Data pipelines explained: turning raw data into insights

Organizations need data-driven decisions to grow their business, but they can only do that if the data they have provides value. Raw data isn’t suitable for analysis; it needs to be organized and cleaned first.

Without a clear process in place, however, this is easier said than done. According to a recent report, 67% of organizations don’t completely trust their data. When information is scattered across multiple systems in conflicting formats and structures, it’s difficult to gather insights. The key to resolving the problem is to build a single source of truth for all data.

This is where a data pipeline can help. It’s a structured framework for gathering raw data and processing it into useful and actionable insights. This guide will explain how data pipelines work, why they’re important, and how to build one. 

What is a data pipeline, and how does it work?

A data pipeline is a structured process that automatically moves data from its source to a new destination, transforming and processing it in the process. 

It’s a way to gather information from various locations and turn it into a clean, organized, unified dataset ready for analysis, decision-making, and AI applications. 

Data pipelines have multiple stages, with the output of one stage being the input of the next. The organization of these stages will impact the data’s usefulness of the data and how users consume it. 

With that in mind, let’s take a closer look at some common data pipeline components and their integration.

How do the components of a data pipeline work together?

A data pipeline architecture consists of six interconnected stages that run in sequence to turn raw information into trusted data. These stages are:

• Source identification: The first stage is identifying the sources of your structured and unstructured data, whether from databases, app analytics, user behaviour, social media, or other sources. 
• Data ingestion: Once your data sources are identified, data ingestion collects them and starts processing them through the data pipeline. This can happen in real time or in batches, depending on the speed required.
• Data processing: During processing, raw data is converted into cleansed and transformed data. This involves filtering errors, standardizing formats, and applying rules for consistency and ensuring the data is ready for enrichment. 
• Data enrichment: This stage uses data mining or AI techniques to enhance cleansed data with additional context from other sources, making it more valuable and insightful.
• Data storage: Next, the data is stored to create a centralized foundation for analytics. Data can be compiled differently and stored in multiple locations, such as cloud data warehouses, data lakes, or search indices.
• Consumption: The final step involves delivering the enriched data to employees and stakeholders so they can use it to glean actionable insights, often supported by visual dashboards for easier analysis.

Businesses need to follow data governance principles and best practices throughout the pipeline to ensure security and data policies are followed and enforced. 

Data pipeline architecture: how it works

The goal of a successful data pipeline is to transform raw data from multiple sources into useful information. It relies on a coordinated system of automated processes that ensure scalability as volumes increase.

Here’s a breakdown of the different stages, along with some example tools used to automate the data flow through the pipeline.

What’s important is that each stage’s output acts as the next stage’s input. For instance:

• Ingestion collects raw data from multiple sources for easy processing and standardization, regardless of origin.
• Processing ensures the data is standardized. Without this step, the data enrichment process would only amplify inconsistencies and errors within the data. 
• Enrichment adds context and intelligence to the data, ensuring its value for analysis and decision-making.
• Storing data in a unified database ensures it’s easily accessible so it can be visualized in dashboards and mined for insights. 

Modern cloud data pipeline solutions connect the process to create a smooth path forward. For instance, RecordPoint’s Connectors Framework allows you to connect all of your structured and unstructured data and create a holistic data inventory.

From there, our data pipeline solution helps to standardize and classify your data automatically, laying the groundwork for enrichment and consumption. 

Why does a well-designed data pipeline matter?

An established data pipeline can save an organization from making rough guesses and give leadership what it needs for informed decisions. In a world where most organizations lack confidence in their data, a strong data pipeline can give a company a competitive edge. A well-designed pipeline delivers several benefits:

• Efficiency: Data pipelines are typically fully automated. Automation reduces manual effort and errors, saves time, and improves data quality and accuracy.
• Scalability: Data pipelines can scale up to handle increasing data volumes and complexity. Cloud-based data pipelines handle scalability particularly well since they have access to multiple servers for heavy data loads. 
• Reliability: A well-planned data pipeline keeps data consistent and dependable by eliminating redundancies, fixing inconsistencies, and applying business rules to ensure data standardization.
• Agility: A streamlined data pipeline makes it easier to integrate new data sources, adjust workflows, and respond quickly to changing business requirements.
• Insights: A high-quality data pipeline delivers consistent, trusted, valuable data for analytics and decision-making. Quality data is also the foundation for AI/ML initiatives.

All of this contributes to a consistent framework that turns raw information into a strategic asset that you can use to respond quickly to trends and make smarter decisions across your entire organization. 

Modern trends in data pipeline design

Data pipeline design has rapidly evolved to meet today’s growing data engineering demands.

Businesses are increasingly relying on external tools, AI systems, and advanced analytics, presenting challenges. The purpose of pipelines has changed from moving and transforming data to ensuring quality and seamless integration with advanced solutions.

