What is a Data Pipeline?

A data pipeline is a systematic and automated process for the efficient and reliable movement, transformation, and management of data from one point to another within a computing environment. It plays a crucial role in modern data-driven organizations by enabling the seamless flow of information across various stages of data processing.

A data pipeline consists of a series of data processing steps. If the data is not currently loaded into the data platform, then it is ingested at the beginning of the pipeline. Then there are a series of steps in which each step delivers an output that is the input to the next step. This continues until the pipeline is complete. In some cases, independent steps may be run in parallel.

Data pipelines consist of three key elements: a source, a processing step or steps, and a destination. In some data pipelines, the destination may be called a sink. Data pipelines enable the flow of data from an application to a data warehouse, from a data lake to an analytics database, or into a payment processing system system, for example. Data pipelines also may have the same source and sink, such that the pipeline is purely about modifying the data set. Any time data is processed between point A and point B (or points B, C, and D), there is a data pipeline between those points.

As the volume, variety, and velocity of data have dramatically grown in recent years, architects and developers have had to adapt to “big data.” The term “big data” implies that there is a huge volume to deal with. This volume of data can open opportunities for use cases such as predictive analytics, real-time reporting, and alerting, among many examples.

Like many components of data architecture, data pipelines have evolved to support big data. Big data pipelines are data pipelines built to accommodate one or more of the three traits of big data. The velocity of big data makes it appealing to build streaming data pipelines for big data. Then data can be captured and processed in real time so some action can then occur. The volume of big data requires that data pipelines must be scalable, as the volume can be variable over time. In practice, there are likely to be many big data events that occur simultaneously or very close together, so the big data pipeline must be able to scale to process significant volumes of data concurrently. The variety of big data requires that big data pipelines be able to recognize and process data in many different formats—structured, unstructured, and semi-structured.

Efficiency

Data pipelines automate the flow of data, reducing manual intervention and minimizing the risk of errors. This enhances overall efficiency in data processing workflows.

Real-time Insights

With the ability to process data in real-time, data pipelines empower organizations to derive insights quickly and make informed decisions on the fly.

Scalability

Scalable architectures in data pipelines allow organizations to handle growing volumes of data without compromising performance, ensuring adaptability to changing business needs.

Data Quality

By incorporating data cleansing and transformation steps, data pipelines contribute to maintaining high data quality standards, ensuring that the information being processed is accurate and reliable.

Cost-Effective

Automation and optimization of data processing workflows result in cost savings by reducing manual labor, minimizing errors, and optimizing resource utilization.

Batch Processing

Batch processing involves the execution of data jobs at scheduled intervals. It is well-suited for scenarios where data can be processed in non-real-time, allowing for efficient handling of large datasets.

Streaming Data

Streaming data pipelines process data in real-time as it is generated. This type of pipeline is crucial for applications requiring immediate insights and actions based on up-to-the-moment information.

A typical data pipeline involves several key stages:

1. Ingestion
   Data is collected from various sources and ingested into the pipeline. This can include structured and unstructured data from databases, logs, APIs, and other sources.
2. Processing
   The ingested data undergoes processing, which may involve transformation, cleansing, aggregation, and other operations to prepare it for analysis or storage.
3. Storage
   Processed data is stored in a suitable data store, such as a database, data warehouse, or cloud storage, depending on the requirements of the organization.
4. Analysis
   Analytical tools and algorithms are applied to the stored data to extract meaningful insights, patterns, and trends.
5. Visualization
   The results of the analysis are presented in a visual format through dashboards or reports, making it easier for stakeholders to interpret and act upon the information.

A robust data pipeline architecture is essential for ensuring the effectiveness and scalability of the pipeline. Common components include:

Data Source

The origin of data, which could be databases, external APIs, logs, or other repositories.

Data Processing Engine

The core component responsible for transforming and manipulating the data according to predefined rules and logic.

Data Storage

Where the processed data is stored, ranging from traditional databases to fast data stores to hybrid cloud-based solutions.

Data Orchestration

The mechanism that coordinates the flow of data through the pipeline, ensuring that each step is executed in the correct sequence.

Data Monitoring and Management

Tools and processes for monitoring the health and performance of the data pipeline, as well as managing errors and exceptions.

