As big data moves to satisfy more on-time data delivery, IT teams across the enterprise must assure that data served up from applications that access big data, and transactional data must be accurate. Recently a customer had given a complete account of the shopping experience he faced; the user was specifically shopping for two cloured doors. During an online search for the doors, it was shown in stock. However, when checked physically, the user was told that doors haven’t been in stock for quite some time.  The store even confirmed to the user that the door had been discontinued for quite some time making the user question the store and online backend technology. Many users around the world have faced similar problems of stockout or unavailable product. Stockouts have been one common occurrence in a complete vertical of production and distribution as the industry, and the vendors are dealing with the complicated problem of maintaining the synchronization from several different locations based systems. It includes different applications and programs, but one major factor that has been contributing is Big Data. When the data is flowing in from several of the systems, and as most of them are not adequately synched with real-world functions, customers are bound to get answers completely unfulfilled. What makes the data synchronization an imperative aspect of real-life functions is decision making, whether it’s leaders, the management, or customers all need real-time data.

What is data synchronization?

The Wikipedia definition of Data Synchronization is “The process of establishing consistency among data from source to target data storage and vice versa and continuous harmonization of the data over time.”

Data synchronization is technically a subject of debate wherein real-time data demands increase for effective management. It’s also a major problem that impacts the big data due to many sources of data that are constantly being updated from millions of different data points. Data moves at a breakneck pace in big data making it a problem for real-time solution providers to depend on physically check many of those data sources. They all must be synched for absolute accuracy to create a more refined version of solutions. For example, consider you are a seller of customized cars across the world; each country will have different requirements making each part differs from the other. There will be a whirlwind of data from different sources. A production system that reports how many parts have been consumed in end item manufacturing, sales systems that report what is available to be sold, and engineering teams with systems with unsecured CAD big data that report on all the current revision level for products. If all the systems aren’t synched to reflect the up to date that doesn’ accurately show all the products, their delivery and if the product is currently available, a non-sync can deliver breakdowns that can disappoint the customers, salesperson, and can lead to management making inaccurate decision regarding production. IT teams are responsible when it comes to developing a solution that makes data sync with every new process flow giving customers better experience and management control over decision making.

Here are five quintessential functions that IT teams should look to solve in the process of Big data synchronization

1. Consider the technology requirement of mobility and what is downloads?

Sales associate are always on the run, and they need the flexibility to deal with different devices, locations, and data. Many of them are using mobile devices to access many of such data and facing increased limitations in data bandwidth; the devices won’t be able to process an extensive set of data. Usually, a sales rep will give an extensive overview of the product, but as the user buys the products, the sales team makes an entry in the data. However, the real-time data update in such cases might lag, but such a process needs to be buffered and synched.  

2. Plan your data update process

Every time the IT team plans or modifies an application and admits a new big data source in IT reporting business, the requirement should include all the needed requirements for planning, how will the syncing process happens with all different types of fresh and incoming data take place accurately. The planning should include the frequency of when you perform data updates and synchronization to master datasets.

3. Step by step architecture of data synchronization

Most sites already have data synchronization policies and update procedures for launching the critical transactional data, but still, haven’t addressed the required solution for big data. There are immense sets of data from different sources, and with an extreme velocity of data delivery with big data. As the data is entered from different sources, the fresh data should be more comprehensive, showing the complete availability of data. Not all types of data can be updated in real time, so the IT team has to decide critical data that needs to be synced with master data. Will the data synchronization happens after the set time frame- hour wise, in burst or day wise. The processes should be documented in the IT operations guide, and they will be updated every time you add new big data information source to your processing.

4. A required tool for data synchronization

There are several commercial tools that are available that can assist with data synchronization. These tools can assist you with big data synchronization efforts and automate of your synchronization operations.

5. Providers to assist synchronization

AWS EMR one of the big data cloud processors recognize the data synchronization issues and have various effective techniques that enable to perform a better synchronization. Many enterprises are using various cloud services when it comes to executing various real-time handling of data, so even different cloud vendors can assist you in performing your synchronization.

Conclusion

The number of legacy applications developed to support the enterprise’s operation has grown over time significantly. Operation in such situations has developed data that were stored disparately making sync a question, however, over time organizations realized that data could effectively manage many of the required functions by bringing many disparate databases into a common repository of knowledge for a newly merged corporation.

To know more, you can download the latest whitepapers on big data.