What Is a Data Pipeline? Types, Solutions, and Examples

Say you manage a sizable online bookshop. It’s always open. Every minute or second, customers place and pay for orders. Your website has to quickly execute numerous transactions using modest data, such as user IDs, payment card numbers, and order information. 

In addition to carrying out day-to-day tasks, you also need to assess your performance. For instance, you analyze the sales of a specific book or author from the preceding month to decide whether to order more for this month. This entails gathering transactional data and transferring it from a database supporting transactions to another system managing massive amounts of data. And, as is common, data needs to be transformed before being loaded into another storage system.

Only after these sets of actions can you examine data with dedicated software. How do you move data around, though? If you don’t know the answer, you probably need better software infrastructure, like data exchange solutions, extract, transform, load (ETL) tools, or DataOps solutions.

You probably need to learn what a data pipeline can do for you and your business. You probably need to keep reading.

A data pipeline is a set of steps for data processing. If the data still needs to be imported into the data platform, it’s ingested at the start of the pipeline. A succession of stages follows, each producing an output that serves as the input for the following step. This continues until the entire pipeline is constructed. Independent steps may coincide in some instances.

Data pipeline components

Before we plunge into the inner workings of data pipelines, it’s essential to understand their components.

• The origin is the entry point for data from all data sources in the pipeline. Most pipelines originate from transactional processing applications, application programming interfaces (APIs), or Internet of Things (IoT) device sensors or storage systems such as data warehouses or data lakes.
• The destination is the last place the data goes. The use case determines the final destination.
• Data flow is the transportation of data from source to destination and the changes done to it. ETL is one of the most often utilized data flow methodologies. 
• Storage refers to systems that maintain data at various stages as it moves through the pipeline.
• Processing comprises all activities and stages involved in consuming, storing, changing, and placing data.  While data processing is related to data flow, this stage focuses on implementation.
• Workflow specifies a series of processes and their dependencies on one another.
• Monitoring ensures that the pipeline and its stages function correctly and execute the necessary functions.
• Technology refers to the infrastructure and tools that support data transmission, processing, storage, workflow, and monitoring.

How does data pipelining work?

Data is typically processed before it flows into a repository. This begins with data preparation, whereby the data is cleaned and enriched, followed by data transformation for filtering, masking, and aggregating data to its integration and uniformity. This is especially significant when the dataset's final destination is a relational database. Relational databases have a predefined schema that must be aligned to match data columns and types to update the old data with the new.

Imagine you're collecting information on how people interact with your brand. This could include their location, device, session recordings, purchases, and customer service interaction history. Then you put all this information into a warehouse to create a profile for each consumer.

As the name implies, data pipelines serve as the "pipe" for data science projects or business intelligence dashboards. Data comes from various sources, including APIs, structured query language (SQL), or NoSQL databases; however, it’s only sometimes suitable for instant use. 

Data scientists or engineers usually perform data preparation duties. They format the data to fulfill the requirements of the business use case. A combination of exploratory data analysis and established business needs often decides the type of data processing a pipeline requires. Data can be kept and surfaced when correctly filtered, combined, and summarized. 

Well-organized data pipelines are the foundation for various initiatives, including exploratory data analysis, visualization, and machine learning (ML) activities.

Types of data pipelines

Batch processing and streaming real-time data pipelines are the two basic types of data pipelines.

Batch processing data

As the name indicates, batch processing loads "batches" of data into a repository at predetermined intervals, often planned during off-peak business hours. Other workloads are unbothered since batch processing jobs typically operate with enormous amounts of data, which could burden the whole system. When there isn't an urgent need to examine a specific dataset (e.g., monthly accounting), batch processing is the best data pipeline. It’s associated with the ETL data integration process.

Streaming real-time data

Unlike batch processing, streaming real-time data denotes that data needs to be continually updated. Apps and point-of-sale (PoS) systems, for example, require real-time data to update their items' inventory and sales history; this allows merchants to notify consumers whether a product is in stock. A single action, such as a product sale, is referred to as an "event," and related occurrences, such as adding an item to the shopping cart, are usually grouped as a "topic" or "stream." These events are subsequently routed through messaging systems or message brokers, such as Apache Kafka, an open-source product. 

