From Manual to Automated: Migrating Legacy Systems with Databricks

By Daniel Nguyen, Software Engineer at Groove Technology

I’ve been working as a software engineer for a while now, and I’ve learned that some of the most rewarding challenges come from modernizing old, clunky systems that are well past their expiration date. Recently, I had the chance to work on a project that required migrating a legacy system—one that involved manual data transfers, constant monitoring, and the constant fear of data loss. Honestly, it was a mess. But the messier the challenge, the bigger the potential reward. This is where Databricks came in, and it transformed not just the system, but the entire process for the client.

In this blog, I want to take you through that journey—from manual to automated, from outdated to modern. More than just explaining what we did, I’ll share why we made the choices we did, the technical hurdles we faced, and the strategies that ultimately helped us succeed. If you’ve ever had to deal with a legacy system and wondered how to breathe new life into it, I hope this story provides some useful insights.

01. The Legacy System: A Data Migration Nightmare

When I first took a look at the client’s existing system, it was clear we were going to have to rebuild a lot of things. To put it bluntly, it was a nightmare. Here’s what I was dealing with:

1.1 Time-Consuming Manual Processes

Every data transfer was a manual process. The system had multiple milestones, and each one had to be triggered by someone sitting there, initiating the transfer, and then waiting for hours—yes, hours—for it to finish. On average, each data transfer took 4 to 6 hours to complete, and this was happening multiple times a day. In a week, this amounted to 20 to 30 hours just on data transfers.

1.2 Data Loss Issues

Because everything was manual, errors were inevitable. And in this case, those errors came in the form of data loss—up to 300 records lost per day. Imagine spending hours moving data only to find out that a large chunk of it had gone missing. The team was constantly playing catch-up, trying to recover lost data and fix problems that shouldn’t have existed in the first place.

1.3 No Scalability

To make things worse, the system wasn’t built to handle increasing amounts of data. The client’s data volume was growing by 15-20% annually, and the system just couldn’t keep up. Every time there was a spike in data, the system slowed down or failed completely, leading to more delays, more lost data, and more headaches.

02. Why Databricks? The Journey to Automation

After analyzing the pain points, I knew we needed something that could automate these processes, handle large datasets, and scale with the client’s growing needs. That’s when I suggested Databricks. But I’ll be honest, Databricks wasn’t a tool I had worked extensively with before, so this meant spending a couple of months getting up to speed on how to best use it for this project.

What convinced me to dive into Databricks was its combination of automation, scalability, and integration with Apache Spark. Plus, it fit perfectly with the client’s existing cloud infrastructure, which made the transition smoother.

Learning Databricks:

It took a couple of months for me to really get a handle on Databricks. I immersed myself in its capabilities, ran tests, and set up a demo for the client. Once they saw how we could eliminate their manual processes and reduce the hours spent babysitting data transfers, they were fully on board.

03. The Solution: Automating Data Migration with Databricks

3.1 Automating the ETL Process

The first and most important thing we did was to automate the ETL (Extract, Transform, Load) process using Databricks. The goal was to eliminate the manual triggers and let the system handle the data migration autonomously.

Here’s how we did it:

Extract: We used Databricks Notebooks to automatically pull data from the client’s legacy system. Instead of manual extraction, we set up automated scripts to fetch data in parallel, taking advantage of Apache Spark’s distributed computing to handle large batches of data at once.

Transform: Once the data was pulled, we processed it using Spark SQL to ensure it was formatted and ready for the new system. This step involved filtering, joining, and aggregating data.

Load: Finally, we used Delta Lake (a Databricks feature) to load the data into the new system. Delta Lake was key to maintaining data integrity, ensuring that no records were lost in the process.

Here’s a simplified version of the script we used for automating this pipeline:

Using Delta Lake ensured that our data was protected with ACID transactions, which made a world of difference when it came to avoiding the data loss issues the client was struggling with.

3.2 Real-Time Data Monitoring

Another huge improvement was the ability to monitor data in real-time. In the old system, they would only find out about data issues hours (or sometimes days) after the fact. With Databricks, we set up Structured Streaming, which allowed us to monitor data as it was being processed and alert us to any issues immediately.

This is how we set up the streaming process:

With real-time streaming, we could catch data issues as they occurred and fix them before they caused bigger problems. This level of visibility completely transformed how the client managed their data.

3.3 Scaling as Data Grows

One of the things that really impressed the client was the scalability of Databricks. Their old system was crumbling under the weight of their growing data, but Databricks’ ability to auto-scale meant that we could handle data spikes without a hitch.

Scaling with Apache Spark:

Databricks uses Apache Spark’s auto-scaling capabilities to dynamically adjust resources based on the workload. During heavy data processing, the system automatically scaled up the resources to meet the demand, and then scaled back down when the load decreased. This not only saved time but also cut down on unnecessary cloud costs.

This was critical in helping the client deal with their millions of records, ensuring no slowdowns or performance issues.

04. Challenges Along the Way

Of course, no project is without its challenges. Here are the two biggest hurdles we faced during this migration:

4.1 The Learning Curve

As I mentioned earlier, Databricks is incredibly powerful, but with great power comes a steep learning curve. It took me two months of research, testing, and troubleshooting to get a solid handle on how to implement it for this specific case.

Solution:

We invested time in internal training to ensure everyone involved was up to speed. It was a challenging process, but in the end, the investment paid off as we were able to leverage Databricks’ full capabilities.

4.2 Managing Costs

Running a cloud-based system like Databricks can get expensive if you’re not careful. Processing large datasets continuously requires significant computing resources, and those costs can add up quickly.

Solution:

We used Databricks’ cost management tools to monitor resource usage and implemented strategies to optimize our pipelines. For example, we set up automatic shutdowns for idle clusters and only scaled up resources when absolutely necessary.

This helped us stay within budget while still delivering the performance the client needed.

05. Best Practices for Databricks-Based Migrations

Here are some of the key lessons I learned during this project:

5.1 Start Small, Scale Later

Begin by automating small chunks of the data transfer process, and test your pipelines before scaling up. This gives you a chance to catch issues early and fix them before they become more significant problems.

5.2 Automate Everything

The more you automate, the less room there is for human error. Use Databricks to automate as much of the ETL process as possible, from extraction to transformation and loading.

5.3 Monitor Resource Usage

Keep an eye on resource usage to avoid unnecessary costs. Use autoscaling wisely and implement automatic shutdowns for idle resources.

06. Final Thoughts: The Power of Automation

Migrating a legacy system is always a tough task, but with Databricks, we turned a slow, error-prone manual process into an efficient, automated system that could handle the client’s growing data needs. For me, this project was a lesson in how the right tools, combined with a commitment to learning and problem-solving, can make a massive difference.

If you’re dealing with a legacy system and want to learn more about how Databricks can help, feel free to reach out. I’d be happy to share more insights from this project.

Daniel Nguyen is a Software Engineer at Groove Technology, specializing in optimizing data workflows and automating legacy systems for modern, scalable solutions.