What is data synchronization?

Data synchronization is crucial for maintaining optimal data quality within enterprise applications, with use cases ranging from mobile device syncing to complex enterprise database management.

Digital environments are increasingly distributed; they comprise various servers, applications and network components spread across countries and continents. And simultaneously, both consumers and businesses are increasingly reliant on cloud-based and cloud-native applications.

Together, these trends mean sprawling, dynamic, multimodal IT ecosystems that generate massive quantities of data (from diverse sources, in a range of formats) that must be parsed and processed. Data records also change frequently in modern IT environments.

To keep systems running effectively, development teams must make sure that all applications in the infrastructure have access to and work with accurate uniform data.

This is where data synchronization tools enter the picture.

Data synchronization services automate data reconciliation processes so every network component is working with accurate, up-to-date data records at all times and the entire network works runs efficiently for IT teams and users. Without data synchronization tools, teams would have to propagate record changes through the ecosystem by using tedious manual data entry.

Syncing software helps make sure that enterprise applications, systems and networks run on the latest data, helping businesses better leverage the wealth of data that modern architectures produce.

Data synchronization involves a range of data management methods, tools and techniques, but most approaches fall into a few broad categories based on the “direction” and timing of data updates.

One-way synchronization—also called unidirectional synchronization—updates a target system based on changes to a source system. Data is copied from the source location to target locations and changes flow from source to target without flowing back to the source.

One-way syncing is often used for data backup and distribution tasks, such as syncing local files to cloud storage and copying content from origin servers to edge servers in a content delivery network (CDN).

Though it’s often considered a type of syncing, one-way syncing isn’t true synchronization, because it doesn’t modify the source system at all.

With two-way synchronization, changes made in either the source or target dataset are propagated to the other component. Data flows in both directions, allowing changes in one system to be reflected in the other, regardless of which component initiated the sync. 

Two-way syncing—also called bidirectional synchronization—requires systems to continuously monitor each other for changes and reconcile differences (often employing conflict resolution processes to address data discrepancies).

Two-way sync is commonly used in environments where data can be modified from multiple sources, making it well-suited for syncing tasks in collaborative applications (syncing calendars or contacts across devices, for example).

Multi-way synchronization enables multiple systems to function as sources of truth, enabling updates from any system. Any system on the network can write changes and propagate them to the others and multiple source systems can make updates simultaneously.

Multi-way syncing is often deployed in distributed environments to efficiently synchronize data across global applications. Because multi-way sync enables users to synchronize data in multiple locations within the same data file, it’s useful for syncing files in cloud-based storage platforms (Dropbox, for instance).   

Hybrid synchronization seamlessly reconciles data across sources—including data lakes and warehouses—in hybrid computing environments. Syncing data in hybrid architectures is especially complex, because they combine on-premises data centers with public and private cloud data and an array of data platforms.

SQL data synchronization serves as one example. SQL data sync enables teams to edit data bidirectionally across cloud and on-premises sync groups (the cluster of databases that are chosen for synchronization in a particular data transfer or exchange). It relies on hub-and-spoke synchronization dynamics—where one database serves as a hub and propagates data changes to member databases—to keep hybrid applications running optimally.

Also called synchronous data updates, real-time synchronization reconciles data updates instantaneously (as they occur in the origin system) so that users across the network have access to the most up-to-date information. IT teams can use a web-based or local file transfer method—or an extract, transform, load (ETL) tool—to manage the data transmission process.

Real-time synchronization is frequently used to make updates to time-sensitive services, such as video conferencing tools, online banking platforms and live data feeds (stock trading tools, for example).  

Batch synchronization, or asynchronous data updates, involves collecting changes over a period of time and then applying them all at once. Updates occur at regular, predefined intervals, such as nightly or hourly, minimizing the impact on system resources during peak usage times. In some cases, IT personnel manually trigger system updates based on specific system events.

Because updates don’t occur in real time, batch synchronization is best for tasks that either don’t require time sensitivity (database backups, for instance) or where real-time updates aren’t feasible (as in systems with sporadic network connectivity).

