The Evolution of Data Integration
Data integration's scope and value have shifted dramatically. We now use conventional SaaS applications to supplement our business capabilities while continuing to develop unique solutions. The information about an organization's services that are exposed to clients is becoming as crucial as the services themselves, thanks to a strong ecosystem of partners willing to exploit an organization's information. Integrating SaaS, bespoke, and partner apps and the data they contain is now a need. A corporation nowadays differentiates itself by combining commercial talents in a novel way. Many firms, for example, are evaluating data in motion and, at rest, creating business rules based on their findings and then applying those rules to respond even faster to new data.
Data integration work
Trying to acquire and make sense of data that reflects the environment in which an organization function is one of the most challenging issues it faces. Organizations collect an increasing amount of data in various formats from many data sources every day. Employees, users, and consumers need the means to extract value from data in organizations. This means that businesses must be able to bring together relevant data from many sources to support reporting and business activities.
The physical data integration strategy is the classic data integration method. And that entails physically moving data from its source system to a staging area, where it is cleansed, mapped, and transformed before being physically transferred to a target system, such as a data warehouse or a data mart. The data virtualization strategy is another alternative. A virtualization layer is used to link to physical data stores. Unlike physical data integration, data virtualization entails the generation of virtualized views of the underlying physical environment without the requirement for data transportation.
Extract Transform and Load (ETL) is a typical data integration technique. Data is physically taken from many source systems, changed into a different format, and loaded into a centralized data storage.
Considerations for Improving Simple Integration
The cost of no longer needing to manually integrate data is the first benefit of deploying data integration technology. There are additional advantages, such as eliminating bespoke coding for the integration. Instead of writing their integration code, organizations should use an integration solution provided by a vendor whenever possible. Improved data quality, optimal performance, and time savings are all reasons.
Adding the following extra goals to integration maturity roadmaps could provide significantly more value to organizations:
Streamline Development
Choose a system that allows you to save and reuse a catalog of formats and sub-processes, particularly non-functional activities like logging and retries. The time it takes to implement and maintain any integration logic will be drastically reduced if you test it on the fly.
Configuration
To connect applications and systems, data integration processes are set up. These setups must quickly reflect any changes, guarantee that appropriate methods are used and disseminate changes across many environments (development, test, quality assurance, and production). Most companies still change configuration parameters manually in their integrated development environment (IDE), which is a time-consuming operation that may interfere with integration logic. Accessing and controlling variables in scripts or deployment interfaces, on the other hand, enables fully automated deployments that shorten project length.
Testing
Data integration development begins with testing. It verifies the data integration technology and target systems. Thus it should be done as soon as the logic is created or updated. Most firms, however, must establish processes before testing, which causes delays. Integration process development is greatly sped up using an IDE that allows instant troubleshooting. Furthermore, because certain data integration processes are crucial, they must be tested in contexts such as the production environment and updated to them. This testing necessitates the creation of test cases. This logic must be developed on top of the integration process logic and the probes to capture findings in many organizations. this increases the development time and cost. Project duration can be greatly decreased by using an API to inject data and record test cases or by using an integration testing solution.
Establish a Common Data Model
Building a consistent data model and restricting technology make future integrations easier because all integration procedures will speak the same language. Services and events involving business objects can be simply built, and subscribing to the right events increases business visibility.
Savings from Leveraging Past Investments
Many legacy apps are still essential to business processes and include critical data that must be connected with all other systems in your environment. Despite their basic business functions being valuable assets that may be reused in other services, many of their components and capabilities have now been supplanted by other apps. Data integration can assist you in bringing data from legacy systems into more current settings.
Data Virtualization
Data virtualization is a logical data layer that integrates enterprise data from disparate systems, regulates it for centralized security and governance, and makes it available in real-time to business users.
The Uses of Data Virtualization
- Compare current business performance to past years' results.
- Comply with requirements requiring previous data to be traceable.
- Look for and learn about related information.
- Replace legacy systems while modernizing business applications.
- Make the switch from on-premises to cloud-based apps.
- Deliver data as a service to monetize it.
How Data Virtualization Works
Data virtualization uses a simple three-step process to deliver a holistic view of the enterprise.
Connect To Any Data Source
Databases, data warehouses, cloud applications, big data repositories, and even Excel files.
Combine Any Type Of Data
Structured, streaming, social, and flat files.
Consume The Data In Any Mode
Reports, dashboards, portals, mobile, and Web applications.
Embedded Data Virtualization with CData Drivers
CData provides embedded data virtualization technologies that integrate data virtualization features into your applications and platforms without requiring you to write any code.
Our standards-based drivers integrate seamlessly with any application or tool, including popular analytics and reporting tools such as Power BI, Tableau, and Excel. With minimal time and effort, you can smoothly handle all of your fragmented data. This is a significant advantage over typical data integrations, months-long migration operations, or seven-figure logical data warehouse setups only to make your data available.
This is a significant advantage over retrieving whole sets of data from each data source before merging the data and using it in a BI tool, application, database, or platform.
We've built fully integrated connectors for over 250 SaaS applications, CRMs, ERPs, marketing tools, collaboration platforms, accounting systems, databases, file formats, and other APIs on-premises or in the cloud.
CData's embedded data virtualization technology improves data access and connection without causing any disruption to your current systems or applications.
FAQs
How can I sift through so much information to find what matters?
Data virtualization makes it easier to consume big data by providing a single virtual layer that integrates heterogeneous data sources. Data virtualization magnifies the enormous data pile, allowing you to focus on exactly the information that matters to you.
How can I ensure that business intelligence is available to the entire team?
Create an abstraction layer to keep things simple: data virtualization obscures the underlying complexity of data processing from the business user. This allows businesses to empower their business users by providing self-service access to the data they need.
What are the benefits of virtualizing data?
Data virtualization has numerous advantages, including:
- Reduce IT costs by requiring fewer hardware servers and other resources, decrease the administration and management time spent by automating the process and reduce your digital footprint by lowering energy consumption.
- Increases efficiency by putting your hardware and resources to good use.
- Real-time data access is provided 24 hours, seven days a week.
Why is data virtualization necessary for large businesses?
Large businesses require data virtualization for a variety of reasons, including:
- It allows users to access data more quickly and with little downtime.
- The lead time for designing and implementing data availability is considerably decreased
- Data redundancy is decreased
- There is more responsiveness to change
Conclusion
In this article, we have extensively discussed Data Virtualization Vs. Data Integration. We start with a brief introduction of Data Virtualisation and Data Integration, then discuss the various attributes of Data Virtualisation and Data Integration.
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