Key challenges with real-time data processing for your data pipeline projects

One of the hardest parts of real-time machine learning is building real-time data pipelines, they need to be able to handle millions of events at scale in real-time, and be able to collect, analyse, and store large amounts of data. This means that the capacity for applications, analytics, and reporting all has to be robust, and capable of handling the data streams and the size of the data, in order to function.

Depending on the type of processing you are using for your data pipelines, there will be different challenges that must be overcome, in order to have them functioning at optimum levels. In this article, we are going to look at some of the specific challenges that real-time data processing faces, and why you need to address these issues in order to succeed.

Online interference

Changes to data and predictions made in real-time mean that machine learning models must be extremely fast in order to feature the data, a typical Service Level Agreement (SLA) for interference, for example, is around 100 milliseconds.

The infrastructure of the data pipeline has to be capable of operating and adjusting at these speeds, otherwise maintaining the integrity of the infrastructure is going to become more difficult and apply a greater burden to your engineering team.

Fresh data and new features

Most real-time models will benefit from fresh data, but they need to know where to look for it, and where it will come from, in order to correctly identify and process it.

As your pipeline grows, and new features become necessary, you will find it more challenging to adapt as your stack increases and the number of moving parts grows. You need to have a strategic process in place for growth and to check for fresh data, otherwise, the pipeline will stagnate, and the infrastructure will not be able to content with the changes.

Read the starting guide on building data pipelines.

Maintaining team learning and keeping up with training

As you grow and evolve, your machine learning is going to deviate from its original form and become customised to your needs over time. This means that training and serving skew is inevitably going to happen – how you operate, diagnose, and solve debugging issues, for example, will depend on what you have implemented, and how you have developed the pipelines.

Because of the real-time nature of the data flow, you need to have workarounds and solutions ready to be implemented for a variety of reasons, and it is essential that these are carefully monitored, and the processes noted down – because they will evolve and change from the basics, and your team need to know how to operate these programs and platforms, regardless of the changes.

Real-time data processing for your data pipelines

As real-time data access continues to grow, and there is a shift to hybrid and multi-cloud environments, the challenges of working with data pipeline projects are going to evolve as well. Working with experts who understand the data environments and have tried, and proven solutions make a lot of financial and operational sense.

Ardent data pipeline development

Ardent have worked on a number of data pipeline projects dealing with multiple types of data processing including batch processing and real-time processing. If you are looking to build robust, secure and scalable data pipelines, our team of highly experienced and skilled data engineers can help. Get in touch to find out more or explore our data pipeline development services.

With real-time data processing, if you are dealing with large volumes of data that needs to be available in real-time then you may consider operational monitoring and support services. This can help you avoid data dropouts and delays. Our Ardent engineers carry provide this support to one of our long-term client to ensure data availability and accessibility.

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