Data virtualization mitigates the frequent risks inherent to data sync
- Data sync and data virtualization both have their place in integration.
- Data sync requires manual intervention and replication, which makes it error-prone with risks for data corruption and loss, among other problems.
- Virtualization is a logical data layer that integrates data spread across disparate systems without replication.
- Virtualization eliminates the need to rebuild business logic and delivers data in real-time.
- Virtualization leaves data in the source system, so each time it is retrieved, it is up-to-date
SAP data integration is accomplished via two different methods: data sync or data virtualization. There are pros and cons to each and use cases for each. But for consistent data accuracy in real-time, for most integrations, virtualization is the best choice.
Regardless of the method, data integration combines information from disparate sources into a single, unified view that can be analyzed. This provides actionable business intelligence, improves collaboration, boosts efficiency, and enriches the value of the data over time.
Let’s explore the differences between data synchronization and virtualization in integration and why many consider virtualization the way to go.
The data sync method of SAP integration
The data sync method of integration utilizes the ETL process: Extract, Transform, Load. To truly synchronize data, it must first be extracted from the source, then transferred, transformed — cleansed, standardized, de-duped, verified, and have use case and rules applied — and finally loaded into its new destination.
The potential for missed or incomplete steps is high with so much data handling. The most important step in this process is the data transformation step, which ensures data integrity and that all the data is compatible.
In basic terms, syncing data means pulling data from one system and pushing it to another system. Often, this is performed in a one-way flow (unidirectional), going to SAP as the system of record. When changes happen in SAP, updating a corresponding field in another system can be done in the background and run on a schedule.
Bi-directional syncing is where changes made on one system will update the other system. A real pitfall of this is the potential for duplicate files when you change the same data at the same time in both systems. Which is the system of record? Because you made a change in two different places before they synced, which system wins?
When it comes to SAP integrations, bi-directional sync is not the best idea. When you normally do a sync, you’re just loading in data — for example, an order — and placing it in the right tables in SAP. But just putting the data there doesn’t execute any of the surrounding business logic. This effectively confuses SAP because it has received this order, but none of the other processes around it got started, even though SAP thinks they were because it has the order.
Challenges of data sync integration
SAP integration via data sync comes with a number of challenges, many of which rest in the transformation process. These include:
- Data loss due to records being rejected by the target system
- Corrupt or irrelevant data
- Disparate data sources with different coded mapping that leads to different transformation needs
- Duplicate data
- Missing values due to delays in processing of updates or batch scheduling delays
- More costly to build and maintain due to duplicated data and data storage costs
- Making copies of the data makes it more vulnerable to hackers
- Data is always “stale” since it isn’t coming direct from the source system
- More susceptible to data errors
- Data governance compliance issues arise from data moving into and out of multiple systems
While data sync integration can collect large quantities of data from multiple sources and creates a single point of view that can provide business intelligence, it’s an integration method where the risks outweigh the benefits for most data. It can be a useful tool for integrating that changes infrequently and/or for reporting purposes, such as customer and product data, but is too risky to use at every integration point.
SAP Integration via virtualization
Data virtualization takes information from different sources, repositories, and formats and integrates it without replication by creating a virtual data layer. This delivers unified data in real-time.
How virtualization works
Virtualized data is displayed in the receiving system, but it is pulled in real-time from SAP right when the data is shown. For example, if you are looking at a screen that shows SAP data in Salesforce, that data was pulled milliseconds before it was shown to you. But that data isn’t stored in Salesforce once you move away from that screen. If you open the screen again, it will pull the data again, which is how it is kept up-to-date.
Benefits of data virtualization
Every company has data from a number of different sources. By using data virtualization, you avoid the pitfalls common with ETL while offering the following benefits:
- Data virtualization can bridge the semantic differences of unstructured and structured data, which makes integration easier and improves overall data quality.
- Provides an integrated view of the data while leaving it in the source system
- Retrieves data in real-time
- Making changes to data is faster as data does not need to be replicated
- No need to rebuild business logic
- Increased flexibility to change data sets or applications
- Allows for rapid integration and prototyping
- Reduced ongoing maintenance and change management
- Minimize storage costs
- Rapid iterations and prototyping can be done
- Data is kept in one secure place to minimize the attack surface area from hackers
- Data being queried is always “fresh” and accurate from the source
- Helps to meet compliance requirements as there are less copies of data and movement
In addition, integration using virtualization means acceleration of the data pipeline as well as less developer overhead.
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