Three new trends are emerging:

• Data observability: Continuous monitoring of pipeline health and data quality is now standard. Teams must have tools to detect anomalies early and maintain trust in data as it flows through each stage. This sets the stage for analytics.
• Data contracts: Formal agreements between data producers and consumers are now commonplace to maintain schema integrity and prevent downstream disruptions. These contracts are particularly important to ensure quality in complex ecosystems. 
• AI and ML integration: Modern pipelines are built to feed data directly into artificial intelligence and machine learning workflows. This accelerates model development and makes it easier to scale AI initiatives as business needs change. 

In short, pipelines now need to be intelligent and purpose-built to help organizations extract maximum value from their data while supporting advanced analytics, spearheading AI initiatives, and staying compliant along the way. 

Types of data pipelines and when to use them

The type of data pipeline you choose affects processing type, costs, automation levels, transfer speed, and the level of expertise required. Broadly speaking, there are three key distinctions to keep in mind.

Batch vs stream processing data pipelines

Batch processing pipelines don’t transfer data in real time. Instead, they create a ‘batch’ of data and load it into storage at set intervals, preventing system overload. This approach is ideal for moving large amounts of data frequently without an immediate need for real-time analytics.

Streaming data pipelines transfer data in real time on an ongoing basis. This means employees don’t have to wait for data to get extracted, transformed, and loaded. Instead, they get immediate access to data, which enables faster decision-making. Streaming pipelines are also ideal for scenarios like IoT monitoring, threat detection, and live analytics. 

Traditional data pipeline vs. cloud-based data pipelines 

Traditional data pipelines consist of on-premises systems that require dedicated infrastructure and manual maintenance. They offer full control over processes and hardware, but they’re costly to scale and difficult to adapt when your needs change.  

In contrast, cloud data pipelines are fully managed, scalable data pipelines built on cloud infrastructure. They usually have lower costs and maintenance requirements, are fully automated, and are easier to scale than their counterparts. They’re the better choice if your organization needs agility, scalability, and seamless integration with modern technologies.

ETL vs. ELT pipeline

There are two primary approaches to data pipeline ordering: extract, transform, load (ETL) and extract, load, transform (ELT).

An ETL pipeline focuses on extracting data from various sources, transforming it into the right format, and loading it into a target system. It’s the best approach when there’s a need for strict data cleansing and standardization before storage.

Another alternative option is the ELT pipeline, where the transformation happens after raw data has been stored in data lakes. This approach makes use of the processing power of modern data platforms, warehouses, and lakes, making it more flexible for large-scale datasets.

Practical examples of pipelines delivering value

Data pipelines are essential for any business that wants to turn raw data into reliable insights. Here are some tangible examples to show how organizations can use this framework to improve efficiency, meet compliance reliably, and scale consistently:

• Analytics and BI: Companies can use data pipelines to unify their data into a single dashboard for optimal visibility. For instance, a retail chain could combine inventory and point-of-sale data to forecast demand and optimize its store’s stock levels. 
• Decision-making: Data pipelines unify and enrich data with additional context, making it more valuable for decision-making. For instance, a business could merge customer feedback and sales data to identify new ideas for a marketing campaign. 
• Operational efficiency: Data pipelines speed up repetitive reporting tasks and sync information automatically, saving time. As an example, a finance company could automate data collection from customer databases to speed up KYC processes. 
• Compliance: As pipelines standardize data and store it in one location, they also create consistent audit trails and enforce data retention policies across systems. This means businesses can more easily maintain security and comply with legislation. 
• AI readiness: Modern pipelines prepare and deliver high-quality datasets for machine learning and large language models, enabling companies to train models more consistently to support everything from chatbots to diagnostics and analytics. 

These use cases give businesses the tools to optimize their current operations and better prepare for the future, without introducing unnecessary risks. 

Challenges with modern data pipelines (and how to solve them)

Despite the benefits, managing a reliable data pipeline still has its challenges, especially as data volumes and complexity grow.

According to a study published in ScienceDirect, most data-related issues occur during the data cleaning stage, where issues like incorrect data types are the primary culprits. Integration and ingestion also remain the most difficult areas for developers to navigate. 

With that in mind, here are some of the top concerns and ways to address them.

As you can see, data governance and observability are no longer optional; they’re foundational to success. With pipelines spanning multiple systems and compliance becoming increasingly complex, organizations need to be confident they have full visibility over where their data comes from, how it’s transformed, who accesses it, and where it’s stored. 

Solutions like RecordPoint bring these elements together with automated governance, intelligent policy enforcement, and full data lineage tracking. This gives you the tools to build data pipelines you can trust. 

How to choose the right data pipeline architecture

To choose the right data architecture, you need to identify your target outcomes, your overarching business goals, and the capabilities you have to implement a given framework. Follow this structured process to determine the best approach:

Step 1: Define business and data goals

Start by identifying what you want your data pipeline to achieve. Perhaps you’re looking to improve compliance reporting, prepare datasets for AI, or facilitate real-time analytics. This initial step will help you define your priorities around cost, latency, and accuracy. 

