Selecting the right ETL (Extract, Transform, Load) tool is more than a software decision—it's about unlocking your data's full potential. The modern data landscape has changed and moved the goalposts with regards to the criteria for evaluating an ETL tool. 

With unstructured data types, data engineering overload, and AI now a reality, what should you look for when evaluating ETL tools?

In this guide, we list eight critical factors you must look at before choosing an ETL tool. 

1. Evaluate ease of use for technical and non-technical users

 A well-designed ETL tool empowers everyone in the data ecosystem to contribute effectively. Engineers can work in their preferred environment with code-based configurations and version control, while analysts and business users can confidently adjust pipelines and verify data quality through intuitive interfaces.

Technical teams benefit from having both CLI tools and web interfaces at their disposal. They can develop locally using familiar Git workflows, while still having clear visibility into pipeline status and performance. Meanwhile, analysts can use SQL-first interfaces to build transformations and business users can monitor progress and schedule refreshes without requiring engineering support.

This multi-layered approach fosters true data collaboration. Engineers can focus on complex problems while other teams safely self-serve their data needs. The result is faster delivery of data projects and better alignment across teams.

When evaluating tools, look specifically for: 

• The quality of API documentation
• Availability of native connectors
• Testing frameworks for data quality
• Clear version control workflows
• Granular access controls

The interface should expose these technical capabilities in ways that match how each team prefers to work - whether that's through YAML configs, SQL transforms, or web-based monitoring dashboards.

Look for tools that offer clear documentation, helpful error messages, and the right mix of code-first and UI-based interactions for your team's needs.‍

2. Ensure your ETL tool leverages modern data platform capabilities

As organizations and data volumes grow, your ETL tool must effectively harness the power of your unified data platform rather than maintaining separate processing infrastructure.

Modern ETL tools should take full advantage of your data platform's native processing capabilities. This approach lets you process data directly where it lives, eliminating the need for separate processing clusters while maintaining high performance.

The focus shifts from maintaining separate processing layers to optimizing how your tool uses platform resources. Look for features that automatically scale compute resources based on workload demands and intelligently manage costs by releasing resources when they're not needed.

Real-time data needs are handled through your platform's native streaming capabilities rather than separate streaming infrastructure. Your ETL tool should seamlessly integrate with these features, allowing you to process data as it arrives while maintaining data quality.

Data quality and governance become platform-native through built-in features for access control and data validation. Look for ETL platforms that leverage these capabilities for monitoring and managing data quality across your entire platform.

A well-designed modern ETL tool treats your data platform as both the storage layer and compute engine, focusing on orchestration rather than processing. This approach gives you scalability on demand while keeping your architecture simple and maintainable. 

Teams can focus on building data pipelines rather than managing infrastructure, ensuring reliable data delivery with built-in performance optimization.

3. Evaluate your tool’s data transformation capabilities

While a basic ETL tool might handle routine cleaning and formatting, more complex scenarios demand advanced functionality. This can include detailed aggregations, complex joins across multiple data sources, and real-time transformations for streaming data..

Without strong transformation capabilities, you risk working with partial or inaccurate datasets that tell only part of the story. Enterprises with diverse departments and vast customer data often need transformations that go beyond the basics—like machine learning integrations, deep merges of multiple data streams, or intelligent data derivation tied to specific business rules.

Tools offering real-time or near-real-time transformations are particularly valuable. For instance, a healthcare provider matching patient data from various systems might need immediate updates to ensure doctors have the most current records. 

Automating transformations can streamline processes and potentially reduce human error. Exploring the advantages of ELT may enhance data handling by transforming it after loading into the target system.

Strong transformation capabilities also support compliance and auditing, as you can track exactly how data changes at each stage. This visibility is crucial in regulated industries, where you must prove data accuracy and accountability.

4. Evaluate security and compliance

Handling sensitive data requires a rigorous approach to security. Healthcare, finance, and other sectors process personal, financial, or regulated information, leaving no room for oversight gaps. A lapse can trigger severe legal and reputational fallout, making proactive security measures essential.

