Valued at nearly $7 billion and rapidly growing, the global market for data pipeline tools has emerged for good reason. Data—the fuel that stokes the data-driven enterprise’s flame—is an increasingly valuable and hot commodity.
The only problem? Capturing its potential is often easier said than done, especially with disconnected systems and silos hindering the process. However, with a well-built and maintained data pipeline, organizations can maximize data efficiency and accelerate value for years to come.
What is a data pipeline?
A data pipeline functions as the delivery route for data to transfer from one system to another. This often includes disparate sources of data like applications and internet-of-things (IoT) devices to repositories—a data warehouse, data lake, object storage, and so on—or other applications and systems (like a data analytics platform).
Despite its name, the pipeline isn't literal infrastructure. Rather, it encompasses the steps and activities that must be completed for data to travel to its intended destination and the various technologies and tools that facilitate this process. They can be (and often are) in the cloud, but are also on premises for legacy systems.
Why are data pipelines important?
Despite its advantages, big data can also be a big problem for enterprises that don’t have a means of managing and activating it effectively. As a delivery mechanism, pipelines are essential to not only harnessing data’s potential, but making it usable in the first place.
Consider this: An estimated 50% of enterprises manage over 5 petabytes of data. At least 80% of that data is unstructured, while almost 90% of it lives in a hybrid-cloud, multi-cloud, or cloud-only infrastructure. This complex web of cloud applications, if not properly managed, can lead to multiple data silos between systems, departments, and end users.
This is where a data pipeline comes into play. It ensures dataflows are never interrupted, delivering information precisely where it must go in exactly the right format. In turn, organizations that leverage pipelines for better data processing can translate raw data into actionable business insights.
Types of data pipelines
Pipelines fall into two overarching categories:
- Batch processing. Under this category, data processing happens in large volumes, or “batches.” This can either be scheduled to occur automatically or triggered by a user query. Batch processing pipelines are usually the ideal choice in cases where there isn’t an immediate need to analyze a dataset, such as monthly or quarterly accounting purposes. Rather than in real time, this process can take anywhere from minutes to several hours.
- Streaming data. In contrast, a streaming data pipeline is used to funnel real-time data streams at the appropriate pace. A “data stream” is an incremental sequence of small data packets, usually representing a series of events (like financial transactions). This approach is best for use cases in which data must be continuously updated, such as inventory management or stock trading.
The key difference between batch processing and streaming data pipelines is that the former occurs during off-peak hours. This is done by design, as high data volumes require much more computing power. Stream processing pipelines, on the other hand, run continuously but demand reliable, low-latency networking technology.
ETL vs. ELT pipelines
ETL stands for “extract, transform, and load.” An ETL pipeline is a specific subcategory of data pipeline that extracts or copies raw data from multiple sources and stores it in a temporary staging area. Then, it modifies (or transforms) the data and loads it into its destination, such as a data lake or data warehouse.
The ETL sequence is normally used for batch processing or other more traditional types of data processing and analysis. However, despite this association, an ETL pipeline may also be used for stream processing.
On the other hand, an ELT pipeline—“extract, load, and transform”—follows a different sequence. This subcategory describes a system where data is extracted from a source database and piped directly into the destination as raw data for future data cleansing, processing, and analysis.
That said, transformation isn’t necessarily required; an ELT pipeline may omit the final step altogether. However, it’s rare a pipeline doesn’t modify the data, as this makes data analysis much more difficult.
How do modern data pipelines work?
A data pipeline consists of several core components that in combination move, process, and manage data efficiently. These include:
- Origin and destination
In simple terms, the term “origin” refers to the point at which data enters the pipeline. This is normally a particular data source, such as a company’s customer relationship management (CRM) system. However, storage mechanisms, like data lakes and warehouses, are also considered origins.
As the name implies, a “destination” is the final point to which data is transferred. This is typically decided by the data’s intended use case. For instance, a dataset’s destination may be a development environment if it’s to be used to train a machine learning (ML) model. Dataflow
- Dataflow
Dataflow refers to the path data takes to arrive at its destination. It also includes any changes (like transformations) it undergoes as well as any data stores it passes through on the journey. In other words, dataflow describes how a pipeline’s inputs eventually become outputs.
A typical dataflow would start when data enters the pipeline after extraction and ingestion. It's then transformed into a uniform format, processed, and loaded into its destination. However, in the case of an ELT pipeline, data is loaded into a storage repository before it's processed and transformed (if transformation is necessary for it to function in its destination; that depends on the use case).
