you want to get the most out of that data, you need to know that it’s fresh and reliable. Data pipelines can help ensure the quality of the data. Most of them include data transformation steps that take out duplications and verify that data before it arrives at the final destination for analysis. 

Though it will take an initial investment of time and effort to get your pipeline up and running, the benefits of using one of these pipelines will more than make that investment worthwhile for your business. 

How to Integrate Data Pipelines in Real Time 

The difference between a useful data pipeline and one that has the power to truly take your business to the next level is real-time data integration. 

Your average pipeline processes data using batch processing — periodically capturing and transferring the data through the pipeline. It’s convenient for moving large amounts of data but doesn’t allow you to glean real-time insights. 

The alternative is data stream processing, a method that processes data immediately upon creation. Through this method, data continuously flows from various sources into the pipeline. You can use real-time pipelines to make decisions based on the most current, accurate information. 

So how do you integrate custom data pipelines in real-time?

The first step is to identify all the data sources that you want to include in your data. These sources could be social media, website data, data from your CRM system, or any other data sources that could provide value to your organization. 

Once you have your data sources, you need pipeline architecture that can handle a considerable flow of data in real-time. You can build this architecture in-house or turn to external solutions for a more convenient option. When the solution is in place, you will still have to monitor it to ensure smooth integration with your existing systems. 

Use Cases for Data Integration

Companies in every industry can benefit from data integration, particularly real-time data integration. Some example use cases for this technology include:

Fraud detection

Without a real-time integrated pipeline, you won’t be able to detect fraud as it happens. Real-time data integration changes that. For example, one of these pipelines can take transaction data from the source such as location data, purchase history, and other account data and analyze it in real-time to identify fraud immediately. 

Business Analytics

In many industries, information critical to your business decision-making can change in an instant. If you don’t have access to the most up-to-date, integrated data, you may struggle to make the best decision for your company. That’s why business analytics is such a common application of real-time data integration. With a custom data pipeline, you can supercharge your business analytics to gain better insights and inform your business strategies. 

Inventory Management

Business leaders that work with physical inventory know how difficult and essential proper inventory management is. Luckily, integrated data can simplify this vital business process. Not only will real-time data integration give you the most accurate, current information about your inventory levels, but it will also help you forecast future demand. 

These are just some examples of how organizations use data integration. Depending on your industry, you may find many additional uses for this technology.  

In Review

A custom data pipeline can transform your business by making it possible to see real-time insights from data. Gain an edge over your competitors — regardless of your industry — by integrating a custom data pipeline in real-time. Contact Live Earth today to learn more.