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5 Best Practices For Automated Data Pipeline Management

For years, companies have struggled to find a fit-for-purpose solution to their data pipeline management. But more often than not, they fail to find one solution that encompasses all of their needs, prompting them to search for software or applications they can customize. However, this could come at a hefty cost.

Fortunately, technology is progressing at an alarming rate. According to Techopedia, there are many applications that already make use of real-time data processing, although batch data processing hasn’t left the scene completely.

In the future, companies hope to process all the data it collects in real-time to streamline their operations and data management. Before fast-tracking so far ahead, software solutions are available to assist companies in managing parts of their data pipelines that currently don’t support real-time functionality.

Automation has become commonplace in businesses, especially for repetitive or routine actions like collecting and sorting data. Therefore, companies are moving toward software solutions that lend themselves to including the best practices for data pipeline management as explained below:

1. Use automated options where possible

Finding the best solution for the company to deal with the masses of data they collect from various sources daily could include automated solutions that simplify the process and speed it up simultaneously. Rather than hiring more staff to handle the workload, one system can take over the responsibility and improve productivity.

Employing data modeling software, like the packages available on this website, may improve the process from start to finish, as it helps align all the data pipeline parts. Its automation and no or low code methods leave data engineers free to focus on more essential tasks like monitoring the input, flow, and output of the information.

2. Construct the pipeline in phases

Instead of rushing into the process of constructing a data pipeline, companies can build it in phases. Not only will it allow the company to place a greater focus on each stage, but it could also ease the financial burden of upgrading. Furthermore, this will relieve pressure on the engineers while providing a platform to start managing more data.

Businesses can prioritize their various data sources and targets so that the most critical information will receive attention first. At the same time, the remainder of the pipeline will gradually add more to the process. Some companies still use legacy systems to collect data, so they may want to start with these to modernize their operations.

3. Consider cloud infrastructure

Another valuable addition to a data pipeline is cloud infrastructure. Moving to a serverless environment streamlines the collection and storage of data in both batches and real-time. In addition to making operations more efficient, cloud infrastructure is more convenient for data engineers who can access the data pipeline anywhere at any time.

Managing a data pipeline may need immediate adjustments, like applying rules to the data for sorting or cleansing, which the data engineers can do in a cloud setup. Plus, they can fix any data issues wherever they occur in the pipeline before it becomes troublesome to the rest of the operations.

4. Unify user experiences

Although some may consider standardization monotonous, it’s essential for unifying users’ experiences across the board. When data enters the pipeline in a specific way and goes through the same method of sorting and processing, it should consistently deliver accurate, high-quality results in the form of data sets. According to TechTarget, varying formats and representations may cause significant hurdles for a company to overcome, which means unification is essential.

Consequently, data engineers can spot any issues in the pipeline without much effort. The pipeline can include drifts in the schema either at the source or elsewhere. Aside from adding drifts to the schema, it can make the necessary adjustments in real-time. This means all users will immediately have access to the update without significant delays.

5. Implement regular quality checks

Having data pipelines flow automatically may be more convenient. However, the chances of problems occurring along the way may also be likely, especially when a company is in the beginning phases of streamlining its data pipeline. According to Gartner, companies can lose an average of USD$12.9 million each year due to poor data quality, which proves the importance of these checks.

A sufficient reporting procedure will also be beneficial to ensure that the company swiftly addresses all quality-related problems. Moreover, implementing data modeling software may include features like email notifications to alert data engineers of anything that may require their attention, so they don’t have to rely solely on their regular checks for improving the data pipeline quality.


Data can be defined as the lifeblood of any business, and without it, many would struggle to stay afloat. Companies can find the most efficient and reliable software solutions to assist them with masses of information. Until systems catch up to the future, where everything can happen in real-time, businesses may need to enlist the help of data modeling software and other techniques that will streamline the process for the best possible outcomes.

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