Data Pipelines: Optimize and Modernize Your Data Strategy

While traditional ETL processes have served organizations well for a long time, today's data landscape presents new challenges that benefit from more flexible approaches.

But simply moving your pipelines to the cloud isn't enough. The architecture decisions you make today will either empower your teams to innovate or leave them perpetually fighting fires.

In this article, we dive into what makes a modern data pipeline architecture tick – from core components to best practices, showing you strategies to build flexible, scalable, and modern data pipelines.

What does a modern data pipeline architecture look like?

At its core, a modern data pipeline architecture extracts and transforms your data, allowing your team to maintain multiple views of the same data for different use cases – from aggregated analytics to granular transaction analysis.

The collaborative aspect of modern pipeline architecture deserves special attention. When your data transformations are visible and understandable to all stakeholders, teams can work together more effectively. This transparency helps eliminate the traditional silos between technical and business teams.

Instead of juggling data across various systems to support different use cases – one for business intelligence dashboards, another for AI/ML modeling, yet another for regulatory reporting – you get a unified platform fordata lakehouse management, where data lives and transforms.

Four core components of a modern data pipeline

Understanding key data engineering principles helps us break down the building blocks of a modern data pipeline:

• Data ingestion: Modern data pipelines need to work with all types of data – structured, semi-structured, and unstructured – to support your downstream analytics and AI use cases. Your ingestion layer should facilitate smooth integration of these diverse data types into your transformation workflows.
• Data transformation: With modern ELT pipelines, transformations happen after data lands in your cloud data platform or lakehouse environment. You can see your data flow using visual transformation tools, making it easier to understand complex transformations without diving into code.
• Orchestration and workflow management: Modern orchestration needs to provide clear visibility into the pipeline's health. When issues arise, your team should quickly identify the root cause and implement fixes without disrupting dependent processes.
• Governance and quality checks: Data governance isn't an afterthought in modern architectures – it's a foundational element that should be present at every stage of your pipeline. This means implementing automated quality checks, maintaining clear lineage, and ensuring compliance throughout the transformation process.

Five best practices to modernize data pipeline strategy

Modernizing your data pipeline involves establishing practices that scale with your organization's needs and data engineering skills. Let's look into five approaches that successful data teams are using today.

1. Adopt a collaborative, visual approach

The complexity of modern data pipelines demands better visibility into transformation logic. Visual approaches to pipeline development are about making complex data flows understandable and maintainable across your organization.

When your transformations are visible and understandable, you eliminate knowledge silos that often plague data teams. Business analysts can validate logic by seeing the flow of data. Data scientists can understand upstream dependencies affecting their models. New team members can grasp complex pipelines without diving into thousands of lines of code.

The key is finding the right balance between visual development and code. Your visual tools should generate production-grade code that can be version-controlled, tested, and optimized. This approach supports self-service analytics, giving business users direct access to data insights.

Visual approaches also facilitate better code review processes. When reviewers can see the data flow alongside the code, they can more effectively validate both the technical implementation and the business logic. This leads to higher-quality deployments and fewer production issues.

The team collaborative aspect extends to testing as well. Visual representations make it easier to identify edge cases and potential failure points, allowing you to build more comprehensive test suites. You can involve business stakeholders in validation, ensuring that transformations meet business requirements before deployment.

However, this visualization shouldn't come at the cost of performance. Tools like Prophecy’s Data Transformation Copilot, with its intuitive drag‑and‑drop interface, ensure that your team enjoys the clarity of visual development at scale without sacrificing the engineering rigor required for scalable production systems.

2. Implement incremental data transformation

The key to successful incremental processing lies in your data architecture decisions. Your transformations need to clearly identify and track what's new, what's changed, and what's been processed. This requires careful consideration of change tracking mechanisms.

Implementing incremental transformations also demands robust error handling. When processing fails, you need to clearly understand which increments were processed successfully and which need to be retried. This granular control helps prevent data loss while maintaining system reliability.

Consider your transformation dependencies carefully. Incremental processing works best when your transformations are modular and loosely coupled. This architecture allows you to process changes in parallel where possible while maintaining proper sequencing where dependencies exist.

Testing also becomes particularly crucial with incremental transformations. Your test suite needs to verify not just the transformation logic, but also the increment detection and handling mechanisms. Comprehensive testing helps ensure that no data falls through the cracks during incremental updates.

Performance monitoring takes on new importance in incremental architectures. You need visibility into processing times for different increment sizes, helping you optimize your transformation logic and resource allocation. This data helps you make informed decisions about batch sizes and processing windows.

Prophecy’s modern approach to ETL modernization converts legacy workflows into cloud-native data pipelines. By automatically restructuring your existing code, Prophecy enables precise change tracking, robust error handling, and optimized performance—ensuring your incremental processing is both reliable and scalable as your data demands grow.

3. Integrate robust governance

   Strong governance starts with version control. Your pipeline code, configuration, and transformation logic should all be versioned, allowing you to track changes over time and roll back when needed. This foundation ensures you can maintain control as your data operations scale.

Automated testing plays a vital role in governance. Beyond basic unit tests, consider implementing data quality checks that validate business rules, format requirements, and relationship constraints. These automated checks act as an early warning system for data issues, helping maintain trust in your pipelines.

Access control needs to be both granular and manageable. Your governance system should make it easy to control who can view, modify, and execute different parts of your pipeline while maintaining a clear audit trail of changes and access patterns. Most importantly, you should inherit data permissions from the policies you already have in place for your cloud data platform - e.g., Databricks Unity Catalog. Your environment is more naturally secure when permissions don't need to be manually replicated.

