From Informatica to Databricks: How I’m Evolving as a Data Engineer

As a data engineer, I’ve had the opportunity to work extensively with traditional ETL tools, while simultaneously developing a strong interest in cloud-native platforms that are redefining the future of data processing and analytics.

My career began with classic enterprise tools like Informatica PowerCenter, Oracle databases, Putty, and IBM Tivoli Work Scheduler. These tools were reliable and battle-tested — they taught me the discipline of data modeling, ETL design, job orchestration, and maintaining large-scale workflows. But as the data landscape evolved, so did the need for more agility, scalability, and integration.

That’s what led me to pursue and complete the Databricks Data Engineer Associate certification — and today, I’m actively preparing to take that knowledge into production use. This article is about that journey: from the structured world of traditional ETL to the unified, cloud-first approach offered by platforms like Databricks.

🔧 Traditional ETL with Informatica: Reliable, but Fragmented

Let me walk you through what a typical ETL process looked like in my earlier projects:

1. Source Data Handling

The data I worked with came from a variety of sources — flat files dropped into network folders, or live tables and views from Oracle databases. Each source came with its own schema quirks, formats, and load windows.

2. Mapping and Transformation

All transformation logic was built using Informatica PowerCenter. Using its visual interface, I created mappings to apply business logic and designed workflows to control execution. This required defining sources, targets, and a variety of reusable components like mapplets, lookups, and transformations. It was structured and modular — but not always flexible.

3. Job Scheduling

To run these workflows at the right time (and in the right order), we relied on IBM Tivoli Work Scheduler. It handled dependencies, retries, and batch triggers. But it was a separate layer, meaning ETL orchestration was decoupled from transformation logic — which increased operational overhead.

4. File Management

When working with flat files, I’d often use Putty to check file arrivals, rename files, or trigger workflows. This added another manual layer of scripting and monitoring to an already complex process.

5. Monitoring and Debugging

Troubleshooting a failed load meant checking logs in PowerCenter, verifying dependencies in Tivoli, and sometimes accessing source/target systems directly. The process worked — but it was time-consuming and reactive.

In short, the traditional stack was solid but siloed. It got the job done, but scalability, agility, and visibility across the pipeline were real challenges.

🚀 Databricks: A Unified Approach to Data Engineering

When I began exploring Databricks, the shift in approach was immediately clear. Instead of stitching together multiple tools, everything I needed was available in one integrated platform.

From data ingestion and transformation to orchestration, visualization, and even machine learning — Databricks brings it all together in a single workspace.

Here’s what stood out to me:

• Delta Lake brings data reliability, ACID transactions, and versioning to cloud storage — something that traditional file systems can’t easily offer.
• Autoloader allows schema-aware, incremental ingestion from cloud storage, without writing complex shell scripts.
• Notebooks combine PySpark or SQL code with documentation, visuals, and output — making development faster and collaboration easier.
• Job orchestration is native and integrated — you can schedule notebooks, set alerts, configure retries, and monitor executions, all in one place.

More importantly, the cloud-native architecture allows jobs to scale dynamically, based on data volume — without manual tuning.

🔄 Can Informatica and Databricks Work Together?

Yes — and this is an important message for companies modernizing their stack.

If your organization currently relies on Informatica for ETL and is planning to adopt Databricks, you don’t need to abandon your existing workflows overnight. Integration is possible, and migration can be strategic and incremental.

Here’s how organizations are bridging the gap:

• Call Databricks notebooks from Informatica: You can invoke a Databricks notebook (or job) using REST APIs directly from Informatica workflows. This allows you to integrate cloud-based transformations into your existing ETL flow.
• Use Informatica Cloud (IICS): Informatica’s cloud offerings now support connectors for Databricks. This makes it easier to write to or read from Delta Lake as part of your ETL process.
• Hybrid pipelines: Let Informatica continue extracting and loading data, while offloading the transformation logic to Databricks. This distributes workload and prepares your team for a full transition.
• Staged migration: Teams can begin by rewriting one pipeline at a time in Databricks, testing performance and stability before deprecating the original workflows.

This phased approach ensures business continuity while allowing organizations to adopt a modern data platform without disruption.

💡 The Real Shift: From ETL Maintenance to Data Engineering Impact

With traditional ETL, a large part of my time went into ensuring the system ran smoothly — tracking file arrivals, checking logs, managing retries, and resolving failures across multiple systems.

With Databricks, the focus shifts to engineering solutions: designing efficient pipelines, building reusable logic, collaborating across teams, and enabling downstream analytics or machine learning use cases.

It’s a move from maintaining complexity to delivering value.

📌 Traditional vs. Modern Mindset

Here’s how I view the two approaches today:

• Informatica and traditional ETL provide structure, control, and reliability — but they require multiple tools and often lead to process-heavy development cycles.
• Databricks and modern cloud platforms promote flexibility, rapid iteration, scalability, and end-to-end integration — allowing teams to focus on outcomes, not overhead.

It’s not about which is “better.” It’s about choosing tools that match the speed, scale, and complexity of today’s data needs.

🎓 My Current Focus and Next Step

While I haven’t yet implemented Databricks in a live production environment, I’ve invested time in learning and applying the platform’s core concepts through the Databricks Data Engineer Associate certification, which I’ve completed successfully.

At the same time, I’m actively working on Google Cloud Platform in my current role — gaining practical experience in cloud-based data engineering, including storage, querying, and scalable architecture design.

My goal now is to combine my strong foundation in traditional ETL, my growing cloud engineering experience, and my certification-backed Databricks knowledge to contribute meaningfully to modern data projects.

🤝 Open to Opportunities

I’m currently looking for opportunities where I can:

• Apply my Databricks training in real-world data pipelines
• Contribute to building modern, scalable, cloud-based data platforms
• Leverage my understanding of both structured ETL design and agile, cloud-native execution

If your team is using Databricks or exploring the transition from traditional ETL to cloud-native data platforms — I’d love to connect.

Let’s build data solutions that are not only reliable, but truly built for the future.

💬 Thank you for reading. I’m always open to connecting with fellow data professionals, sharing ideas, and collaborating on meaningful projects.

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