How to approach stream processing for your data pipelines

A data pipeline is a set of processes and associated tools that make the movement of data between a source and its target, automated. There are three key elements involved – a source, processing steps, and the destination. The processing steps you choose will depend on your needs, your software, and how your pipeline has been developed.

Stream processing is a data management technique that involves continuous movement of data, which is quickly analysed, filtered, and transformed or enhanced in ‘real-time’ before the data is passed on to another application, data storage, or steam processing engine.

Essentially this means that the data is being utilised or having action taken as it’s created, rather than scheduling or batching it for later.

Why is stream processing used?

The way stream processing functions (in real-time), applications can respond to new data events the moment they happen, allowing the process to continually monitor the data pipeline and detect conditions in a very short space of time.

The method of processing, due to its constant movement, is not suitable for every data set and can be resource-heavy when it comes to operational requirements. However, there are methods and settings that can optimise the usage and reduce the monetary and technological burden; for example – a quality software-encoded stream may use 25% of a quad-core CPU but using a hardware-encoded stream would only require around 5% of the same CPU.

How does stream processing work with data pipelines?

Data processing is generally approached by collecting raw data, filtering, sorting, processing, analysing, and storing it, before presenting it in a readable format. With the real-time format of stream processing bringing in a constant flow of data, the pipelines are set up to allow continuous insights and data delivery across a business and are often used to populate data lakes or data warehouses, or as an option for publishing to a messaging system or data stream.

How does stream processing compare with batch processing?

Stream Processing sends the data across as it is received, whereas batch processing waits until all of a specific data set is gathered together before delivering it. Both options have their benefits, and their restrictions and the choice of one over the other will largely depend on what data you are processing, how quickly you need to know the results, and whether it is beneficial or not to have it supplied in a single batch, or as it is generated.

Stream processing is an especially popular solution for clients who require high data availability with no delays, and who need data pipelines consisting of various sources to run consistently without errors. Our experts have had a great deal of success in making data science efficient and ensuring that our clients are having their needs met without delays and are confident in the integrity of their data systems, and the monitoring that is supporting it.

Stream processing for your data pipelines with Ardent

Deciding which type of processing to use for your data pipelines requires careful thought, evaluation, and understanding of what you want to achieve. If you would like to reach out for expert advice, our data engineering team are on board to help. Working on numerous data pipeline development projects including building robust, scalable data pipelines with AWS infrastructure, we have the expertise to help you unlock the potential of your data. Get in touch to find out more or explore our data engineering services.

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