What’s data synchronization in modern systems?

From faster decision-making to more accurate insights, there’s a lot to gain from a single source of truth. But for most businesses, data rarely lives in one place. Instead, it stretches across apps, internal systems, and thousands of databases. 

Data synchronization helps you keep everything in order: It collects records, updates files, and makes sure data is consistent across all of your systems. With a strong data sync strategy, you’ll be powering analytics engines, feeding real-time apps, and giving teams confidence to act on data-driven insights.

Let’s break down what data synchronization is, how it works, and why it’s central to keeping data-first organizations flowing smoothly.

What’s data synchronization and why does it matter?

When data is updated in one place, the same record in other locations may need to be updated as well. If this update doesn’t happen, teams might be working off of different, mismatched data, leading to confusion and conflicting conclusions. By automatically propagating content changes to other systems as they happen, data synchronization keeps everything consistent.

A failed synchronization creates a domino effect — duplicates and mismatches pile up, systems break down, and conflicting information undermines trust. To keep confidence high in data-first strategies, your synchronization needs to work smoothly.

Types of data synchronization techniques

Keeping records consistent across your company takes an understanding of which options will work best for your team. Here are the main types of data synchronization techniques.

One-way synchronization

One-way synchronization is the process of moving data in a single direction from a source system to its destination. This option works best when one system creates records and another tool collects the data for reporting. With a single producer and a single recipient, one-way sync is fairly easy to implement.

Two-way synchronization

Two-way synchronization allows data to flow back and forth between two systems, pushing updates on either side of the channel. Most of the time, you’ll experience this type of data movement when users interact with a couple of systems needing to remain operationally consistent. 

That said, you’ll need to create some data-write rules — two-way syncs need to know which data takes priority if changes happen simultaneously. 

Multi-way synchronization

A multi-way synchronization extends a two-way sync to three or more systems. Data flows in multiple directions across every connected system, whether that’s other applications, databases, SaaS apps, or through reverse ETL. Multi-way sync is becoming much more common, especially as businesses continue to work remotely and rely on distributed cloud architecture.

Hybrid synchronization

The most complicated form of data sync is hybrid synchronization. This involves reconciling data across multiple environments (cloud platforms, data warehouses, data lakes, private clouds, etc.) and distributing it across infrastructure types.

Hybrid synchronization is harder to implement because it has to balance multiple data types, latencies, security requirements, and network boundaries. The more layers you add in, the more manpower needed to architect a workable solution.

But when done right, hybrid sync unifies several workloads under one data system. You’ll have complete visibility over your entire data ecosystem at a glance.

Data synchronization methods

While there’s many ways to keep data flowing properly, these main data synchronization methods help maintain accuracy across distinct environments:

• File synchronization: Files synchronization is an automatic process that analyzes files and then replicates any changes in other storage systems. This process occurs incrementally (where only the changed portion of a file updates) or fully (where an entire file is duplicated from the original to the copies). 
• Distributed file systems: A distributed file system (DFS) uses data replication to spread a file across connected nodes. Distributed architecture boosts accessibility and lets users access information through nodes that are physically closer to them, decreasing latency. 
• Version control: Version control tracks changes to files over time, with each iteration becoming a new variant. This approach ensures people are only working with the most recent information, while keeping a full history updated for teams to revert to if needed.
• Mirror computing: Mirror computing creates identical copies of data before distributing it across multiple systems. The main difference between mirror computing and DFS is the scope of data captured and duplicated. This is a resource-intensive form of data sync, best suited for copying mission-critical workloads. 

Benefits of synchronized data

When data stays consistent across systems, teams can trust that they’re working from the same set of facts. A reliable view improves accuracy, keeping reports and dashboards updated with the most current and complete information available.

Data synchronization also improves operational efficiency, saving you the time it would take to manually reconcile specifics. Any SaaS tool or data integration will stay aligned as data easily flows between them. 

Challenges of data synchronization

As your organization scales, data synchronization becomes more complicated. The more data, systems, or tools you add in, the more careful your planning needs to be. Without strong automation and monitoring, sync issues like duplicate records or overwritten values can quickly creep in.

Even after making sure your data pipelines are correct, you’ll need to consider security and performance. Since synchronization directly increases the volume of data migrating through your business, you may encounter more latency and touchpoint issues that need extra security controls. 

While data synchronization is worth it, you’ll have to find technologies that help you mitigate these challenges. 

Data synchronization tools and integration technologies

Several tools work together to make data synchronization a possibility, helping to align distributed systems:

• ETL/ELT tools: Extract data from source systems and load it into centralized databases, like cloud data warehouses or analytics platforms.
• Reverse ETL and data activation: Data activation moves modeled data from analytical environments back into operational systems.
• API integration platforms: APIs enable real-time communication between different applications. These integrate data from potentially thousands of individual sources.
• CDC-based pipelines: Change data capture (CDC) is a data integration technique where databases only replace data that has changed rather than entire files — minimizing the need for massive updates.
• Replication systems: Replication systems push updates from a central hub to all connected secondary nodes, which is used in DFS and mirror computing.

Use cases for real-time data synchronization and synced data workflows

Keeping your data fresh, current, and aligned across all systems is much more than a nice-to-have aspect of data management. The following workflows actively depend on proper synchronization: 

• Team synchronization: Ensures customer, revenue, and operational data remain consistent across different teams, reducing reporting issues and any misaligned decision-making.
• E-commerce inventory updates: When a customer places an order, data synchronization lets you track stock levels, update fulfillment systems, and avoid any inventory discrepancies.
• Multi-database syncing: Working across multiple databases or regions specifically requires synchronization to maintain accuracy despite data existing in several locations. 

How Fivetran supports data synchronization and real-time data sync pipelines 

Fivetran’s fully automated connectors continuously synchronize data from hundreds of sources into a cloud data warehouse or data lake, and from those destinations back into hundreds of operational systems. By using CDC, Fivetran performs near real-time updates with automatic schema drift — keeping pipelines running smoothly across your business. 

With a high degree of reliability and accuracy, Fivetran’s fully managed ELT pipelines let your business work from fresh data without the need for manual upkeep. 

Get started by requesting a demo today. 

FAQs

What are the best data synchronization tools available?

The best data synchronization tools depend on the reason you need them, but every use case can benefit from reliable ELT data pipelines to improve and automate data ingestion.

What is the purpose of data synchronization?

The main purpose of data synchronization is to keep all versions of data the same, maintaining consistent and up-to-date data across all of your systems.

What is the difference between data synchronization and data replication? 

Data replication is a strategy where you duplicate data from one location to another. Data synchronization refers to the broader systems that keep data aligned across systems. 

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