ETL refers to a specific type of data pipeline. ETL stands for “extract, transform, load.” It is the process of moving data from a source, such as an application, to a destination, usually a data warehouse. “Extract” refers to pulling data out of a source; “transform” is about modifying the data so that it can be loaded into the destination, and “load” is about inserting the data into the destination.

ETL has historically been used for batch workloads, especially on a large scale. But a new breed of streaming ETL tools are emerging as part of the pipeline for real-time streaming event data.

While Data Pipelines and Extract, Transform, Load (ETL) processes share similarities, there are key differences:

Scope

Data pipelines encompass a broader range of data processing tasks beyond traditional ETL, including real-time data streaming and continuous processing.

Latency

ETL processes often operate in batch mode with a high latency that may not be suitable for real-time requirements. Data pipelines, especially those designed for streaming data, provide much lower-latency processing.

Flexibility

Data pipelines are more flexible and adaptable to changing data processing needs, making them suitable for dynamic and evolving business environments.

Data Security

Ensuring the security and privacy of sensitive data throughout the pipeline is crucial to compliance with regulations and protecting organizational assets.

Scalability

The architecture should be designed to scale horizontally or vertically to accommodate growing data volumes and processing demands.

Fault Tolerance

Building in mechanisms to handle failures and errors gracefully is essential for maintaining the reliability of the pipeline.

Metadata Management

Effective metadata management is crucial for tracking the lineage and quality of data as it moves through the pipeline.

Performance

While there are use cases such as batch processing with relatively long processing windows, many times a data pipeline feeds mission-critical and time-sensitive operations such as payment processing or fraud detection. In those cases, fast performance and low latency are critical for the business to meet their required service level agreements (SLAs).

Another example is a streaming data pipeline. In a streaming data pipeline, data from the point of sales system would be processed as it is generated. The stream processing engine could feed outputs from the pipeline to data stores, marketing applications, and CRMs, among other applications, as well as back to the point of sale system itself.

A third example of a data pipeline is the Lambda Architecture, which combines batch and streaming pipelines into one architecture. The Lambda Architecture is popular in big data environments because it enables developers to account for both real-time streaming use cases and historical batch analysis. One key aspect of this architecture is that it encourages storing data in raw format so that you can continually run new data pipelines to correct any code errors in prior pipelines, or to create new data destinations that enable new types of queries.

A more modern variant of the Lambda Architecture is the Kappa Architecture. This is a much simpler architecture because it uses a single stream processing layer for both real-time and batch processing.

A recent abstraction for data pipelines comes from an open source project, Apache Beam. It provides a programmatic approach to creating data pipelines, with the actual implementation of the pipeline depending on the platform on which  the pipeline is deployed. Apache Beam  provides a unified model for both batch and streaming data processing, providing a portable and extensible approach especially helpful when considering multi-cloud and hybrid cloud deployments.

Finance

Handling financial transactions, fraud detection, and risk analysis in real-time.

E-commerce

Managing and analyzing large volumes of customer data, transaction logs, and inventory information in real-time.

Business Intelligence

Deriving insights from historical and real-time data to inform decision-making processes.

Healthcare

Processing and analyzing patient records, medical images, and sensor data for improved diagnostics and patient care.

As technology continues to evolve, the future of data pipelines will likely involve advancements in:

Artificial Intelligence (AI) Integration

Incorporating AI and machine learning algorithms directly into data pipelines for automated decision-making and enhanced predictive analytics.

Serverless Architectures

The adoption of serverless computing models to further simplify and streamline the deployment and management of data pipelines.

Edge Computing

Extending data processing capabilities to the edge for faster and more efficient handling of data generated at the source.

Data Mesh

A paradigm shift in data architecture that decentralizes data ownership and processing, making it more scalable and adaptable to diverse organizational needs.

In conclusion, a robust data pipeline is a cornerstone of modern data-driven enterprises, enabling them to harness the power of data for informed decision-making and innovation. As technology continues to advance, the evolution of data pipelines will play a pivotal role in shaping the future of data management and analytics.

Hazelcast is a real-time data platform that combines a fast data store with a stream processor, allowing it to combine historical contextual data with real-time streaming data in  data pipelines for extremely fast, precise, and accurate decisions and actions. It can be used for either batch or real-time data pipelines, but its extremely low latency processing makes it ideal for real-time use cases. By leveraging fast distributed data storage and computing capabilities, Hazelcast accelerates data access and processing, making it a valuable addition to data-intensive workflows.