All businesses need real-time data to make quick business decisions and serve customers faster. This data is scattered over the cloud, social media platforms, operational systems, and websites. Data virtualization helps remove data silos and expedite access to company data. It is a type of data integration technology that allows real-time access to data.
In this blog, we will learn about it and discuss its pros and cons with some use cases.
What is data virtualization?
Data virtualization is a method for handling data that involves adding a layer of extraction on the logical level. As a result, users can access and alter disparate data sets without worrying about technical details like the data’s original format or storage location.
Users can get to all of their data through a single interface. It eliminates the need to move large data blocks physically. Instead, it uses pointers to the real data. This makes it easier to store data and faster to get to it.
How to perform data virtualization
Data virtualization is a technique that allows you to access and manipulate data from various sources without physically moving or copying the data into a centralized repository. It provides a unified view of your data, making it easier to work with distributed and heterogeneous data sources. Here are the steps to perform and implement data virtualization:
1. Define your objectives:
Start by clearly defining your objectives and understanding why you need data virtualization. What problems are you trying to solve? What data sources do you want to virtualize? Knowing your goals will help you choose the right tools and approaches.
2. Choose the right tools:
Select a data virtualization tool or platform that fits your requirements. Various commercial and open-source options, such as Denodo, Informatica, TIBCO Data Virtualization, and Apache Drill, are available. Evaluate these tools based on factors like data source compatibility, scalability, performance, and ease of use.
3. Data source discovery:
Identify and catalog all your data sources. This includes databases, data warehouses, cloud storage, web services, APIs, and flat files. Understand the structure and schema of each source to determine how they can be integrated.
4. Data modeling and mapping:
Create a logical data model that represents the integrated view of your data. This model should define how data from different sources will be mapped to a common schema. Consider data types, relationships, and transformation logic during this process.
5. Data access and integration:
Use the data virtualization software to connect to your various data sources. Configure connections, define data source-specific transformations if necessary, and set up data access permissions.
6. Query and transformation:
Write SQL or other query languages supported by your data virtualization system to access and manipulate the virtualized data. You can seamlessly perform data transformations, filtering, aggregation, and join operations across different sources.
7. Performance optimization:
Monitor and optimize query performance. Data virtualization tools often provide query optimization features, caching, and indexing mechanisms to enhance performance. Tune your queries and caching strategies as needed.
8. Security and access control:
Implement security measures to protect your virtualized data. Define access control policies to ensure that only authorized users can access specific data sources and perform certain operations.
9. Testing and validation:
Thoroughly test your virtualization solution to ensure that it meets your objectives and provides accurate and reliable data. Validate query results against the original data sources to verify correctness.
10. Documentation and governance:
Document your data virtualization architecture, data models, and access policies. Establish governance practices to maintain data quality, security, and compliance over time.
11. Scaling and maintenance:
As your data landscape evolves, be prepared to scale your data virtualization solutions. Regularly update your data models and adapt to changes in data sources. Monitor system performance and make necessary adjustments.
12. Training and user adoption:
Train your users and data analysts on how to use the data virtualization platform effectively. Provide documentation and support to ensure they can make the most of the virtualized data.
13. Feedback and continuous improvement:
Collect feedback from users and stakeholders to identify areas for improvement. Continuously refine your data virtualization solution to address evolving business needs.
The pros and cons of data virtualization
Data virtualization offers faster, more flexible, and better business intelligence operations. Utilizing it comes with a few benefits as well as potential drawbacks. The following are a few pros of utilizing data virtualization:
Access in real-time
This enables real-time access to and manipulation of source data through the virtual or logical layer without physically relocating the data to a new location.
Cost-effective
Implementing data virtualization needs fewer resources and costs than constructing a separate consolidated store.
Improved data governance and security
Data virtualization platforms allow administrators to impose centralized data governance and security thanks to their single virtual data layer fabric. There is no need to relocate the material, and access levels may be controlled.
Reduced complexity
All of the data for the organization is made accessible via a single virtual layer, making it suitable for a wide range of users and applications.
