Organizations are constantly collecting, analyzing, and storing data daily, and the cloud has become a conduit for that unprecedented data supply. Hence, the need for data consistency, accuracy, and privacy. Unfortunately, things that may look like minor errors or glitches can significantly negatively impact decision-making, sales, customer retention, and other daily operations.
Sorting through stored data is hard enough without syncing it with existing databases and parsing it out regularly while maintaining data integrity. That’s why data synchronization is now one of the most valuable tools organizations use to manage data.
The process assures accurate, secure, and updated data with improved teamwork and customer experiences. Once organizations synchronize everything, they get cleaned, improved, and updated data with no inconsistencies, errors, duplications, and other bugs.
Imagine listening to a jazz concert where the musicians and instruments are not synchronized. You end up listening to disparate sounds that don’t make sense or entertain. Similarly, clocks also need synchronization to prevent chaos because we rely on them to run and coordinate all aspects of our lives.
These same principles apply in the business world. An organization needs its departments, goals, employees, and software applications synchronized to operate and grow. However, while all companies know the essence of aligning goals and departments, many often overlook the importance of synchronizing their data.
This guide discusses everything to do with data synchronization, implementing it, and why it’s important.
It’s the process organizations use to consolidate data across different and disparate sources and software applications to ensure the data within those systems is consistent. It’s a continuous process that applies to new and existing data.
The sheer quantity of data the cloud stores and affords presents challenges to organizations. However, it also provides a solution for big data. Current data solutions offer easy and quick tools to bypass monotonous tasks and create data harmony throughout the system.
Synchronization ensures accurate, compliant, and secure data with a successful team and customer experience. Additionally, it assures congruence between data sources and different endpoints. So as data comes in, there are tools to clean it while others check it for errors, duplication, and consistency before putting it to use or storing it.
Remote synchronization occurs over a mobile network, while local synchronization involves computers, devices, and systems next to each other. An efficient system ensures all organizational data is consistent throughout the data record. Therefore, the changes must upgrade and reflect through every system in real-time if any modifications occur. It prevents mistakes and privacy breaches and ensures the availability of up-to-date data.
Finally, synchronization requires two things to happen:
Database synchronization establishes data consistency between databases and automatically copies changes back and forth. Data harmonization over time occurs continuously, and the most trivial case is pulling data from the source database to the destination. It means changes made to the source (master) database should apply to the target database.
Each table should have a primary key in database sync to identify one row alone. It significantly simplifies the process of data maintenance while speeding up synchronization.
Below are the different types of database synchronization:
The different ways to synchronize data include manual database updates, python scripts triggered by source database changes, and fully automated data pipelines using ETL. In all the instances, the process follows the following steps:
The data sync process detects a change made to the data on a target database using several ways, such as setting a flag within the table or a script that regularly checks the last modified file date.
Since synchronization does not mean full replication, the process only needs to identify instances where changes are made by comparing versions, checking changelogs, or looking for flags indicating new values.
The sync process schedules the movement of data after identifying and extracting changes using one of two ways:
The data transfer process might occur through a web or file transfer process. When synchronization uses ETL platforms, it processes automatic background updates without manual intervention.
When two data instances are not identical, the incoming data passes through a transformation layer that includes cleansing and harmonization.
The sync process writes incoming changes to the target data using one of several ways, including:
The goal is to update each data instance without any loss.
The updated system confirms the updates’ success using one of several ways. For example, if the application programming interface (API) handles the update, it will return a message confirming its success. Failure to send this confirmation message will see the process either attempt to restart the update or return an error message.
There are several data synchronization methods available, as discussed below:
File synchronization and version control tools can change several file copies at a time, while DFS and mirror tools have more specific uses.
Below are the definitions and differences between synchronization, integration, replication, and data pushes:
Organizations collect and handle data through numerous applications and software programs, with some running operations with over 100 software tools. As a result, employees view the same data set across different applications. For example:
The result is a lot of information coming in from disparate sources, making it easy for databases to become disorganized and disjointed if they don’t talk to one another.
