What Is a Data Pipeline and How Does It Work?

Data integration technology and practices have come a long way in recent decades. Organizations have moved away from the rudimentary batch-mode integration that transmits entire data sets back and forth between siloed systems. Today there can be a real-time streaming data pipeline that supports up-to-the-minute data analytics. Information flows seamlessly from one system to another without delay.

The value proposition for real-time integration has grown considerably stronger in the process. In the old days, the focus was almost exclusively on transactional and operational requirements. Today, real-time analytics have emerged as a powerful engine of innovation and growth. Streaming data pipelines are supporting agility, as business leaders strive to respond quickly and effectively to ever-changing business conditions. To do that, they need real-time access to their data.

In this context, data integration is no longer simply an operational task executed by the IT department; it’s a top-down strategic imperative. It’s not just about sharing information effectively anymore; it’s about holistically understanding the enterprise, its customers, its suppliers, and the wider world in which it operates. Real-time streaming data pipelines are essential to business agility.

The Real-Time Data Imperative

Today’s cloud-based big data platforms include powerful applications like Hadoop, Snowflake, Cloudera, Splunk, and Databricks. These tools support massive volumes of information and naturally lend themselves to both structured and unstructured data, consolidation and normalization of information from multiple sources, and real-time analytics to drive both operational and strategic business decisions.

For big data to deliver on its promise of business value, it needs fast, reliable, scalable integration. It’s no longer enough to operate old-style ETL processes that run overnight to populate a data warehouse with up-to-date information. Today, many important analytics functions require real-time feeds. Clickstream analytics, dynamic pricing, supply chain planning, and fraud detection call for rapid response. Real-time streaming data pipelines address that need in a way that old-style ETL batch operations simply never could.

Today, businesses are working with a multitude of different systems and applications. They are sharing business data and platforms with their trading partners. Many are consuming software as a service. Often, they are integrating data from mobile devices, clickstream analysis, or real-time data feeds from other sources. Smart companies are enriching their data with information from external sources, adding value and context to the data they already have.

When a company can effectively bring all that information together, it can dramatically increase the value of the business insights that result. Today’s integration challenges arise from this fundamental need for a holistic view of the business.

What Is a Streaming Data Pipeline?

Business intelligence is most impactful when the underlying data is fully up-to-date. Real-time data has considerably more value than information that is a day old or older. This is especially true with certain business processes such as fraud detection in the credit card industry, or intrusion detection in IT services, where rapid detection of anomalies can prevent potential concerns from developing into real-world problems.

With the increased volume of data, there is also increased velocity. To complicate matters even further, IT environments are in a constant state of flux. The point-to-point connections of yesterday are no longer up to the job. Real-time streaming data pipelines are fast, flexible, scalable, and reliable.

Streaming data pipelines offer a highly coordinated, manageable system for capturing data changes across a myriad of different systems, transforming and harmonizing that information, and delivering it to one or more target systems at scale. This provides business leaders with real-time insights that drive informed decision-making and competitive advantage. It also breaks down information silos and enables next-generation innovation that helps businesses to leapfrog the competition, leveraging artificial intelligence and machine learning, for example. Real-time streaming data helps leaders to understand their customers better, identify patterns in buying behavior, and create memorable customer experiences.

Key Considerations for a Streaming Data Pipeline

Here are some important considerations that IT leaders should keep in mind as they design a strategy for integration with streaming pipelines:

Think holistically. Most organizations fail to ever develop a complete picture of the enterprise. Almost inevitably, they omit certain systems or processes from their data pipelines. Mainframe systems are commonly left out of the picture, due in large part to the complexities of integrating mainframe data sources with modern relational databases and web services APIs. Modern data platforms lack native connectivity and processing capabilities for mainframe data, making it challenging to integrate much of the data stored in an organization’s most critical business systems.

Most integration tools are not enabled to easily handle mainframe data formats, including variable-length records, COBOL copybooks, and other idiosyncrasies of mainframe systems. Mainframe data is simply not compatible with most data analysis tools without first being prepared for use in a modern analytics environment. The mere process of capturing changes on the mainframe and feeding them to the data pipeline is beyond the scope of most data integration tools.

Data quality matters. If business leaders are to rely on advanced analytics for strategic insights, they need to be confident that the underlying data is accurate and complete. As businesses increasingly turn to AI and machine learning technologies, the risk of getting it wrong looms much larger than ever before. Data quality isn’t a “one and done” proposition. It requires tools and methodologies that can scale as the volume of data in your organization grows. And inevitably, it will grow.

Prioritize data governance. As business leaders seek to bring order to the data stored in different systems across the enterprise, data governance is becoming increasingly important. A sound strategy for a streaming data pipeline should fit within an overall governance framework that incorporates data quality, enrichment, location intelligence, and more.

Scalability matters. Finally, it’s important to use enterprise-grade tools capable of handling thousands or tens of thousands of records per second so that both your data pipeline and your business can scale as the volume of available data increases, which it inevitably will in the coming years.

With Connect CDC from Precisely, businesses have the power to build streaming pipelines, create critical links between legacy and target systems, share application data across the enterprise, and integrate easily with modern data platforms such as Snowflake and Hadoop. Learn more about how Connect CDC can help your organization build a holistic approach to data integration; download our e-book Streaming Legacy Data for Real-Time Insights.