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Introduction: The Data Preparation Bottleneck
According to a survey by Anaconda (Anaconda 2025), one of the leading software platforms for data professionals, approximately 22% of time in a data project is spent on data preparation (Anaconda 2022). With such a significant portion of the project dedicated to this phase, it raises important questions: Why does data preparation take up such a large share of resources and time? What exactly does data preparation involve? In this blog, we’ll explore these questions and aim to demystify the data collection process, making it less ambiguous and more efficient.
What Are Data Pipelines?
As the name suggests, data preparation involves all the steps necessary to get data ready for downstream tasks—whether that’s analysis, modeling, or visualization. This process is part of a broader category of tasks known as data pipelines. A data pipeline, in simple terms, is a tool that connects one task to another, facilitating the flow of data from one stage to the next.
A Real-World Scenario: Scaling Travel Logistics
Let’s break it down with a relatable scenario. Imagine you have an upcoming event several months away, and you need to make travel arrangements. The logistics can quickly become overwhelming—there are flights to book, accommodations to secure, weather conditions to consider, and traffic patterns to navigate. At first, organizing all of this might seem manageable; you could jot everything down on a physical or digital notepad and create a plan. But what if you need to organize travel for 10 people? It’s still doable with some phone calls and coordination. Now, imagine scaling it up further—what if you have 50 or 100 people to plan for? Suddenly, this task becomes daunting and time-consuming, and manually keeping track of all the details is no longer practical. Does the person in Figure 1 seem familiar?
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Here’s where a data pipeline can save the day. A data pipeline could automate the entire process by: - Gathering flight and accommodation details from various booking websites - Tracking weather forecasts - Monitoring traffic conditions in real-time.
By continuously updating with the latest information, the pipeline would provide you with a complete overview of your travel options, making it much easier to make informed decisions. This automated approach eliminates repetitive tasks, freeing up your time to focus on the fun parts of the trip, rather than getting bogged down in logistics.
How Data Pipelines Streamline Complex Tasks
In this blog, we’ll take a closer look at what data pipelines actually are. Figure 2 offers a simplified view of a data pipeline, the green pipeline symbol illustrates how data is ingested from various sources and then transformed into valuable information.
If you’re a data scientist, engineer, analyst, or simply curious, this blog is for you. Let’s dive in and discover how data pipelines can make your workflow more efficient, saving both time and resources.
Deep Dive: What Is Included in a Data Pipeline?
A data pipeline is a series of processes that transform and transfer data from one source to another. Figure 3, illustrates the effect of a data pipeline, it is often used to turn raw data into actionable insights. Let’s break down the core components and terminologies associated with data pipelines.
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1. Data Ingestion
Data ingestion is typically the first step in a data pipeline. It involves pulling data from various sources and loading it into designated sinks.
- Data Sources: These can include databases, APIs, user inputs, streaming platforms, or files.
- Data Sinks: These are the output destinations for the ingested data, such as databases, APIs, files, or visualization dashboards.
The sink may serve as either:
- A temporary storage for additional downstream processing (e.g., another pipeline).
- A final destination for immediate use or analysis.
Often, the data undergoes some level of transformation before being passed from the source to the sink, ensuring it meets the pipeline’s requirements.
2. Data Transformation
This intermediate step involves processing the ingested data to make it suitable for downstream tasks.
Transformations can include:
- Converting data into a different format (e.g., from JSON to CSV).
- Cleaning the data by handling missing values or removing duplicates.
- Aggregating data to generate summaries or compute metrics.
Transformation ensures raw data is reshaped into structured, usable formats that align with specific requirements.
3. Data Consumption
Data consumption refers to how the processed data from the sink is utilized. Some consumption methods include:
- Visualization: Creating dashboards or reports to present insights in an accessible way.
- Machine Learning Models: Using the data to train predictive or analytical models.
Regardless of the consumption method, implementing quality checks is crucial to ensure the pipeline’s output is accurate, consistent, and valuable.
4. Data Orchestration
Data orchestration focuses on managing and monitoring the entire pipeline to maintain efficiency and reliability.
Key aspects include:
- Automation: Ensuring the pipeline runs without manual intervention.
- Logging: Tracking the pipeline’s operations for debugging and auditing.
- Error Handling: Managing failures or interruptions gracefully.
- Performance Tracking: Monitoring for bottlenecks or inefficiencies to ensure smooth operation.
Orchestration ensures that the pipeline consistently delivers its outputs as expected, maintaining reliability and efficiency. To achieve this, various monitoring tools are employed, with cloud-based solutions being particularly popular. As illustrated in Figure 4, Azure Data Factory (Microsoft Azure 2025) provides a dashboard that highlights successful versus failed pipeline runs, enabling real-time oversight and prompt issue resolution.
Types of Data Pipelines
Data pipelines can be categorized into three primary types, each designed to address specific data processing use case:
1. Batch Pipelines
Batch pipelines are ideal for processing large data sets at scheduled intervals.
- Use Cases: Examples include daily or weekly data aggregations for reporting, periodic data backups, or transforming historical data for analysis.
- Advantages: Efficient for non-time-sensitive tasks that require processing large volumes of data.
2. Streaming Pipelines
Streaming pipelines handle real-time data processing, making them suitable for scenarios where immediacy is crucial.
- Use Cases: Live analytics, monitoring IoT devices, or powering recommendation systems in real-time.
- Advantages: Ideal for time-sensitive applications requiring consistent data flow and near-instant insights.
3. Hybrid Pipelines
Hybrid pipelines combine the strengths of batch and streaming pipelines, offering a versatile solution for handling both high-velocity and large-volume data.
- Use Cases: Industry implementations where both periodic and real-time data processing are required, such as e-commerce platforms managing inventory updates alongside real-time user activity tracking.
- Advantages: Provides flexibility to adapt to varying data processing requirements, ensuring both efficiency and availability.
Each type of pipelines plays a critical role depending on the time sensitivity and volume of the data being processed.
Importance of data pipelines
Efficiency: Automates repetitive tasks, reducing manual effort and minimizing errors.
Scalability: Handles large volumes of data seamlessly, making it easier to process growing datasets without performance bottlenecks.
Reproducibility: Ensures consistent and reliable results by maintaining a structured and repeatable workflow.
Real-World Applications Data pipelines support critical applications across industries, including:
- Recommendation Systems: Delivering personalized user experiences on platforms like Netflix or Amazon.
- Financial Forecasting: Analyzing historical trends and real-time market data for predictive insights.
- Real-Time Analytics: Supporting live monitoring in industries like healthcare, transportation, and e-commerce.
Figure 5 illustrates an example of a more intricate data pipeline that incorporates all the key components mentioned earlier, along with additional features. This pipeline ingests data from multiple sources, processes it through various transformations, and generates insights for comprehensive COVID-19 pandemic analysis.
What’s Next?
In this blog, we’ve explored data pipelines, emphasizing their role as essential tools for transforming and transferring data from one source to another. These pipelines have diverse applications across industries, streamlining processes and enhancing efficiency.
Think back to a project you’ve worked on—could it have benefitted from a data pipeline? Well now you can implement one. Tools like Python (Van Rossum and Drake (2009)) or Altair (VanderPlas (2018)) provide accessible frameworks to build and experiment with data pipelines. Take a look at the recommendations for a guided demo.
Whether you are automating repetitive tasks, managing large datasets, or enabling real-time data analysis, pipelines can be a game-changer.