Life after Talend and Informatica: Migrating to the future of data

The AI revolution is transforming how organizations approach data. However, while over 50% of organizations globally plan to deploy AI in 2025, 90% of enterprise data still sits trapped in on-premise legacy systems.

If you're one of the countless data leaders wrestling with Informatica, Talend, or other legacy ETL tools, you're not alone in feeling the squeeze between AI ambitions and infrastructure limitations. Your legacy systems weren't designed for today's AI-driven world. They struggle with the unstructured and multi-modal data that modern AI workflows demand.

Meanwhile, your teams are spending most of their time managing data pipelines instead of creating strategic value. The cost? Not just the skyrocketing license fees—which can reach millions annually—but the opportunity cost of being left behind in the AI era.

The good news? There's a proven path forward to a more modern solution. Many organizations have already blazed the trail, achieving dramatic improvements in performance, cost reduction, and AI readiness.

The hidden costs of legacy ETL systems

Your current pain points aren't just operational inconveniences—they're strategic barriers to your organization's future. Legacy systems like Talend and Informatica present numerous operational challenges in today’s AI-first environment.

Lack of support for AI

Legacy ETL systems were architected for a structured data world. Today's AI applications increasingly rely on unstructured data like video, images, audio, and text. Your Informatica or Talend infrastructure simply wasn't built to handle this shift.

The scalability limitations are equally problematic. On-premises systems can’t handle the volume of data modern enterprises generate. That explains why Infinite Lambda found that 90% of on-premises data never gets used for analytics.

Operational burden

Perhaps most frustrating is the operational burden. Your data teams shouldn't be getting calls in the middle of the night because pipelines have failed.

Sadly, that’s life at many organizations. Data teams told Infinite Lambda that they spend 80% of their time managing data, frequent schema changes, troubleshooting, etc. The lack of standardization and automation means teams are constantly in firefighting mode, leaving only 20% of their time to create new, unique value.

A large operational expense

Talend and Informatica require enormous licensing fees that, as data volumes and usage grow, make budgeting unpredictable. Even worse, the expensive infrastructure required to run them is a huge capital investment.

Additionally, the lack of automation in legacy ETL systems means most problems must be solved through manual intervention. This inflates operational expenses and distracts teams from strategic, value-adding activities.

Talend and Informatica recognize they need to get customers off-premises and onto the cloud. They’ve done this largely by raising prices and forcing them into their cloud-based solutions. These offerings are three times the cost of running on-premises and have fewer features. Even worse, they retain the same legacy processes that make on-premises ETL systems so unwieldy.

Moving to a modern data framework

As a result, many Talend and Informatica users remain stuck. Only 25% of today’s data is moving to the cloud. The remaining 75% remains on-premises due to technical debt, compliance fears, and the lack of a clear migration strategy.

If you’re moving to the cloud anyway, it’s a good time to consider a more modern alternative - one built to handle unstructured data that integrates with today’s modern AI ecosystem. That may lead you to think about the processes you need and the technologies that could support them.

At dbt Labs, we've spent a lot of time thinking about this. The result is the Analytics Development Lifecycle (ADLC)—an eight-phase framework that brings the best practices of software engineering into data and analytics.

The ADLC is a mental model for how mature organizations approach data work. Built on the Software Development Lifecycle (SDLC) in software engineering, it encompasses development, testing, deployment, and monitoring in a continuous cycle that ensures your data products are reliable, scalable, and maintainable. More importantly, it provides a roadmap for achieving the kind of AI-ready infrastructure that will serve your organization for years to come.

In the context of enterprise AI, the ADLC becomes even more critical. Modern AI workflows require structured data that's accessible, documented, and governed. You need automatic trust frameworks to ensure data integrity, comprehensive documentation to provide context for AI systems, and robust governance to manage how AI applications consume your data. These are capabilities that legacy ETL systems simply can’t provide.

The ADLC framework helps you evaluate both your current state and your desired future state. It's a guide for what tooling you need and how different components should work together harmoniously. Most importantly, it recognizes that data work never stops—you need processes that support continuous improvement and adaptation.

dbt: The modern data transformation solution for AI

This is where dbt comes in. dbt encapsulates everything you need to achieve the ADLC vision, sitting on top of your data warehouse to make software engineering best practices accessible across your entire organization. It supports all the key components of a data solution - data transformation, development, observability, cataloging, and semantics.

An AI-first data transformation solution

We’re used to the idea that Large Language Models (LLMs) consume unstructured data. However, often, the real value lies in an organization’s structured data.

Making that data reliable and available for LLMs involves leveraging the components that dbt provides:

• Automatic trust frameworks, such as data testing and data lineage, ensure data quality and traceability.
• Writing documentation alongside your transformation models gives LLMs the context they need to better understand and use your data.
• Semantic models make data available to both consumers and AI applications in a consistent manner, using the language of the business in lieu of technical jargon.

Meeting users where they are

dbt accomplishes all of this in three ways. The first is through improving data quality and trust. The second is by driving efficiency, which is critical for processing all that data that’s still sitting on-premises.

And third, and perhaps most important, is meeting users where they are. Your organization likely has people with varying levels of technical expertise, from seasoned SQL developers to business analysts who understand the data but may not be comfortable with complex coding environments.

dbt addresses this through multiple interfaces:

• A VS Code extension for your most technical team members
• A browser-based Studio IDE for those comfortable with SQL but new to local development environments
• dbt Canvas, a new low-code development environment for analysts who have business context but may need support with SQL complexity.

The result is that everyone can contribute safely and productively to the same projects, backed by the governance and safety features that enterprise organizations require.