Documentation becomes a critical governance tool when it's treated as a living part of your system. Automated documentation generation helps ensure that your pipeline documentation stays current with the actual implementation, reducing confusion and maintenance overhead.

Platforms like Prophecy, with automated testing and documentation generation features, embed robust governance directly into your pipeline development process. By enforcing version control and generating dynamic documentation, every transformation meets compliance requirements while accelerating your development cycle.

4. Promote self-service data access

Start with a solid data modeling foundation. Your transformed data should be organized in intuitive ways that make sense to business users. This might mean creating different views of the same data to serve various use cases, from detailed transaction analysis to aggregated reporting.

Security controls need to be both robust and granular. Implementation patterns should allow you to grant access at the right level – whether that's entire datasets, specific columns, or filtered views. This granularity helps you maintain compliance while still giving users the data they need.

Consider implementing a semantic layer that translates technical data structures into business-friendly terms. This layer helps bridge the gap between technical implementation and business understanding, making it easier for users to find and use the data they need.

Data discovery also plays a crucial role in self-service success. Users need to easily find relevant datasets, understand their contents, and trust their quality. This requires clear documentation of data assets, including their sources, update frequencies, and known limitations. This again is made possible, in part, by integrating with systems you already have in place, such as Databricks Unity Catalog.

Performance optimization becomes particularly important in self-service environments. Your architecture needs to handle concurrent queries efficiently while preventing any single user from impacting overall system performance.  This can mean query optimization techniques and relying on cloud data platforms such as Databricks which are perfectly suited to address scaling requirements.

5. Design for agility and future growth

Building data pipelines that can adapt to changing requirements while scaling efficiently is perhaps the most crucial architectural challenge you'll face. Your architecture needs to accommodate growth in both data volume and complexity without requiring constant redesign.

Start with modularity as a core principle. Break down complex transformations into smaller, reusable components that can be tested and maintained independently. This approach not only makes your pipelines more manageable but also enables parallel development across teams.

Consider how your architecture handles schema evolution. Your pipelines should gracefully adapt to new fields or change data types without breaking existing transformations. This flexibility is crucial for maintaining system reliability as your data sources evolve.

Resource efficiency becomes increasingly important as you scale. Your transformations should optimize compute resource usage through techniques like partition pruning and predicate pushdown. This efficiency helps control costs while maintaining performance as data volumes grow.

Error handling deserves special attention in scalable architectures. Your pipelines need to handle failures gracefully, providing clear visibility into what went wrong and enabling quick recovery. This becomes particularly important as dependencies between transformations grow more complex.

Monitoring and observability need to scale with your system. Implement comprehensive metrics collection for both technical performance and data quality. These metrics help you identify potential issues before they impact business operations and guide your optimization efforts.

Platforms like Prophecy with compiler technology ensure your data pipelines remain agile and future-proof by converting AI-driven and visual designs into highly optimized, native Spark or SQL code.

This capability allows your architecture to seamlessly adapt to growing data volumes and evolving business requirements—supporting long-term scalability and operational excellence.

Examples of modern data pipeline architectures

Here’s how organizations have implemented these architectural principles and best practices to solve complex data challenges.

• Healthcare analytics at scale: Aetion's Story

Aetion faced a significant challenge: processing 500 million patient records to enable swift hypothesis generation and insights across the biopharmaceutical lifecycle. The complexity wasn't just in the volume – it was in maintaining data quality and governance while enabling rapid analysis.

To overcome these hurdles, Aetion reimagined their data pipeline with a modular design that emphasized reusable components and streamlined processes. This modernization accelerated the launch of their Discover product by enabling the team to efficiently process over 500 million records within just two months.

This significant improvement not only allowed them to meet the Discover launch deadline but also enabled them to onboard their first three customers ahead of schedule.

• Financial analytics: streamlining critical data access

A global biotechnology leader, Amgen, faced a common yet critical challenge in their Financial Insights Technology (FIT) team. Their data landscape was fragmented across Excel-based reporting and multiple legacy ETL systems, creating bottlenecks in accessing vital financial data.

The proprietary nature of their existing workflows made scaling difficult and onboarding new team members a time-consuming process. Their modernization strategy focused on three key areas:

• Consolidating disparate data systems
• Implementing visual development workflows
• Establishing centralized governance

The transformation delivered impressive results. With a twofold increase in KPI processing capacity, the FIT team was able to refresh and update 500 dashboards for over 1,500 users, ensuring that decision-makers received near real-time insights.

The 20% boost in data processing speed significantly reduced the turnaround time for critical financial reporting, enabling faster, more informed decisions.

By streamlining workflows and reducing manual interventions, Amgen’s modernization initiative not only improved efficiency but also allowed the team to focus on high-value analysis—ultimately supporting the organization’s mission to deliver innovative treatments.

Reimagine your data pipeline

Transforming your data pipeline is essential to meet the demands of today's complex data environments. Here's how Prophecy helps you modernize your data pipeline:

• Low-code visual development: Empower your team with an intuitive drag-and-drop interface that accelerates pipeline creation without sacrificing code quality.
• Unified platform: Handle a variety of data (structured, semi-structured, unstructured) and types of use cases (AI, BI, reporting) on a single, cohesive platform for seamless data processing.
• Built-in governance: Ensure compliance and data quality with version control and comprehensive testing.
• Scalable architecture: Adapt effortlessly to growing data volumes and evolving business requirements with highly optimized, native Spark or SQL code.

Ready to take the next step? Learn more about the future of data transformation and how you can position your organization ideally.