Having the same data appear across different applications is essential for individual teams. Still, without cohesion and synchronization, manually re-entering updated data in apps leaves employees overwhelmed and prone to errors leading to further discrepancies.
When data is not in sync, it leads to many adverse effects, such as:
These problems above are why poor data quality and management costs organizations millions of dollars annually.
Synchronized data allows organizations to get a crystal-clear view of every aspect of the business, communicate transparently, and produce actionable and reliable reports. It also enables the alignment of departments towards common goals, teamwork, and making informed decisions.
The essence of data synchronization grows with increased access to cloud-based data and mobile devices. Mobile devices have permeated all organizations, leading to many new problems and solutions. These devices use data for their basic operations and personal information for websites, email, and apps.
Therefore, updates to the information users generate and the end target must be constant and secure. In addition, the synchronization process requires clean, consistent, and updated data for product and service competence and data governance issues such as security and regulatory compliance.
Conflicting data can result in low data quality and errors, leading to a lack of trust down the line. Proper implementation of data synchronization across the system ensures the organization sees an improvement in performance in many areas, such as:
Furthermore, data availability and timely error resolution save time and emphasize critical business development processes like new product development, strategic decision making, and marketing. Everyone benefits from synced data:
All in all, data synchronization ensures organizations operate smoothly and can scale.
Data sync is helpful in numerous situations, including the following:
Synchronization helps maintain consistency between two or more data sources. So updates in one source are mirrored on all the others. For example, customer addresses might appear in several places and applications on a database, such as the CRM, billing system, customer’s e-commerce account, and order fulfillment system.
So if the customer changes their address in their e-commerce account, the change should reflect in all other systems using a synchronization process.
Synchronization is essential in cloud computing and distributed systems because data can exist in several places. It ensures users can always access the most recent data versions and guarantees their updates are saved.
For example, when using cloud services such as DropBox or OneDrive, users can create documents on one device, save them in the cloud, and open them on another application, web browser, or device. The cloud server reflects and stores any changes they make and forces an update on all the connected devices to replace older versions with the latest copies.
Synchronization also helps with hybrid integration where data is stored on-premises and in cloud services such as Microsoft Azure, AWS, or Google Cloud Platforms. Processes like AWS data synchronization or Azure data sync handle data enrichment, filtration, transformation, and aggregation before transferring and storing it, and vice versa. This occurs in real-time while maintaining data accuracy and consistency and without interrupting business operations.
Data replication is used when storing data in repositories like data warehouses. However, updating the data requires real-time synchronization. For example, during a disaster recovery scenario, an organization will need an up-to-date data snapshot, so if it regularly syncs its backups, it will avoid substantial data loss.
Synchronization can include significant changes, such as amending the structure of a relational database. Therefore, the process can add and drop tables and rename columns. For example, when GDPR introduced the requirement to ask users about cookie preferences, affected organizations had to introduce a new database column and sometimes an entirely new table to store the added information. These changes must reflect across the network to all database instances.
Other synchronization use cases include:
Below are the benefits of synchronizing data:
While data synchronization is not rocket science, maintaining healthy, up-to-date data across cloud and on-premises systems is challenging. Below are some of these challenges:
There are many types of data synchronization solutions available. They include:
Veritas provides NetBackup data synchronization through SyncNetBackupData. It calls in the API whenever an asset gets flagged for synchronization. The System Update then picks up the marked asset. The process imports the images and protection before recalculating traffic light status.
By default, it processes batches of 100 assets in five minutes or until there are no more assets marked for importing. Additionally, it prioritizes assets added first unless a Backup Now request marks specific assets as a high priority.
If a sync fails, the system locks it for some time to process other assets and prevent a backlog.
There are plenty of choices for data synchronization solutions, so organizations need a clear strategy that answers the following questions:
Sometimes organizations get applications with native integration tools that solve their operational challenges. For example, NetBackup provides the safest, easiest, and more intuitive way to synchronize data. Otherwise, they may need one or more iPaaS solutions that work for them.