What Is a Data Pipeline? Considerations & Examples

Although these steps sound simple, data pipeline architectures exhibit complexity due to the massive volume and complexity of the raw data.

In recent times, data pipelines have evolved to support Big Data and its three primary traits – velocity, volume, and variety. Hence, data pipelines must support:

Next, let us examine the various parts of a data pipeline.

Components of a Data Pipeline

Although data pipelines can differ based on usage, listed below are its common parts or components:

1. Export or Import

This component covers the data input process (from its source). Entry points typically include data (or IoT) sensors, processing applications, social media inputs, and APIs. Furthermore, data pipelines can extract information from data pools and storage systems.

2. Data Transformation

This component includes all the data changes (or transformations) as it moves across systems. Data changes in accordance with the data format supported by the target application (for example, data analytics).

3. Data Processing

This component involves the tasks and functions required to import, extract, convert, and upload the data to its target output. Data processing also includes tasks like data merging and sorting.

4. Data Synchronization

This final component is all about synchronizing data between all supported data sources and the endpoints of the data pipeline. This component involves maintaining data consistency (through the pipeline lifecycle) by updating the data libraries.

Next, let us discuss the benefits (or advantages) of implementing a data pipeline.

A well-designed and efficient data pipeline is reusable for new data flow, thus improving the efficiency of the data infrastructure. They can also improve the visibility of the data application, including the data flow and deployed techniques. Data professionals can configure the telemetry of the data across the pipeline, thus enabling continuous monitoring.

Moreover, they can improve the shared understanding of all the data processing operations. This enables data teams to explore and organize new data sources while decreasing the cost and time of adding new data streams. With improved visibility, organizations can extract accurate data, thus enhancing the data quality.

What are the crucial design considerations when implementing data pipelines? Let’s discuss that next.

To implement a successful data pipeline, development teams must address multiple considerations in the form of the following questions:

Here are some preliminary considerations to factor during design:

Depending on the business need, data pipeline architectures can vary in design. For example, a batch-driven pipeline is deployed for point-of-sale (POS) systems as they generate large data points. Further, a streaming pipeline processes real-time data from POS systems and feeds the output to data stores and CRM applications.

Another example is the Lambda architecture, which combines both batch and streaming pipelines within a single architecture. This architecture is widely deployed for processing Big Data.

Here are some of the real-life case studies:

With the growing volume and complexity of data, organizations must modernize their data pipelines to continue extracting valuable insights. As outlined in this article, data pipelines offer multiple business benefits to data-dependent organizations.

ACCELQ offers a codeless approach to data-based automation for the benefit of its global customers. Our technology solutions range from enterprise automation to outcome-based services.

Looking to improve the effectiveness of your data pipeline? We can help you out. Sign up today for a product demo!