Data Pipelines Explained: Definition and Varieties
Table of Contents
- By Bryan Lee
- Nov 13, 2023
In 2021, the average American spent over 8 hours on the internet daily. This screen time includes everything from streaming video, scrolling social media, and browsing the web. While these are all different processes, professional or casual, all of a user's actions contribute to the data pipeline of the providing service.
The same holds for any IoT (Internet of Things) device, such as smart watches, car computers, home security, and even pet monitors. The information flooding in from countless sources helps businesses make informed decisions and uphold a competitive advantage. This is otherwise known as a data pipeline.
What is a Data Pipeline?
Data pipelines are streamlined and automated systems used for compiling and utilizing data effectively. They're composed partly of management tools such as databases, data warehouses and data lakes, which leverage artificial intelligence to analyze and sort information from various sources. However, that's just the back end of the process.
Pipelines have a long line of stages that protect your data security, movement, and accuracy. These benefits are gained by implementing access controls, data masking, encryption, and general quality checks. Of course, businesses also have to set up the right tools to collect data in the first place.
These steps play a pivotal role in the modern technological landscape by providing quick, correct, and well-structured data for decision-making.
Types of Data Pipelines
There are various types of data pipelines designed to suit how each business handles information. The insights that a delivery company requires differ from what a publisher needs. Let's explore the most common types of pipelines.
ETL pipelines are short for Extract, Transform, and Load. These pipelines are the backbone of data integration and processing and are widespread across most industries. They excel at extracting data from a substantial pool of sources and efficiently cleaning it for analysis.
"Cleaning" refers to the process of transforming data into a more readily used format by deleting duplicate points, converting data into uniform measuring systems, removing irrelevant categories, and accounting for outliers.
The thoroughness of an ETL pipeline lowers the burden on developers and eases the data migration process from legacy systems to more modern solutions.
However, there are a few things that might make you opt for a different type of pipeline. They are rather complex to set up, requiring significant time and development power. Additionally, they're best suited for managing large volumes of data and require a corresponding amount of computational power.
Streaming Data Pipelines
Streaming data pipelines are meant to handle a steady influx of data. Because they're constantly receiving data, these pipelines are best designed for businesses needing flexible and timely decisions.
Some examples include industries like social media, stock trading, messaging applications, and security monitoring systems. Streaming data pipelines handle continuous data streams, cleaning and analyzing data at various points of the process rather than exclusively at the end.
The necessity of manual coding does raise the entry bar for businesses considering a streaming data pipeline. There are tools like Spark that can ease the process, but working with those services requires strong coding experience in multiple languages.
Batch Data Pipelines
As the name implies, batch data pipelines partition information into chunks before submitting it to a database or warehouse. Unlike a streaming pipeline, these are more suited for scenarios where real-time processing isn't an absolute dealbreaker.
Batch data pipelines typically operate at designated intervals such as daily, hourly, or weekly cycles. Although, the latter is relatively rare these days. This more long-term approach, at least in data processing, makes it ideal for historical analysis and reporting.
The benefits of batch data pipelines make them an enticing option despite their slow-sounding process. They're easily scalable since they're designed to handle the most significant volumes of data and produce results in a reliable time frame.
Some businesses may consider the batch process a weakness rather than a strength. Getting the full picture requires a complete cycle, so batch data pipelines introduce latency to your operations and prevent you from making real-time updates.
Hybrid pipelines share the strengths of batch data and streaming pipelines to answer a wide range of scenarios. They're capable of running routine processing to manage historical data while also analyzing a steady stream of data.
The flexibility to quickly switch between in-depth processing to more quick-twitch monitoring makes it an attractive choice for any business. However, this flexibility comes with unique challenges.
Building and maintaining a hybrid pipeline is more complex than focusing on a single type. The process demands more human resources and interference, since users must frequently decide what service they need.
Common Data Pipeline Tools
Many challenges around setting up a well-oiled data pipeline involve the setup process. Earlier iterations required massive human effort to organize, but today, various tools exist to automate those tasks.
While some of these tools are more user-friendly, many require baseline coding or data management knowledge to utilize fully. However, as related technology advances, they'll likely need less and less human interaction.
- Apache NiFi, Kafka, & Spark: A suite of pipeline tools dedicated to building pipelines through an intuitive interface. Its frameworks support real-time monitoring and batch processes, plus have an API library to customize pipelines to the user's needs.
- Talend: An open-source ETL pipeline tool that assists in data integration and transformation.
- AWS Glue: A fully managed, scalable, and serverless ETL pipeline solution that fully integrates with other AWS services. This makes it an easy inclusion for those already relying on the Amazon suite.
- Google Cloud Dataflow: Another serverless and fully managed ETL pipeline service. Like AWS Glue, Dataflow is fully compatible with other GCP services and Google Cloud.
Always Be Careful When Using Data Pipelines
Data pipelines are a non-negotiable part of data management for businesses aiming to scale their operations and stay competitive. Grasping the strengths and weaknesses of the different types of data pipelines will help you pick the right choice for your unique operations.
By integrating these processes into their operations, organizations can make better decisions in a faster timeframe than their competitors. Suppose you're struggling with which pipeline you should choose. In that case, IDStrong has a massive library of the different data types and challenges your business may face, such as preventing data loss or creating the proper security infrastructure. For more help, feel free to contact our team at any time!