Our recently launched dbt Fusion engine takes this further, delivering significant performance gains and intelligent cost optimization. Currently in public beta for Snowflake and Databricks, Fusion provides real-time feedback while writing code, providing a developer experience that’s orders of magnitude faster than anything before it. For organizations concerned about cloud costs, Fusion's intelligent optimization features help ensure you're not overspending on compute resources.

Migration strategy: beyond lift-and-shift

We’ve talked about the why. Now it’s time to talk about the how - i.e., migration.

The statistics are sobering: only one in seven data platform migrations succeeds. The reasons usually fall into two categories: organizations either attempt massive manual rewrites or rely on fully automated lift-and-shift approaches.

Manual rewrites are appealing in theory but problematic in practice. You'll spend years recreating functionality, learning from past mistakes the hard way, and trying to maintain business continuity while building everything from scratch. Even with AI assistance, the complexity and time requirements (typically years) make this approach risky and expensive.

Full automation seems like the obvious alternative, but most automated migration tools are essentially lift-and-shift solutions. They convert your legacy code to run on modern platforms without any insight into how to optimize it for cloud architectures. You end up with the same patterns designed for on-premises systems, often resulting in higher compute costs and missed opportunities for improvement.

As with most things in life, the truth often lies in the middle. The smart approach combines the best of both worlds.

This is what Flowline from Infinite Lambda does. Flowline combines an initial lift-and-shift with a set of baked-in best practices, including post-migration refactoring and data validation.

dbt integrates seamlessly with Flowline to provide best-in-class migration for porting from legacy systems onto a modern data architecture. Leveraging the best attributes of both systems, Flowline achieves over 95% automated code conversion from platforms like Informatica and Talend to native dbt code.

The technical process is thorough:

• First, deterministic code migration converts your existing ETL jobs to dbt models with established best practices built in.
• In the testing phase, every data output is compared between your legacy system and the new dbt implementation. Any differences are identified and explained, whether they're due to platform differences, data type changes, or even bug fixes in the legacy system.
• Finally, the refactoring phase optimizes your new dbt models for performance and cost efficiency. This may involve consolidating multiple legacy transformations into a single model, creating reusable macros, or restructuring data flows to leverage modern warehouse capabilities.

The results speak for themselves. Flowline’s approach delivers approximately 10 times the improvement over manual migration timelines, ensuring you end up with an AI-ready data stack rather than just a translated version of your legacy system. It does this at a fixed cost with a predictable timeline, eliminating the number one reason why data migrations tend to fail.

Real-world success stories

Consider Massif, a leading French insurance provider with over six million members. They were facing the classic legacy ETL challenges: pipelines taking hours to complete, massive Informatica license costs, and a planned two-year modernization effort that seemed overwhelming.

Using Flowline’s software-accelerated approach, what Massif thought would take two years was completed in just three months. More importantly, the results were dramatic: pipelines that previously took two hours now complete in five minutes, enabling their operations team to finish work during business hours rather than waiting for overnight batch processes. They reduced their ETL licensing costs from €1 million annually to €200,000—an 80% reduction that freed up budget for strategic initiatives.

AstraZeneca's journey illustrates the broader strategic value. As a global pharmaceutical company, they were dealing with increasingly complex legacy transformations across Informatica, Talend, and even SSIS.

These systems were not just expensive to maintain. They were actively slowing down their ability to develop new reports and dashboards, taking weeks for what should have been simple data products. More critically for a healthcare organization, their legacy infrastructure left no capacity for AI innovation.

After migrating to a modern data stack with dbt, they now have an AI-ready platform that supports generative AI use cases while delivering significantly faster development cycles and reduced costs. Perhaps most importantly, their teams now have the bandwidth to focus on advanced AI applications in healthcare rather than maintaining legacy infrastructure.

Getting started: Your migration roadmap

If any of this resonates with your current situation, your next step is assessment. Understanding your current state—the complexity of your existing ETL jobs, the volume of data you're processing, your performance requirements, and your strategic objectives—is crucial for planning a successful migration.

We recommend starting with Infinite Lambda’s comprehensive migration readiness assessment. This evaluation, which takes about five minutes to complete, will help you understand the key areas that need attention in any migration project. You'll receive a detailed migration guide that goes beyond high-level strategy to provide practical, actionable guidance for your specific situation.

The assessment covers critical factors like your current ETL complexity, data volumes, performance requirements, team skills, and strategic timeline. Based on your responses, you'll get targeted recommendations for addressing potential challenges before they become roadblocks.

If the assessment reveals that your migration needs are complex—and most enterprise migrations are—you don't have to tackle this alone. Professional migration services can provide the end-to-end support you need, from initial assessment through production deployment and team training. These services typically offer fixed-cost engagements that eliminate the budget uncertainty that has derailed so many migration projects.

The time to act is now

The AI era isn't coming—it's here. While you're wrestling with legacy ETL maintenance and escalating license costs, your competitors may already be leveraging modern data stacks to power AI initiatives that will define the next decade of competitive advantage.

Your legacy systems served you well in their time, but they're now actively limiting your potential. The migration path exists, the tools are proven, and organizations across industries have demonstrated that dramatic improvements in performance, cost, and capability are achievable.

Used together, Flowline and dbt rapidly accelerate your transition from legacy ETL systems such as Talend and Informatica. With Flowline, you’re ensured a consistent, high-quality migration in less time than a manual or automated lift-and-shift effort. The end result is a native dbt implementation - a data control plane that’s flexible and cross-platform, collaborative, and produces trustworthy outputs.

If you’re ready to see how to make this shift, without the pitfalls of manual rewrites or lift-and-shift dead ends, watch our on-demand webinar with Infinite Lambda. You'll learn how teams like yours are moving beyond Talend and Informatica to build an AI-ready data foundation with Flowline and dbt.