Key features of a modern data pipeline

You can only make data-driven decisions to grow your business if your data has value. Learn how a data pipeline allows you to turn raw data into useful data, and what to look out for in a modern data pipeline.

Brenda Prowse

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Brenda Prowse

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November 25, 2022
Key features of a modern data pipeline

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Organizations need to make data-driven decisions to grow their business, but they can only do that if the data provides value. Raw data in itself is not suitable for analysis, and it needs to go through a process to make it organized and clean. 

This process is called a data pipeline, and it gathers raw data from sources and then runs it through stages to turn it into useful and actionable insights. 

What is a data pipeline?

A data pipeline has multiple stages which turn raw data into useful data. The output of one stage is the input of the next stage, and all the stages run in coordination. The result of the pipeline affects how users consume the data, so it's essential to create an impactful process. A data pipeline architecture can be expanded into six different stages. 

  1. Content sources: The first stage is identifying where your structured and unstructured data comes from. This could be anything from relational databases, app analytics, user behavior, email, social media, file shares, or third-party data. Modern organizations operate a large number of data sources, raising a variety of challenges.
  2. Data ingestion: Once your data sources are identified, data ingestion can collect the disparate sources and start putting them through the data pipeline. 
  3. Data processing: During the processing step, raw data is converted into cleansed and transformed data. 
  4. Data enrichment: In this stage a variety of data mining or AI technologies are applied to the cleansed data to surface latent information contained in it. Data enrichment improves the usefulness of your data by enhancing it with extra context merged from another source. Check out our detailed post on data enrichment to learn more.
  5. Data storage: This step is where data is stored. Data may be sliced differently and stored in multiple places, such as data warehouses, data lakes or search indices.
  6. Data consumption: Finally, employees and stakeholders of an organization can use the data and glean actionable insights, leveraging tools like analytics and reporting dashboards to make it easier to understand the data.
  7. Data governance: Data governance monitors the entire data pipeline and ensures security and data policies are followed and enforced. Learn more about data governance principles and best practices.

There are some critical factors to consider when using a data pipeline. For example, the type of data pipeline you use can affect speed, processing type, automation, and costs. There are four types of data pipelines to consider, but they are split across two dimensions: transfer speed, and level of in-house expertise required.

Batch vs real-time processing data pipelines

  • Batch processing: Batch processing is not transferred in real-time. Instead, the data pipeline will create a "batch" of data and load it into storage at a set interval of time. This helps prevent the overall system from getting overwhelmed. Batch processing may be ideal if you need to move large amounts of data frequently without an immediate need for real-time analytics. Historical data also goes through batch processing.
  • Real-time: Sometimes also referred to as streaming data pipelines, this type of data pipeline is preferred for users wanting to analyze a complete dataset with real-time data. This means employees don't have to wait for data to get extracted, transformed, and loaded. Real-time data pipelines are preferred if your employees need immediate access to data.

Cloud-based vs open-source data pipelines

  • Cloud-based: Cloud-based data pipelines usually have lower costs and maintenance requirements. All of the data is available, and the processing is usually automated and stayed within the cloud. Azure Service Bus and Azure Event Hubs are examples, since they can automate data processing in the Azure cloud.
  • Open-source: Possibly the most affordable option, open-source is an alternative and traditional data pipeline. However, you will need a development team with expertise in this area to successfully build a data pipeline. 

No matter the data pipeline type, they all share similar benefits including:

  • Turning raw data into data that can be used for business intelligence (BI), data analysis, and other business processes.
  • Boosting confidence in data quality and security.
  • Providing data enrichment by combining internal and external data to create a complete and accurate dataset.
  • Creating a data governance framework that makes it auditable.

What is the ETL and data pipeline?

The ETL pipeline and data pipeline are terms often used interchangeably, but they are different. An ETL is a sub-type of a data pipeline, while a data pipeline is a generic term to describe the collection, transformation, and storage of data.

Depending on the data pipeline architecture, the Extract, Transform, and Load steps can change order. For example, the ETL pipeline transforms data before storing it in data warehouses. ETL is frequently used for batch processing, but real-time can also be used.

Another alternative option is you could use the Extract, Load, Transform (ELT) pipeline where the transformation happens after raw data has been stored in data lakes.

What are data pipelines used for?

There are plenty of ways to use a data pipeline to manage an organization's growing data corpus. Some of these use cases include:

  • Investigate data sets for analytical processing.
  • Create data visualizations for visual understanding.
  • Consolidate multiple data sources and turn them into a single source of truth.
  • Federated search across all data sources.
  • Improve operations by moving data into storage.

Key features of a data pipeline

A modern data management pipeline has key features to ensure data chaos is transformed into processed data. Some of these key features include:


Scalability is crucial for a data pipeline. The ability to quickly scale in the event of an increased and unexpected data volume is necessary to keep the data pipeline orchestrated properly. Cloud-based data pipelines can handle scalability the best since they have access to several servers to rely on for heavy data loads.


Modern data pipelines need to have a distributed architecture to prevent failure. This ensures data pipelines remain reliable even if an immediate failover occurs. The fault-tolerant architecture will use a different node in the same cluster if one fails.  

High Throughput

Organizations gather structured and unstructured data, and need a data pipeline to effectively manage it all. No matter how much data goes through the pipeline needs the capability to keep the data moving from one stage to the next without failure. Choose a data pipeline that can handle processing large volumes of data without lagging.

Final thoughts

There are multiple data pipeline architectures, but the best one will depend on your business needs. Deciding on the most efficient data pipeline can make a huge impact on your organization's success. 

But creating a well-architected and high-performing data pipeline is a challenging effort for developers. They face hurdles like structuring data, incorporating scalability, validating data quality, monitoring for errors, and more tasks. 

Organizations may find it's better to use an automated data pipeline instead of building one from scratch. This way developers are free to work on other important projects.

At RecordPoint, we offer an intelligent pipeline for records management. Using our Connectors, you can bring consistency to your data management, allowing you to connect to structured and unstructured data to create a true data inventory. Then our data pipeline detects signals like data size, type, location, metadata, and data sensitivity (using data privacy signals like the presence of PII and PCI, as well as customer consent) before it inventories and classifies the data, establishing a retention period for the data in line with legislation such as GDPR and industry best practices.

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