Every millisecond, humans generate significant volumes of data, from various IoT devices such as our wearable devices to daily activities such as internet surfing and tracking our workouts. Data continues to accumulate. Statista estimates that by 2025, the amount of data will have increased to 180 zettabytes. That's far too much information.
Businesses acquire this data in order to improve their customers' experiences — such as understanding their customers' wants, enhancing their business and so on — in order for businesses to gain insights from this data. The data must be in a holistic format. To address these issues, the concept of data pipelines was developed.
In this article, you'll learn what a data pipeline is and how it works. We’ll walk you through the types of pipelines, challenges, common comparisons. Finally, we’ll examine the architectural design of the process and provide step-by-step directions for setting up a data pipeline.
What is a data pipeline?
Like any physical pipeline that moves something from one point to another, data pipelines involve moving data from one location to another.
In reality, a data pipeline is a set of processes that automatically move data from a data source and not performing some pre-processing steps on the data, such as cleansing, before moving it to its destination.
Types of data pipelines
There are different types of data pipelines. These three are the most common:
- Real-time data pipeline, also known as a streaming data pipeline, is a data pipeline designed to move and process data from the point where it was created. Data from IoT devices, such as temperature readings and log files, are examples of real-time data.
- Batch data pipelines are designed to move and process data on a regular basis. They're used to process data that isn't required immediately. The pipeline can be used to move and process data on a daily, weekly or monthly basis.
- Lambda architecture data pipeline combines real-time and batch data pipelines, allowing you to have both functions in a single data pipeline.
Quick comparisons: Data pipelines vs ETL, CI/CD and data lineage
- Data pipelines vs ETL (Extract, transform, load). Data pipelines are often compared to ETL, the process of extracting data from a specific source, transforming and processing it, and then loading it to your desired location. Though the terms overlap and are similar, it’s probably best to consider data pipelines the overarching category, with ETL a subset.
- Data pipelines vs CI/CD pipelines. In simple terms, a CI/CD pipeline is a process that guides software through the stages of development, testing and deployment into production. So, while data pipelines handle data, CI/CD primarily deals with software development.
- Data pipelines vs data lineage. Data lineage is simply the tracking of data movement from source to destination. It provides a detailed view of how data flows from the source to the point where it's processed and then to the destination. Data lineage makes it simple to determine whether an error occurred during the data flow.
Challenges of data pipelines
These are some of the challenges that occur in a data pipeline.
- Integrating a new data source. The data source to be integrated into the pipeline may be in a different format, posing a challenge. For example, a pipeline may be designed to accept only text data from the start, but if the new data to be integrated is in video format, pipelines must be reengineered to accept such data, which can take time.
- Increased latency in data processing. As the number of data sources grows, so does the time required to move and process the data, resulting in less efficiency.
- Missing data value. During transmission, or when data is transferred from one node to another, data may be missing partially or fully.
- Operational errors. In some cases, some parts of the pipeline may malfunction, causing the entire pipeline to fail.
Architecture design of a data pipeline
Now that we’ve got the basic concepts of a data pipeline, let’s turn to the actual process itself. The architecture design of data pipelines typically include the following five components.
1. Data source
A data source is a critical component of any data pipeline — it is where the data originates. So, components in the data pipelines assist in retrieving data from various types of data sources. Data sources often serve up streaming data and can include:
- IoT devices
- Raw data files
- Information from surveys, interviews, fieldwork and more
2. Ingestion tool
An ingestion tool is used to stage data from various data sources into a unified format. Consider it a merger that combines all our various data sources into one. Ingestion tools are classified into two types:
- Batch ingestion tools collect batch data, such as batches from data lakes, and transfer it at regular time intervals.
- Streaming ingestion tools collect different types of real-time data and transfer it immediately.
3. Transforming and enriching
This component is the part or parts that ensure the quality and integrity of your data. This is where you mold the data to achieve your goals. Steps in this phase can include:
- Cleaning the data
- Removing redundancy
- Enriching the integrity
- And more
After the data has been transferred and enriched, the next thing will be to transfer it to a unified system such as a database or data warehouse because it's been structured to be used for analyzing, reporting, modeling, etc.
5. Automation and monitoring
Another component is the automation of the data pipeline so that it can repeat the entire task, as well as monitor it for any errors or faults that may occur. The more visibility you have into what you're monitoring, the better. End-to-end visibility for data is called data observability, which helps facilitate overall data optimization.
How to set up a data pipeline: Step by step
Setting up your data pipeline is dependent on your use case — what you want to achieve — as well as the type of data you will be working with. You should set up your data pipeline in the same manner as the architecture design of a data pipeline.
Let's go over the steps for efficiently setting up your data pipeline.
- Determine your ultimate goal. To begin, you must understand the goal of your data pipeline, why you need one, and what your use case will be. Do you intend to use it for machine learning, reporting or something else?
- Know your data sources. You must be familiar with all of the data sources that will be integrated into the pipelines. Do you want to get the data from the web? What are the formats of the data? Is it text, video or audio?
- Determine which ingest strategy you’ll use. Once you have your data source, the next step is to decide what type of ingestion strategy to use. As data ingestion is classified into two type, you have to determine whether you’re collecting data in real time or in batches.
- Establish your data processing strategy. The following step: specify how you’ll process the data. Do you want to enrich it or just a subset of it?
- Design a storage strategy for the data pipeline output. Determine whether you intend to store the pipeline output on-premises or in the cloud. What format do you want the data to be stored?
- Ensure data monitoring and governance. You must ensure that data monitoring is in place. This provides an overview of how the pipeline is operating and alerts you if there's an attack on the pipeline.
- Create a consumption layer. This is the final stage in which the data from the pipeline storage can be used for your use case. In this phase, you plan how you want to integrate your consumption tool (for example, an analytics tool) with the pipelines, determine how to best utilize your data and so on.
Data pipelines are the first step in utilizing data
Because we collect so much data, it is wasted unless we use it. So, consider a data pipeline as a way for you to extract business value — while also promoting sustainable technology.
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