Streaming data pipelines offer lower latency than batch systems because data events are handled immediately after they occur. Still, they’re less dependable than batch systems since messages might be missed inadvertently or spend a long time in the queue. Message brokers assist in addressing this problem with acknowledgments, which means a consumer verifies the processing of the message to the broker so it can be removed from the queue.  

Data pipelines vs. ETL pipelines

Some words, such as data pipeline and ETL pipeline, may be used interchangeably. However, consider an ETL pipeline a subtype of the data pipeline. Three fundamental characteristics separate the two types of pipelines. 

• ETL pipelines follow a predetermined order. As the acronym suggests, they extract, convert, load, and store data in a repository. This order is not required for all data pipelines. In fact, the emergence of cloud-native solutions has increased the use of ETL pipelines. Data intake still comes first with this pipeline type, but any transformations come after the data has been loaded into the cloud data warehouse.   
• Although the scope of data pipelines is greater, ETL pipelines frequently involve batch processing. They could also be inclusive of stream processing.  
• Finally, unlike ETL pipelines, data pipelines as a whole may not always need data transformations. Just about every data pipeline uses transformations to make analysis easier. 

Data pipeline architecture

A data pipeline's design comprises three key phases. 

1. Data ingestion. Data is acquired from many sources, including structured and unstructured data. These raw data sources are commonly referred to as producers, publishers, or senders in the context of streaming data. While organizations might opt to extract data only when they’re ready to analyze it, it’s better to first land the raw data in a cloud data warehouse provider. This allows the company to amend any past data if they need to change data processing operations. 
2. Data transformation. During this stage, a series of tasks are conducted to convert data into the format required by the destination data repository. These tasks incorporate automation and governance for repeated workstreams, such as business reports, ensuring that data is constantly cleaned and converted. A data stream, for example, maybe in layered JavaScript object notation (JSON) format, and the data transformation step will attempt to unroll that JSON to extract the essential fields for analysis.
3. Data repository. The transformed data is subsequently stored in a repository and made available to multiple stakeholders. The altered data is sometimes called consumers, subscribers, or receivers.

Benefits of data pipelines

Companies tend to learn about data pipelines and how they help businesses save time and keep their data structured when they’re growing or looking for better solutions. The following are some advantages of data pipelines businesses might find appealing. 

• Data quality refers to how easy it is for end users to monitor and access relevant data as it moves from source to destination. 
• Pipelines enable users to generate data flows iteratively. You can grab a tiny slice of data from the data source and present it to the user.
• Pattern replicability may be reused and repurposed for new data flows. They are a network of pipelines that generates a method of thinking in which individual pipelines are viewed as examples of patterns in a larger design. 

Challenges with data pipelines

Building a well-architected and high-performing data pipeline necessitates planning and designing multiple aspects of data storage, such as data structure, schema design, schema change handling, storage optimization, and rapid scaling to meet unexpected increases in application data volume. This often calls for using an ETL technique to organize data transformation in many phases. You must also guarantee that the ingested data is checked for data quality or loss and that job failures and exceptions are monitored.

Below are some of the most prevalent issues that arise while working with data pipelines.

• Increase in processing data volume
• Changes in the structure of the source data
• Poor quality data
• Insufficient data integrity in the source data
• Data duplication
• Delay of source data files
• Lack of a developer interface for testing

Use cases of data pipelines

Data management is becoming a progressively important concern as extensive data grows. While data pipelines serve various purposes, the following are three primary commercial applications. 

• Exploratory data analysis (EDA) evaluates and investigates data sets and reports on their primary properties, typically using data visualization approaches. It helps determine how to modify data sources to obtain the necessary answers, making it more straightforward for data scientists to uncover patterns, detect anomalies, test hypotheses, and validate assumptions.
• Data visualizations use popular visuals to describe data: charts, graphs, infographics, and animations. These information visuals explain complicated data linkages and data-driven insights in a way that’s easy to understand.
• Machine learning is a subfield of artificial intelligence (AI) and computer science that uses data and algorithms to mimic how people learn, gradually improving its accuracy. Algorithms are taught to generate classifications or predictions using statistical approaches, revealing crucial insights in data mining initiatives.