Streaming pipelines process data immediately after ingestion (often with no transformation necessary) and deliver results in real or near-real time.
- Processing
Data processing is a broad term that includes many different activities. It refers to key steps that occur along the path, such as:
- Data ingestion. This is the process of collecting and importing data from a data source so that it can be moved.
- Data storage. As data flows through the pipeline, it may temporarily be preserved at key storage architectures, like a data warehouse, lakehouse, or mart. Each of these has its own function; for instance, data marts are small, dedicated structures for specific subjects (like sales data).
- Transformation. In most pipelines, data is modified in some way before reaching an end user or application. Transformation can involve data cleansing, deduplication, restructuring, and other processes that render data usable for its specific use case.
- Workflow
A pipeline’s workflow refers to a defined sequence of tasks and their dependencies. Because changes happen sequentially, one task might depend on another’s completion before it can begin. Upstream jobs (those related to the origin) must happen before downstream ones (which are related to the destination).
For example, data ingestion would be considered an upstream task, as it happens near the data source. A downstream task, such as transformation, can only happen once data’s been extracted and ingested.
- Monitoring
Data pipelines are intricate systems. Therefore, monitoring is essential to ensure the above components are working properly.
Data pipeline benefits and use cases
As enterprises grow to understand the power of business intelligence (BI), many are realizing how critical pipelines are to their success. Simply put, they ensure data gets where it needs to go, which is increasingly essential in a data-driven business world.
More specifically, data pipelines can:
- Streamline the flow of that data and make it markedly more efficient
- Uphold data integrity by providing a processing forum—for getting rid of faulty datasets, eliminating duplicates, tagging anomalies and other data quality issues
- Democratize data access across the enterprise, breaking down silos and generating actionable data analytics
- Enable key data-related operations in real time
Perhaps the best way to illustrate these benefits is to put them into context. Data pipelines have many notable use cases. Below are some of the most prominent:
Business intelligence
Enterprises that use pipelines can gather data from disparate sources, including CRM systems, supply chain platforms, and core databases. This empowers them to create unified datasets, reduce query times, activate intelligence, and improve access to key business insights for data-driven decision-making.
Machine learning applications
Pipelines can help data scientists capture and deliver the datasets they need to train artificial intelligence (AI) and ML models effectively. More importantly, they’re essential for transforming raw training data into formats relevant to their model’s particular application.
Recommendation engines
On a similar note, pipelines are instrumental to improving customer experiences in the e-commerce sector with greater, faster, and more powerful personalization. Streaming pipelines are especially useful in this case, allowing recommendation engines to fetch real-time data and tailor suggestions accordingly.
Healthcare analytics
Data pipelines help healthcare organizations integrate and process patient records, medical images, and other types of data. This, in turn, supports predictive analytics for diagnosing diseases and improving healthcare outcomes.
Pipeline challenges (and how to overcome them)
Data engineers, scientists, and analysts may encounter several obstacles when using data pipelines. Some of these include:
- Data quality issues. According to Gartner, poor data quality costs organizations nearly $13 million annually. These problems can be (and often are) addressed during processing, but they slow everything down as a result.
- Unexpected increases in data volume. This is especially problematic during peak hours. Retooling pipelines to deal with this isn't easy and can cause more delays.
- New data sources. Similarly, an ETL pipeline that's suddenly asked to handle real-time data won't be up to the task, requiring the building of a new pipeline on the fly.
- Lack of standardization. If a lot of different business units manage data differently, handling dataflow will be immensely complex.
- Security. Protection is a constant worry. However, it can be difficult to ensure data is always as secure as it needs to be as it moves through the pipeline.
Fortunately, there are ways to mitigate these issues and make the most of what your pipelines have to offer. Below are some helpful best practices:
- Build pipelines in the cloud as much as possible, as the added scalability means unexpected data volume upticks won't rattle you
- Use all data repository tools at your disposal—but, don't try to just use warehouses or lakes; use both as needed, especially if you anticipate an influx of new data sources
- Establish policies for departments to follow, specifically for encrypting data and, if necessary, using certain uniform formats
- Use data exploration to spot anomalies early on and rectify them before they cause problems for end users
- Use a comprehensive data platform like Teradata VantageCloud as a fundamental part of your pipeline
As the complete cloud analytics and data platform for AI, VantageCloud enables you to drive innovation faster and harness your data’s true potential. Combined with ClearScape Analytics™, you can activate data for any number of use cases—AI/ML deployments, business intelligence, you name it.
Connect with us to learn more about Teradata VantageCloud and how we can help your organization build the data pipeline you need to succeed.