What is a Data Pipeline and How Does it Work?

What is a data pipeline?

A data pipeline is an automated process for moving, transforming, and managing data from source to destination. Monitoring these data pipelines end-to-end is essential for data engineering teams looking to develop a deeper understanding of their application metrics and improve their compute performance. Improving these data pipeline architecture and reliability is essential to keeping track of your data metrics. Thus, data pipeline observability tools are valuable for a data team member.

These tools can track the entire data journey while increasing the efficiency and reliability of your data pipeline. The best features for managing your data pipeline are often found in automated data pipeline software that monitors a company’s data performance throughout the entire pipeline.

Types of Data Pipelines

As you navigate the landscape of data pipelines and tools to manage pipelines, you will want to seek data pipeline examples that can help you achieve your business's needs. This is especially important before successfully implementing data pipeline tools like Acceldata to cater to the needs of data pipelines.

However, before you pick tools, you should know the different types of data pipelines. Data pipelines can be divided into two sections: batch processing and streaming.

Additionally, data pipeline tools are best implemented when you understand the data pipeline stages. The data pipeline process can be split into three main stages: sources, processing, and destination. The sources stage involves the initial process of capturing data that teams can then analyze. Following this, the processing stage occurs when the data is manipulated based on your company's requirements. Finally, the data pipeline's destination stage involves sending data to its destination, typically a data lake or data warehouse, for detailed analysis. These data pipeline stages help you prioritize predictable and scalable data pipelines, making measuring and analyzing your key data metrics easier.

Data Pipelines vs. ETL

When discussing data pipeline tools for your organization, you might have encountered the term "ETL." These two words are often used interchangeably, making comparing the differences challenging.

ETL is short for "extraction, transformation, and loading" and is a term to describe subprocesses that may occur within a data pipeline. Therefore, the core distinction between data pipeline vs. ETL is that ETL is a part of the data pipeline process that occurs depending on your company's needs and desires. It is helpful for data teams to look at an example of an ETL data pipeline to understand ETL and its role in their data pipeline better.

Additionally, you should learn about any unfamiliar terminology related to data pipelines. "Data pipeline" is a more generic term than ETL, which focuses on specific data pipeline stages. Thus, you should also seek more information on comparing a data pipeline vs. a data warehouse. A data warehouse is the final destination in the final stage of the data pipeline. Because Acceldata's software covers all parts of the data pipeline, data teams using Acceldata can:

• Improve data visibility
• Allow access to data from multiple different sources and hybrid data lakes and warehouses
• Align data with business outcomes
• Integrate with your other data systems

Data Pipeline Use Cases

Data pipelines are needed for almost every data analytics operation within an organization. Thus, organizations build data pipelines for several use cases depending on their needs.

• Data integration is one of the data pipeline's primary and most important use cases. These data pipelines are created to aid data migration from its source(s) to its destination(s) in batches or real-time.
• Data pipelines are useful for data replication and synchronization to help in data migration. These processes are important to ensure that data is consistent and reliably managed across multiple systems.
• In real-time analytics, you can also create data pipelines to stream and display data from the Internet of Things (IoT), social media, and transaction logs. This can help with real-time insight for decision-making. Real-time analysis is handy for risk management and processing transaction data in fraud detection.

• Data pipelines are also used for historical or batch processing. Here, the data pipeline workflow is scheduled to aggregate data over various periods—daily, weekly, or monthly—for reporting and analysis. This use case is helpful in operational analytics for supply chain management, machine learning, and Customer 360 View projects.

• Log and event processing is another use case. Data pipelines are sometimes built to implement log and event processing so users can monitor their application and system’s health and performance.

Data Pipeline Architectures

To effectively move data from source to destination, your data pipeline architecture must have a couple of components.

These are the various components:

• Data sources and ingestion layer: This layer generates the data by connecting or integrating with the source. The format, source, and data type depend on the business use case.
• Data processing layer: The data manages the data transformation and preprocessing.
• Data storage layer: This layer stores the transformed data in your destination, which is often a data warehouse.
• The data consumption and analytics layer handles your data analytics or machine learning workflow.
• Data orchestration and monitoring layer: Here, everything about deployment, managing, and scheduling of our data tasks takes place.

We have a more comprehensive article on data pipeline architectures; you should read all about it.

How can Data Pipelines Facilitate Machine Learning Tasks?

Data pipelines automate and standardize machine learning (ML) to improve various lifecycles processes and workflows, particularly reproducibility and scalability. By lifecycle processes, we mean the data collection, preprocessing, model training, evaluation, and deployment stages.

• During the data collection stage, data pipelines ensure that data from various sources is reliable, consistent, and good enough for data modeling.
• Once you have the raw data, the next stage is preprocessing. Data pipelines come in handy for automating the data transformation, normalization, and feature engineering process. This step will ensure that you have high-quality ML models.
• Data pipelines are essential for reproducibility when building ML models. These pipelines ensure preprocessed data flows for splitting the data and computing evaluation metrics.
• Data pipelines are integrated with CI/CD tools to deploy models into the production environment in the evaluation and deployment stage. These pipelines are also used to monitor model performance to ensure.

With ML data pipelines, for example, a financial company can build a pipeline to use transaction data to detect anomalies and deploy this model to monitor real-time predictions.

Data Pipeline Tools

Data teams can use data pipeline tools to help them analyze data metrics found in the pipeline. While many different data pipeline tools are accessible to data teams, it is best to seek one best suited for your team’s needs and end-to-end pipeline process. By choosing high-quality software like Acceldata, your data team can create a comprehensive data pipeline framework.

Not all data pipeline orchestration tools are created the same, and some tools may be more suited to your individual needs than they would be for a different team. Ideally, your data team will pick a tool that offers ETL and gives you a comprehensive look to help you understand the pipeline. These tools ensure you do not miss any step of the data pipeline and collect crucial, accurate data at every stage of the journey. With Acceldata’s numerous data pipeline tools, your data team can gain valuable insights into how your data performance compares to competitors and what changes can be made in the data pipeline to optimize performance.

Acceldata as Your Data Pipeline Tool

While you are aware that the data pipeline is crucial to understanding your organization’s data metrics, you might not have considered automating data pipelines or using comprehensive data pipeline architecture examples to base your data pipeline framework and data quality framework. If you have operated with manual data metrics, switching to a platform to automate your data pipelines may seem daunting. Successfully automating data pipelines is possible if data teams choose platforms suited to the team’s needs. Customizable platforms are essential to navigating data pipelines without wasting too much time and energy collecting data metrics.

Furthermore, consider reviewing a data pipeline architecture diagram to learn more about the various components and layers that make up data management. A data pipeline architecture diagram, such as the one offered by Acceldata, is crucial to fully understand the different data management layers, including data ingestion, processing, etc. With Acceldata’s expert data pipeline software, data teams can confidently collect essential data metrics and create a robust data pipeline architecture. Acceldata is vital for data teams that must increase pipeline efficiency and reliability while tracking a entire pipeline data journey.

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