Step 2: Assess data volume, velocity, and latency requirements

Based on your goals, determine how much data you’re handling and how quickly it needs to be processed. Sales data might only need to be reported daily, while IoT sensor data needs to be processed in real time, for instance. 

This assessment will help you choose between a batch, streaming, or hybrid pipeline. It can also guide your decision on whether to choose ETL, ideal for structured, pre-modelled data, or ELT, best for flexible large-scale systems where transformation happens after loading. 

Step 3: Identify integrations and sources

Now that you’ve planned your data pipeline, catalogue all of your internal and external data sources to begin the process. Gather information from across your ecosystem, from applications and APIs to data lakehouses and social media metrics. 

Solutions like RecordPoint can automate the process by sourcing and cataloging your data, wherever it’s located, without you having to move anything from its original source. 

Step 4: Evaluate scalability, governance, and compliance

How scalable do you need your platform to be? Cloud-native solutions offer elasticity with a lower overall cost compared to traditional systems, so they’re the best choice if you require agility as your business grows. 

Also consider governance. For complex integrations, this can be a challenge, so ensure you either have the expertise to handle this manually or invest in tools to support the process. RecordPoint can automate policy enforcement, retention, and lineage tracking across the entire data lifecycle to keep data secure and auditable throughout the pipeline. 

Step 5: Consider AI/ML compatibility 

If you plan to use transformed data for AI/ML initiatives, design your pipeline accordingly. This includes curating a specific data catalog for AI training, maintaining metadata for transparency, and ensuring data lineage is traceable to guarantee AI governance, explainability, and reproducibility. 

Step 6: Prototype, test, and monitor

Once you’ve finalized the details, build a minimum viable pipeline focused on one key use case, whether that’s analytics, AI, or compliance. Then, test your framework to check for latency and consistent data quality in real-world conditions. 

The key here is to implement data quality monitoring early to catch data drift and performance issues before they become more serious problems downstream. Remember that the strongest data strategies aren’t fixed in place. They evolve alongside your business.

Best practices for a reliable data pipeline architecture

Finally, let’s finish with some tips and best practices to ensure your pipeline produces consistently valuable data. 

• Scalability: Build a modular, automated, scalable architecture that can grow with your organization’s data demands.
• Version control: Build robust version control and backup processes so that changes to pipeline logic and data transformations can be rolled back if needed. 
• Error detection: Validate data before each load to find inconsistencies and errors early in the process.
• Data lineage: Maintain a clear lineage showing how data moves across your organization to ensure data can be traced back to its source.
• Monitoring: Implement continuous monitoring and alerting to detect pipeline failures or latency issues before they’re amplified downstream. 
• Auditing: Regularly audit and document updates to your pipeline, governance measures, and configurations to keep everything accurate and up to date. 

Collectively, these tips will help you build a data pipeline that’s efficient, consistent, and compliant. 

In brief

A well-designed data pipeline will turn raw information into trusted, high-quality data that fuels your growth. By combining the right architecture for your needs with governance and automation, you’ll be in the right position to scale confidently and make smarter decisions. 

However, creating a well-architected and high-performing data pipeline is anything but easy for data engineers, with hurdles like structuring data, incorporating scalability, validating data integrity, and monitoring for errors being common points of difficulty.

For this reason, organizations may find it better to use an automated data pipeline instead of building one from scratch. This streamlines many of the error-prone tasks while also freeing up more time for devs to work on other projects.

How RecordPoint can help

At RecordPoint, we offer an intelligent pipeline for records management. Using our Connectors, you can bring consistency to your data management, allowing you to connect to structured and unstructured data to create a true data inventory. 

Our data pipeline detects signals like data size, type, location, metadata, and data sensitivity (using data privacy signals like PII and PCI, as well as customer consent) before it inventories and classifies the data. This establishes a retention period for the data in line with legislation such as GDPR and with industry best practices.

The results are full visibility, automated compliance, and complete data governance across your entire ecosystem, giving you more time to put insights to work. Book a demo today.

FAQs

What is data orchestration?

Data orchestration is the process of coordinating and automating the movement and transformation of data across systems in a modern data stack. In essence, a data orchestrator makes sure that every component of the data pipeline runs in the correct order, with the proper dependencies, helping to maintain consistency and reliability. 

How does a well-planned data pipeline benefit data analysts?

A well-planned data pipeline simplifies how data teams collect and analyze information by ensuring the data is accurate and consistent. It automatically manages data flows, freeing up analysts from tedious manual tasks so they can focus their efforts on uncovering insights and driving action. 

What is data observability, and why does it matter?

Data observability is the act of continuously monitoring the quality and performance of data as it transitions through the pipeline. It’s a way of getting real-time visibility into problems like missing records or data drift before they become bigger problems downstream. In modern architectures, this is essential to ensure data is compliant and ready for analytics.

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