Adhering to regulations like GDPR, HIPAA, or SOC 2 means establishing solid processes for both internal and vendor-related data handling. Tools with auditing features and transparent logs help you track how data changes over time and who is responsible at each step. 

Thorough vendor security assessments are critical for ensuring everyone in your supply chain keeps data safe. Vendors with a track record of timely updates and strong customer support can be invaluable in this environment.

A robust security and compliance stance isn't just about avoiding legal trouble. It builds trust with clients, customers, and stakeholders, reinforcing that you handle their data responsibly—even under challenging circumstances.

5. Use ETL tools with comprehensive monitoring and observability

ETL pipelines can be complex, with multiple moving parts and countless data touchpoints. Without effective monitoring, a small interruption can escalate into significant data quality issues or system downtime. Observability ensures you can see inside each pipeline to spot and fix issues quickly.

Centralized logging is essential. If something goes wrong—or even seems off—you need detailed logs of each stage in the transformation process. Drilling down into data sets after loading can uncover trends in errors or performance lapses, shortening troubleshooting time.

Fault tolerance and automated alerts prevent small glitches from becoming major crises. If a connector fails or an API expires, real-time alerts give you immediate notice. Data lineage tracking adds clarity. Knowing exactly how data transforms at each step helps you trace problems back to their source.

Real-time dashboards provide at-a-glance insights into performance. With immediate visibility into throughput and error rates, you can detect anomalies or slowdowns before they impact end users. This approach keeps decision-makers confident that the data is reliable when it reaches their dashboards.

Scalability matters here too. As data sets expand, your monitoring strategy needs to keep pace. A robust observability framework should scale alongside your data volumes and complexity.

6. Evaluate costs and ROI

Budget surprises often stem from hidden fees. Licensing might seem manageable at first glance, but add-ons for training, maintenance, or extra features can inflate expenses. Examining pricing tiers and service agreements closely helps you avoid these pitfalls.

• Pay-as-you-go, flat-rate, or volume-based models each have their advantages and drawbacks. 
• Usage-based structures can save money if your workloads are predictable but can spike if data volumes surge. 
• Flat-rate models offer consistent costs but might lead to overpaying if loads are low.

Total cost of ownership (TCO) includes more than direct licensing. Implementation, necessary hardware (if not cloud-based), and staff training should all factor into your calculations. An in-depth conversation with vendors can reveal any fees for premium support or advanced features.

Understanding the full cost structure helps you plan your ROI effectively. Don't overlook training. Even the most user-friendly platforms require some onboarding to maximize their value. Taking advantage of vendor-provided tutorials or workshops can speed up adoption, but be sure to budget for that time and any associated costs.

Knowing your actual data needs is the final piece. Overcommitting to a high-tier plan can be as wasteful as underestimating and facing overages. 

Strategic considerations for selecting the right ETL tool

For data leaders and IT executives, it's essential to evaluate tools not only on their technical merits but also on how they fit within the broader context of business goals, existing technology stacks, and future growth plans. Organizations like AMGEN have undergone successful modernizations, highlighting the impact of selecting effective ETL tools on data processes.

When considering tools to build scalable ETL pipelines, look at:

• Scalability: Assess the tool's ability to handle current and anticipated data volumes and complexity.
• Technical Compatibility: Ensure seamless integration with existing data infrastructure, platforms, and preferred programming languages.
• Costs: Consider total cost of ownership, including licensing fees, infrastructure costs, and resource expenditures.
• Orchestration: Coordinate pipeline execution while automatically surfacing runtime errors, logs, and performance metrics directly in their interface for quick troubleshooting. 
• Compliance and Security: Evaluate the tool's capabilities in supporting data governance, compliance with industry regulations, and security protocols.
• Skill Sets and Team Composition: Align the tool's complexity with your team's technical expertise to maximize efficiency and productivity.

Prophecy is a modern, low code data transformation tool that simplifies your data pipeline’s design and management, along with checking all the boxes above. Explore the future of data transformation today.