Most leading data synchronization tools can accommodate several syncing dynamics. Push-based synchronization, for example, requires the source system to proactively send data modifications to target systems when a change occurs. Pull-based syncing requires the target system to make the synchronization request and “pull” data from the source. In an event-based syncing environment, changes appear as events in an event stream and multiple systems can ingest data updates simultaneously (but independently).

And with change data capture (CDC), a dynamic software design pattern, syncing tools track all changes to databases and data warehouses and enable users to “capture” and apply the changes downstream.

Data synchronization tools rely on several ongoing processes and systems to maintain data accuracy and network efficiency across environments. Key processes include:

File synchronization makes sure that all instances of a file are updated when changes to the authoritative file occur. Instead of the user manually identifying modified files and copying them one by one, synchronization software analyzes the files and performs the necessary updates automatically.

Take CDNs, as one example of file synchronization mechanisms. CDNs are used to distribute and cache content libraries across a geographically dispersed server network, enabling local servers to handle data requests that use local file copies. This wouldn’t be possible without file synchronization services continuously copying files from the origin server to edge servers.

File synchronization relies on two types of file transfers to maintain consistent data across different systems.

Full file transfers copy entire files from one location to another. It’s an effective process, but it can overuse network resources in situations where only parts of a file need regular updates.

Incremental file transfers address this issue by updating only the modified portions of a file.

File syncing services are useful for updating data on portable devices such as flash drives and external hard disks.

A distributed file system (DFS) spreads its storage infrastructure across multiple nodes, file servers and locations, but uses a single, unified namespace and authoritative copies of data files to maintain data harmonization.

Each node of a DFS typically hosts a segment of the entire file system, with files divided and distributed among the nodes. Users can access files and directories as if they were stored on a single system, regardless of the physical location of the data.

Distributed file systems often rely on data replication, where files or file segments are duplicated and stored across multiple nodes to safeguard redundancy. If one node or storage server fails, the data remains accessible through duplicates.

Notably, file synchronization in a DFS can only occur between systems with the appropriate network privileges and between systems that are actively connected to the network.

DFSs are especially useful for sharing and syncing read-only files (product catalogs, for instance).

Version control is a data syncing method where multiple contributors to work on a set of files or documents while tracking changes and preserving a history of revisions. This approach helps synchronization tools accommodate data files that require simultaneous updates by multiple users. Each user can make edits independently without disrupting another user’s work.

Version control systems (VCSs) aim to maintain a single current version of a file. When a user commits their changes to a central repository, the VCS integrates them and distributes updates to all the other users concurrently

Files are typically checked out and locked during updates and checked back in when updates are complete. File locking functions prevent the data conflicts that can arise when multiple users attempt to edit files locally before any one user’s changes reach the origin server. Because VCSs maintain a comprehensive history of revisions, users can access, review and restore previous versions when necessary.

Database synchronization copies data back and forth between databases and other data components with tabular structures. To accelerate the syncing process, each network database is assigned a primary key that identifies a single row of the database.

Database syncing involves four major processes.

Insert synchronization copies database records from a source database to target databases by matching primary key values. If the syncing tool notices data changes to the source database, it will add the missing rows to target databases.

Drop synchronization, the opposite of insert syncing, removes data records from target databases if those records are removed from the source.

With update synchronization, changes to the source database must propagate to target databases. Syncing tools replace outdated rows in the target database with sync data from the source, so every network database is identical.

Mixed synchronization uses a combination of insert, drop and update syncing to automate the database synchronization process.

Data mirroring, also called as mirror computing, creates identical copies (mirrors) of data and stores them on separate storage devices, across multiple systems in different locations. Any modifications to the primary system are immediately replicated to the secondary systems that hold the mirrored copies.

Depending on the specific implementation and requirements, data changes can be replicated instantly or with minimal delay, ensuring current, identical files throughout the network.

The terms data synchronization, data replication and data integration are sometimes used interchangeably. While these processes are related, they are distinct and each process serves a specific role in data and IT service management.

