What Are Data Pipelines and How Do They Strengthen IT Infrastructure?

How Can Data Pipelines Help Your Business?

A data pipeline is the set of continuous processes through which raw data is ingested from various data sources, transformed into desired formats and loaded into a destination database or data warehouse.

In other words, a data pipeline is the method through which data goes from being raw at point A to being ready for analysis at point B, whether that means moving data between on-premises systems and cloud-based systems or solely between different cloud-based systems. It’s important to note that modern data pipelines are different from traditional ETL (extract, transform, load) pipelines.

Data pipelines fall into two main categories: batch processing and streaming data. ETL pipelines fall into this first category, meaning this pipeline process happens at regular intervals, such as once a day or once a week. As its name implies, this process also has a specific order: Data is first extracted from the source system, then transformed into the desired format before finally being loaded into the destination system.

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In a modern data pipeline, data is extracted in real time or nearly real time, then transformed and loaded into the destination system; instead of happening at intervals, this process happens continuously in a modern data pipeline, which is why they are typically used for streaming data or data that is always changing, such as social media data or stock prices.

With this in mind, the architecture of a data pipeline may vary depending on pipeline type and an organization’s specific requirements, data sources, processing needs and desired outcomes. However, despite these variables, the core components of a data pipeline remain the same.

What Are the Core Components of a Data Pipeline?

Regardless of type, data pipelines generally contain five core components:

1. Data Ingestion: This is the process of acquiring and collecting data from miscellaneous sources. Whether the data is extracted from databases or application programming interfaces, whether it’s done through batch processing or real-time streaming, this data ingestion process is a must.
2. Data Storage: Once ingested, data must be stored in a suitable data repository for processing and analysis. Storage type may depend on factors like data volume, query requirements, scalability and cost, and may include data warehouses, data lakes, distributed file systems or databases.
3. Data Transformation: This component includes tasks like cleaning, enriching and modifying the data to make it suitable for downstream processing. In short, data transformation gets data prepared for analysis, ensuring it’s consistent, structured and ready for further analysis.
4. Data Processing: This is the stage where meaningful information is derived from newly transformed data. Through computations, data exploration and machine learning algorithms, data processing helps decipher insights and patterns.
5. Data Delivery: At the final stage, data that is processed and analyzed is delivered to the intended personnel or systems such as interactive dashboards, reports or APIs. Data delivery ensures that the data pipeline reaches stakeholders in a consumable and actionable format.

Each of these five stages play a crucial role in the management and processing of original raw data. Together they create a seamless flow of data throughout the entirety of the data pipeline, allowing organizations to make informed, data-driven decisions.

Why Data Pipelines Strengthen IT Infrastructure

Data pipelines strengthen IT infrastructure in a variety of ways. First, they integrate data from disparate sources and provide a central collection point. By promoting data accessibility and collaboration across departments and systems, data pipelines significantly strengthen IT infrastructure.

Second, data pipelines also clean and transform data from its raw, unstructured state to a high-quality, digestible format, enhancing the overall quality of the IT infrastructure. This eliminates the guesswork so that IT leaders can make more reliable business insights. Third, data pipelines also automate the process of data collection, transformation, analysis and delivery, which affords IT personnel the time needed to focus on more strategic tasks, such as data governance and security.

How Do You Implement a Data Pipeline into Your Business?

Setting up a data pipeline isn’t as easy as hitting a button. It requires a complex orchestration of factors. The most successful data pipelines are specifically designed for an organization’s needs. An initial conversation with a CDW consultant can help you assess where to start. 

And designing a data pipeline is a multistep process best done with the help of a developer or other expert to ensure all components are built out correctly and will interact seamlessly with each other and all of an organization’s correlating systems to pull all the data into one place.

Companies like IBM, NVIDIA, Microsoft and Hitachi have products and experts in place to design and implement data pipelines into all types of businesses, resulting in eight times quicker data access, millions of dollars in cut costs. Kathy Rudy, chief data and analytics officer at ISG, says that data pipelines are essential for organizations, “as they create and build microservices that are dependent on the same data sources across products and platforms.”

Best Practices for Maintaining and Optimizing a Data Pipeline

When operating correctly, data pipelines can run on their own. But that doesn’t mean they’re entirely self-sufficient. As with anything, data pipelines require continual upkeep.

On average, data engineers spend 80 percent of their time managing data pipelines, according to a market report, but the productivity result is significant.

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Best practices for maintaining and optimizing a data pipeline include:

• Regular monitoring and proactive maintenance: Data pipelines don’t need to be operated manually, but they still should be monitored. Implementing a robust monitoring system can help track the continued health, performance and data quality of a pipeline by monitoring key metrics such as data ingestion rates and processing times.

  Setting up alerts to detect abnormalities can help you tackle errors as soon as they occur. Regularly reviewing monitoring data may also proactively identify any issues.

• Automated testing and validation: Testing validates the smooth functioning of a pipeline. Developing comprehensive automated test suites that cover different stages of the pipeline can help verify the accuracy of data transformations; validate data against predefined rules or thresholds; and identify any bugs, errors or gaps in the pipeline.
• Performance optimization and scalability planning: By regularly reviewing performance metrics and identifying areas for improvement, you can optimize data processing steps, such as implementing parallel processing techniques or optimizing data storage and retrieval mechanisms. These regular reviews also can help you plan for scalability as your data volume grows so that you’re ready to handle increased data processing demands as they occur in real time.

Valued at $6.8 billion in 2021, the global data pipeline tools market is projected to reach $35.6 billion by 2031. In other words, data pipelines are growing exponentially. Following these best practices, your organization can gain a competitive advantage when it comes to data analytics.