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5 Ways To Avoid Data Pipeline Issues

Data pipelines are essential for businesses that want to use data to inform their decisions, but these systems can be complex and quickly fail. Pipelines are complex systems that move data from one place to another. A lot can go wrong in a pipeline, and when it does, it can be challenging to fix the issue. Here are five ways to avoid data pipeline issues:

Automate As Much As Possible 

One way to avoid data pipeline issues is to automate as much as possible. Automating the process of creating and managing data pipelines can help businesses prevent many potential problems. Databand, an IBM Company, provides a data observability platform that helps businesses automate the creation of data pipelines, making managing them more accessible and less error-prone.

One of the best ways to avoid data pipeline issues is to automate as much of the process as possible. Automation can help reduce the chances of human error and make it easier to track down problems when they occur.

Use A Staging Database

A staging database is a temporary database that stores data before loading it into the destination database. Staging databases help to ensure that data is clean and consistent before it enters the destination database. They also provide a place to test changes to the data pipeline before they are implemented in production.

Using a staging database might help you avoid many potential data pipeline problems. It may be used to test pipeline modifications, ensure that data is clean and consistent, and provide a location to store data before it’s loaded into the target database.

Perform Data Quality Checks Early And Often

Data quality checks should be performed at every stage of the data pipeline, from extraction to loading. These checks help to ensure that data is clean and accurate before it enters the next phase of the process. By catching errors early, you can avoid issues downstream in the process.

Performing data quality checks early and often is an excellent way to avoid data pipeline problems. These checks help ensure that data is clean and accurate and can catch errors before they cause issues downstream.

Extract Data Incrementally Whenever Possible

Extracting data incrementally—extracting only new or changed data—helps keep the size of extracted files manageable and makes identifying issues with specific records easier. It also minimizes the impact of extractions on source systems. 

Batch processing is the method of grouping data together into a single unit for processing. Batch processing can improve the performance of data pipelines by reducing the number of network round trips and allowing the use of parallel processing. It can also help to ensure that data is processed in the correct order.

Design For Resiliency And Scalability

When designing data pipelines, it is vital to consider resiliency and scalability. Resiliency is the ability of a system to recover from failures, while scalability is the ability of a system to handle an increased load. Data pipelines should be designed to handle both anticipated and unexpected losses.

Resiliency

Data pipelines should be resilient, meaning they should be able to handle both anticipated and unexpected failures. By designing for resiliency, you can avoid or minimize the impact of data pipeline failures on your business.

Using fault-tolerant systems is one way to make a data pipeline more resilient. Fault-tolerant systems are designed to continue operating even when parts of the system fail. They are often used in combination with error handling procedures to help ensure that data is not lost during a failure.

Another way to make data pipelines more resilient is to use redundancy. Redundancy is the duplication of elements in a system, such as data stores or processing nodes. This can help ensure that data is not lost if a system component fails.

Scalability

Another important consideration when designing data pipelines is scalability. Scalability is the ability of a system to handle an increased load without breaking down. Data pipelines should be prepared to be scalable, so they can take up increased demand as your business grows.

One way to make a data pipeline more scalable is to use parallel processing. Parallel processing allows tasks to be divided among multiple processors so they can be completed more quickly. 

Another way to make a data pipeline more scalable is to use distributed systems. Distributed systems consist of multiple nodes that share the workload of processing data. This can help a data pipeline scale up as needed without requiring additional hardware.

Conclusion

Data pipelines are an essential part of any business that relies on data. They can help ensure that data is processed quickly and accurately and provide insights that would not be possible without them. However, data pipelines can also be complex and fragile. Organizations like Databand, an IBM Company, can help you monitor, optimize and troubleshoot your data pipeline. Following the tips in this article, you can avoid common problems with data pipelines and keep your data flowing smoothly.

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