Data synchronization is the process of maintaining data consistency across systems or devices by using real-time and scheduled data updates.

Data replication is the process of copying data from a source location to target locations around the network. It’s essential for achieving high data availability in distributed networks, where it supports load balancing and disaster recovery protocols. If the primary data store is unavailable for any reason, the system can use the replicas as backups to make sure that users get the data they need without added latency.   

Data replication supports many data syncing functions, including mirror computing and DFS maintenance. 

Data integration, also often a component of data synchronization, combines data from various sources into a single, unified system to make network data more accessible to users and applications. It also focuses on standardizing data with different data formats and from disparate sources for broader system compatibility.

Both data replication and data integration can be useful—and are often essential—for data synchronization tasks. However, both processes also have a range of uses cases and applications beyond data synchronization.

Data syncing tools and solutions help automate synchronization processes so that IT personnel can focus on higher-level tasks. However, maximizing the benefits of data synchronization solutions can require a more tailored approach.

Here are a few ways that businesses can optimize data synchronization software:

To create custom integrations, the development team uses custom code to build a new synchronization solution from scratch, enabling clients to tailor the solution to their organizational and infrastructural needs. 

Custom integrations require a significant investment of time, effort and expertise from the engineering team; however, they also give businesses complete control over the data synchronization process without relying on third-party software.

Native integrations apply an app’s pre-built integration and data flows to another application. They directly connect applications through application programming interfaces (APIs)—software intermediaries that enable data to flow seamlessly between software components.

Native integrations can be more cost-effective than other data synchronization solutions, because they don’t require any custom coding. However, they don’t offer the same flexibility as a custom solution, so they might not be a perfect match for every organization’s needs.

iPaaS is a suite of self-service, cloud-based tools and solutions that integrate data from multiple applications hosted in different IT environments. iPaaS integrates applications at the API level and automates workflows and data pipelines, so changes to an app’s user interface don’t disrupt data synchronization.

Without proper data validation, conflict resolution and error handling protocols, iPaaS integrations can quickly become overwhelming, especially when working with large datasets that require frequent updates. However, iPaaS solutions typically offer various pre-built application connectors and automation templates that enable teams to implement high-performance data syncs without developer involvement.

Robotic process automation (RPA) software uses bots to copy and paste data between applications at the interface level, creating a quick, temporary solution for data synchronization.

RPA tools require extensive maintenance to make sure the bots are always working with accurate data, but they can be deployed quickly for specific, short-term tasks, such as removing customer data from one system and adding it to another. They’re most useful in situations where no other integration options are available or when teams need a temporary fix.

Using data synchronization tools improves data constancy across systems, even in distributed IT environments. They also offer businesses:

Without synchronization tools, employees would have to manually sync data across platforms and services. Manual data entry is a tedious, time-consuming process that takes IT personnel away from higher-level tasks. It also increases the likelihood of human error, which can create data discrepancies and network errors down the line.

With data syncing software, all data handling processes are automated, helping businesses minimize data loss, streamline data management and take advantage of accurate, lightning-fast synchronizations.

Unsynchronized data can create data silos, which negatively impact worker productivity. In siloed environments, employees often must submit data requests, wait for the request to be approved and then wait for the data transmission.

Data synchronization eliminates this problem by ensuring that all available data copies are identical and that every user has a unified view of network data, all without distracting, time-consuming data requests.

When every member of an IT department is working with identical, up-to-date data, they can communicate and complete tasks more effectively. Synchronized data also helps IT teams tackle issues, challenges and improvements collectively, so error handling becomes a group effort and innovation gets faster and easier.

In many instances, data changes propagate through the network continuously and in real time (or near real time). Immediate, ongoing data updates mean more accurate analyses. And accurate analyses help teams get robust, data-driven, actionable insights. Data insights facilitate a deeper understanding of network dynamics and enable businesses to optimize customer support and decision-making protocols.

Data synchronization helps teams seamlessly add new data sources and components to the network, ensuring data consistency and accuracy as the network expands. As organizations scale up, data synchronizations help computing networks scale with them.