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The traditional divide between operational and analytical data is disappearing. For decades, businesses have maintained separate data ecosystems, operational systems for day-to-day transactions and analytical platforms for insights and reporting. But a fundamental shift is happening, blurring these boundaries and creating new possibilities for businesses to leverage their data more effectively.
In a recent podcast appearance on The Data Stack Show, Stacksync Co-founder and CEO Ruben Burdin shared his insights on this convergence and how it's reshaping the enterprise data landscape.
To understand the convergence, we first need to understand what separates these two data worlds.
"Operational data is really the data which is based on events, out of clicks, out of actions that the user takes and which need an answer right now. They are real-time and they are not batched—they're single events."
Operational data powers the moment-to-moment functioning of your business applications. When a user signs up on your platform, that data is written instantly to a database, allowing immediate access to your application. This real-time nature is essential—there's no acceptable delay between action and response.
"Analytical data makes sense at an aggregate level. For example, revenue metrics only make sense once you compute several deals together. A single deal doesn't make sense for a revenue metric."
Analytical data traditionally lives in warehouses where it's processed to reveal patterns, trends, and insights. Its value comes from looking at the bigger picture—not individual transactions but the collective story they tell.
The path to convergence has been decades in the making, with several key technological evolutions paving the way:
"This loop needs to be faster now. Streaming is not really equal to many small batches. Real-time infrastructure is completely different from batch infrastructure."
As Ruben explains, simply breaking batch processes into smaller, more frequent runs doesn't create true real-time infrastructure. The architecture needs to be fundamentally different to deliver authentic real-time performance without incurring unsustainable costs.
One of the most critical insights Ruben shared is that true two-way synchronization isn't just a matter of setting up two opposing one-way data flows:
"One-way sync plus the other way does not make two-way sync."
The reason is simple yet profound: when systems update in real-time from multiple directions, conflicts inevitably arise—the same record might be modified differently in two systems simultaneously. Traditional approaches lack sophisticated conflict resolution, creating data inconsistencies and business risks.
Two-way sync requires dedicated conflict resolution mechanisms that traditional ETL or reverse ETL tools simply don't provide. This is especially important in real-time contexts where there's no opportunity to manually consolidate changes before they propagate.
For businesses using Salesforce, developing custom functionality often means building Lightning apps—a process that requires specialized skills and locks you further into the Salesforce ecosystem.
"I'm thinking about building this in Salesforce and I'm feeling sick in my stomach already," joked one of the podcast hosts about Lightning apps.
Stacksync offers a liberating alternative: with two-way sync between Salesforce and your database, you can build the same functionality using familiar database tools. Your developers can work with standard technologies they already know, while changes propagate seamlessly back to Salesforce.
Another powerful pattern is using an intermediate database to reconcile different data models between systems:
"You would have, let's say, different tables. Your users table would not be synced with your Salesforce. Actually, you would have a Salesforce user table and an app user table."
This approach allows you to maintain separate but synchronized representations of data that match each system's unique requirements. Your application can continue to use its native data model while still keeping business data in sync with CRM systems.
Given the elegance of two-way sync, it's natural to wonder why the industry took the circuitous ETL/reverse ETL route rather than developing this solution sooner.
Ruben identifies several key factors:
"Maybe seven years ago, streaming use cases were only for elite companies."
"I remember when I was working on Snowflake six years ago, the concurrency limit was only seven queries... how do you want to do this real-time?"
"Two-way sync is a much more complex technology than ETL or reverse ETL would be."
The convergence we're seeing today simply wasn't possible until recent technological advancements created the foundation. Data warehouses needed to become more responsive, databases needed better change detection capabilities, and high-throughput streaming architectures needed to become more accessible.
Implementing real-time, two-way synchronization between operational and analytical systems delivers multiple benefits:
Stacksync's approach abstracts away the complexity that typically makes integration projects so challenging:
"We just want to be someone else. And so, so and so, and this is your alternative, right? It's like, okay, now you get stacking right, which publishes real time and to ascend between your Salesforce and your database."
The platform handles:
Most importantly, it creates a database-centric integration approach that feels natural to developers:
"Your engineers can build your internal portal exactly as you would build a normal app into a database. So very familiar for your engineers to build. Your engineers and your go-to market knows exactly how to behave because the database is a very comfortable zone for them."
The distinction between operational and analytical data is fading. Data warehouses are becoming more operational, while databases are increasingly supporting analytical workloads. This convergence represents a fundamental shift in how businesses leverage their data assets.
"What was traditionally analytical data and what was traditionally operational data actually, like, they really start to merge and that really meet the same level of SLA."
Stacksync is at the forefront of this transformation, providing real-time, two-way synchronization between enterprise systems like CRMs, ERPs, and databases. By abstracting away the complexities of integration, we empower teams to focus on innovation rather than "dirty plumbing."
Whether you're looking to build custom applications on top of CRM data, maintain consistency across multiple business systems, or simply free your engineering team from integration maintenance, Stacksync provides the infrastructure to make it happen, reliably, at scale, and in real-time.
Ready to experience the power of real-time, two-way sync for yourself?
Contact us today to schedule a demo and see how Stacksync can transform your data integration.
