When it comes to technology tools that help businesses succeed, a pipeline for data is often not considered to be at the top of the list. This is because, while most companies now realize how valuable data is, they often need to realize how important data engineering is.
A modern data pipeline is designed to help businesses quickly and efficiently extract and transform their data. They allow them to create a usable form of information and then load it into their systems so they can make informed decisions. Having a promising pipeline can help you improve innovation and lower costs. Unfortunately, doing it wrong can lead to a lot of money loss and the loss of vital information.
What Are Pipelines?
A data pipeline is a set of steps that move an organization’s raw data from one source to another. For instance, the source might be a transactional database in business intelligence. On the other hand, the destination might be a data warehouse or a data lake. Transformation logic is then used in the pipeline to prepare the data for analysis.
Why Are Pipelines Needed?
The rise of the cloud has led to a wide range of apps that allow businesses to manage various functions. For instance, the marketing team might combine Marketo and Hubspot to automate their marketing efforts, while the sales team might use Salesforce to manage their leads. This fragmentation of data can lead to the loss of vital information.
Getting the most out of your data can be challenging due to the fragmentation of data silos. Even if you have a promising pipeline, it can take time to find simple business insights. One of the most common reasons businesses fail is their IT infrastructure’s complexity. A challenging factor when implementing a pipeline is the amount of data you have to collect.
Pipelines can help organizations consolidate all of their data sources into a single destination, speeding up the time it takes to analyze and find business insights.
Benefits of Thorough Pipelines for Data
A pipeline comprises various steps that move data from one place to another. For instance, a transactional database might be the source of business intelligence. The destination is where data is analyzed to find business insights. However, there are many advantages to having a pipeline, such as its ability to transform data.
- Replicable Patterns
A good pipeline can help businesses improve innovation and lower costs. Understanding the various steps involved in data processing can create a framework that can be reused and repurposed.
- New Data Sources Integrated Faster
Having the necessary tools and knowledge to guide data flow through an analytics platform can help businesses reduce their time and expense in integrating new data sources.
- Data Quality Confidence
Monitoring and securing your pipelines can improve the quality of your data and prevent issues from happening.
- Data Security Confidence
The security of a pipeline can be built in with a combination of repeatable patterns and a good understanding of the various tools and architectures used in data processing.
- Ability to Build Incrementally
Pipelines can be built incrementally, which is why it is important to think about your dataflows as such. By starting with a manageable amount of data, you can quickly start seeing value.
- Agility and Flexibility
A pipeline can be designed to respond to changes in the data sources or the needs of its users. For example, a pipeline can handle changes in the data collection type. This can be caused by API changes or unexpected characters in the data. Having the necessary tools and methods to prevent this can help keep the pipeline running smoothly.
Final Thoughts
Organizations need a more straightforward management method as they collect and analyze more data. This is where the pipeline comes into play, and it is especially true in a world where data is a vital part of every organization’s operations. So finding the best ways to process and store this data is critical to ensure it is appropriately used.
