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Data Pipelines- Top 5 Features

Data Pipelines- Top 5 Features

23 Mar 2023

Data pipelines refer to all the actions that occur while transferring data from a source to a destination. A data pipeline mainly constitutes three different components. These are a data source, a data processing unit, and a target destination. The main reason behind data pipelining is to extract useful information at the destination without accessing the sensitive production units. In this guide, we will list the top 5 features that a data pipeline must possess to operate effectively.

Realtime Processing and Analysis in Data Pipelines

Real-time data processing and analysis are very crucial for data pipelines. Today, all businesses need to respond quickly to each and every event. For that, they need quick access to data either processed or unprocessed. A data pipeline with real-time data processing and analysis capabilities can help you achieve this goal. In this way, you can instantly detect security issues, fraudulent transactions, equipment failures, etc. on the go.


Data pipelines should be able to scale up and down automatically as needed. At times, you will have bulks of data to deal with. On the other hand, sometimes you will only have to process a few chunks of data. Your data pipeline should be smart enough to handle both kinds of workloads. It should ideally do this in a manner that your resources should neither be wasted nor exhausted beyond limits.

Fault Tolerance

Data pipelines should definitely possess fault-tolerance capabilities. A data pipeline might fail while your data is in transit. In this scenario, a distributed pipeline architecture can save your precious data. In this kind of architecture, the data is distributed across multiple nodes. If one node fails, the next one immediately takes its place without wasting any time. Moreover, this process should ideally take place with no to very little human intervention.

Data Pipelines Should Follow the E1P Rule

E1P stands for “Exactly Once Processing”. Data pipelines should strictly comply with this rule. It is so because, at times, data might be lost or duplicated within a pipeline. To deal with this issue, a data pipeline should make use of checkpoints. This ensures that no data is lost on the way to the destination and everything is processed exactly for once. 

Process Large Volumes of Data in Multiple Formats

Many times we come across semi-structured and even unstructured data. In other words, we do not always get structured data in pipelines. Moreover, the format of data can also vary according to the user’s requirements. Therefore, data pipelines should be efficient enough to process large volumes of data at once and that too in varying formats. 


Data pipelines can prove to be very beneficial for businesses if employed properly. By going through this blog, you will get a fair idea of what features your data pipeline should ideally possess. Moreover, if you need any consultancy in this regard, then you can always contact Folium AI.

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