With significant improvements in expandable hardware storage technology, the availability of high-end smartphones and computers that can easily amass several gigabytes of data, and, of course, the emergence of powerful cloud integration technologies, effective data storage and synchronization may seem like a given to everybody. But even at this level of advancement, organizing your business data and keeping it uniform across multiple platforms is easier said than done.
The process that you’ll need to complete is called data synchronization, or maintaining uniformity and consistency of all data instances across multiple applications and storage platforms. The end goal of synchronization is making sure that the same copy or version of the data is used from the data source to the data destination. Data synchronization is now commonly done through the use of powerful real-time data replication software. Such software can keep track of all data changes as they occur, thus ensuring proper synchronization with the original data source with minimal data latency.
Indeed, software solutions can cover much ground for data integration and consolidation, and you can do your own part in keeping the rest of your business data in sync. Among the things you should be paying attention to are how to sync up disparate data types and incongruent character set types, what method of change data capture you’re going to use, and how much data you’re planning to process. Additionally, you should also be taking human error into account when handling data. What do all these parameters mean for keeping your data warehouse or data lake organized and up-to-date? Let’s take a closer look.
What types of data are in your data set?
This is a fairly simple question, but one that must be answered thoroughly nonetheless. What types of information are being stored in your company’s database, and in what formats are they all saved? One simple example of how things can get hairy is when data type conversions for components like date and time are incompatible. What you will need to do moving forward is to ensure that all data types in your source and target are compatible, so that you won’t be losing either the data or the data value precision.
What character sets are you dealing with in your database?
Say for example, your business is growing a customer base abroad, and you’ll be gathering data that’s notated not only in single-byte Western characters, but also double-byte Japanese or Chinese characters. One action you should immediately take before this data is moved to the data lake is to standardize it into an all-encompassing character set like Unicode. That way, the character formats won’t be hard to process, no matter their origin or type.
What method of change data capture will you use?
Change data capture or CDC methodologies like the DATE_MODIFIED method, the diff method, database triggers, and log-based change data capture are used to identify changes in data. They are the foundation of real-time data replication and synchronization. You can use these methods in various scenarios, but log-based change data capture is superior because it can be used in a variety of applications, including in systems with very high transaction volumes. Moreover log-based CDC can track changes quickly and with minimal impact on the database because it reads straight from the logs without directly affecting transactions.
How much data are you planning to synchronize?
These days, the human civilization is producing such extremely huge volumes of data that we’re now generating more data in one year than in the last several thousand years. Even as a single company, your business could be producing so much data that you’re definitely going to need a powerful data replication tool just to be able to move this data fast and efficiently in complex environments.
How can you account for human error?
We’ve heard enough horror stories about hacking and security breaches, but in truth, many data losses occur just by human error and simple mistakes like accidental deletion of entire chunks of data. Though most databases also come with recovery options and safeguards, they can’t address each and every accident. And so, to protect your data further, you can go about the process of replicating your database via traditional means or through Big Data replication.
These are just some of the considerations we think you should take into account when consolidating your data. Keeping an organized database for your everyday business operations will then be a matter of “sync” or swim.