Real-life examples of data pipelines

Following are some IRL data pipeline examples of firms that have created modern ones for their application. 

• Uber needs real-time data to implement dynamic pricing, compute the most likely arrival time, and anticipate demand and supply. They deploy streaming pipelines that ingest current data from driver and passenger apps using technologies such as Apache Flink. This real-time data is incorporated into machine learning algorithms, which give minute-by-minute forecasts.
• Hewlett Packard Enterprise hoped to improve the customer experience with its predictive maintenance capability. They constructed an efficient data pipeline with streaming engines like Akka Streams, Apache Spark, and Apache Kafka.
• Dollar Shave Club required real-time data to interact with each consumer separately. After feeding info into its recommender system, the program chose which products to promote for inclusion in a monthly email addressed to individual customers. They created an automated data pipeline using Apache Spark for this practice.

Best practices for data pipelines

You can avoid the significant dangers of poorly constructed data pipelines by following the recommended practices outlined below.

• Simple troubleshooting: By removing unnecessary dependencies between data pipeline components, you only have to trace back to the failure site. Simplifying things improves data pipeline predictability.
• Scalability: As workloads and data volumes grow exponentially, an ideal data pipeline design should be able to scale and expand.
• End-to-end visibility: You can assure consistency and proactive security with continuous monitoring and quality inspections.
• Testing: After adjusting based on the quality tests, you now have a reliable data set to run through the pipeline. After you've defined a test set, you can run it in a separate testing environment; then compare it to the production version of your data pipeline and the new version.
• Maintainability: Repeatable procedures and rigorous protocol adherence support a long-term data pipeline. 

Data pipeline tools

Data pipeline tools support data flow, storage, processing, workflow, and monitoring. Many factors influence its selection, including business size and industry, data quantities, data use cases, budget, and security needs. 

The following are commonly used solution groups for building data pipelines.

ETL tools

ETL tools include data preparation and data integration solutions. They’re primarily used to move data across databases. They also replicate data, which is then stored in database management systems and data warehouses.

DataOps platforms

DataOps platforms orchestrate people, processes, and technology to deliver a trusted data pipeline to their users. These systems integrate all aspects of data process creation and operations.

Data exchange solutions

Enterprises use data exchange tools throughout acquisition to send, acquire, or enrich data without altering its primary purpose. Data is transferred so that it may be simply ingested by a receiving system, often by completely normalizing it. 

Various data solutions can work with data exchanges, including data management platforms (DMPs), data mapping software when moving acquired data into storage, and data visualization software for converting data to readable dashboards and graphics.

Other solution groups for data pipelines include the following. 

• Data warehouses are central repositories for storing data converted for a specific purpose. All major data warehouse solutions now stream data loading and enable ETL and emergency locator transmitter (ELT) operations.
• Users store raw data in data lakes until they need it for data analytics. Businesses develop ELT-based Big Data pipelines for machine learning initiatives using data lakes. 
• Companies may utilize batch workflow schedulers to programmatically declare workflows as tasks with dependencies and automate these operations.
• Real-time data streaming software processes data continually created by sources such as mechanical sensors, IoT and Internet of medical things (IoMT) devices, or transaction systems. 
• Big data tools include data streaming solutions and other technologies enabling end-to-end data flow. 

Detailed data dates deeply

Back in the day, volumes of data from various sources were stored in separate silos that could not be accessed, understood, or analyzed en route. To make matters worse, the data was far from real-time. 

But today? As the quantity of data sources grows, the rate at which information traverses organizations and whole sectors is quicker than ever. Data pipelines are the skeleton of digital systems. They transfer, transform, and store data, giving businesses like yours meaningful insights. However, data pipelines must be updated to match the pace with the increasing complexity and number of datasets. 

Modernization does require time and effort, but efficient and contemporary data pipelines will empower you and your teams to make better and quicker choices, giving you a competitive advantage.

Want to learn more about data management? Learn how you can buy and sell third-party data!