The data virtualization layer can benefit a business but has several cons. Let’s discuss:
Time spent locating test results
Virtual databases can save a lot of time when setting up test environments, but much time is also needed in the process. QA engineers are usually busy gathering test enterprise data. A database with millions or billions of records will only shorten this time.
High network traffic costs
Complete production files are stored on the data server. However, they are compressed to conserve space. The number and intensity of these processes will generate substantial network traffic, with all possible costs.
Single failure point
A single point of failure frequently emerges from the virtualization server’s sole access point to all data sources. All operational systems risk losing their data feeds if the server goes down.
There is no batch data support
The integration method does not support transporting data in batches or in bulk, which may be necessary for various circumstances.
Data virtualization use cases
It involves adding a logical data layer between different sources of data and the people who use them. It has more than one use in the business world. Let’s explore some use cases:
1. Data integration
The most common application of virtualization is data integration tools and architecture. Big data, cloud information, and social media are just a few examples of businesses’ many types of data.
Data virtualization eliminates the need for users to understand the specifics of each data type’s storage location or format to access the data they require.
2. Rapid prototyping
The Logical Data Warehouse’s data virtualization component enables quick setup, iteration, and materialization to shift data to production as needed. The built-in engine analyses how prototype data is used and makes storage recommendations for production, such as automatic database indexing.
Companies must better use their data assets to make wiser decisions, delight customers, and overcome competition.
3. Uses in development operations
Teams mostly automate everything except data in the application development process to change how customers interact with apps. Data visualization makes it easy for these teams to connect, get to, and use data that is good enough for production.
It helps development teams eliminate data deployment and data management limitations and reduce the resources needed to compute and make copies of data for developers and testers.
4. Analytics on large data sets
Data virtualization is particularly well-suited to large data and analytics needs, which rely on various disparate data sources.
Email, social media, and mobile phone usage are just a few examples of where big data is collected that goes beyond the scope of a traditional database like Oracle. That’s why it works with such a wide range of methodologies.
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How does data virtualization work in different industries?
Data virtualization is a technology that provides a unified view of data from various sources without physically moving or replicating it. It works similarly across different industries, but its applications vary based on specific industry needs:
- Finance: In finance, data virtualization technology enables real-time access to diverse data sources like market feeds, customer records, and transaction histories. It helps in risk management, fraud detection, and compliance by providing a holistic view of financial data.
- Healthcare: In healthcare, data virtualization integrates patient records, lab results, and other healthcare data from multiple systems. This enhances patient care, enables better clinical decision-making, and simplifies compliance with healthcare regulations like HIPAA.
- Retail: In retail, data virtualization combines data from various sources like sales, inventory, and customer data to optimize inventory management, pricing strategies, and customer experience across online and brick-and-mortar stores.
- Manufacturing: In manufacturing, data virtualization connects data from machines, sensors, and supply chain systems to improve production efficiency, monitor equipment health, and ensure timely supply chain operations.
- Telecommunications: In telecommunications, data virtualization integrates data from network equipment, customer databases, and billing systems to improve network performance, customer service, and billing accuracy.
- Government: In the public sector, Iit facilitates data sharing between government agencies to enhance public data services, streamline administration, and support data-driven decision-making.
- Energy: In the energy sector, this brings together data from various sources, such as sensors, meters, and weather data, to optimize energy distribution, monitor infrastructure, and support energy conservation efforts.
- Insurance: In insurance, it merges data from policyholders, claims, and risk assessment systems to assess risk accurately, process claims efficiently, and offer personalized insurance products.
- E-commerce: In e-commerce, it helps consolidate data from multiple online platforms, enabling businesses to gain insights into customer behavior, improve product recommendations, and enhance the overall shopping experience.
- Education: In education, it connects data from student records, learning management systems, and assessment tools to improve educational outcomes, personalize learning, and assess institutional performance.
Conclusion
Data virtualization is excellent for working with data housed on separate platforms. It makes a good business plan when you require business-friendly and well-designed user data displays.
It helps you quickly get up-to-the-minute information and federate data from numerous sources. IT can swiftly deploy and repeat a new data set with it as customer requirements change.
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