How can you integrate data from multiple sources in real-time using data virtualization?

1 What is data virtualization?

Data virtualization is a technique that creates a virtual layer between the data sources and the data consumers, such as applications, dashboards, or reports. Data virtualization does not store or replicate the data, but rather queries and transforms the data on demand, according to the business logic and user preferences. Data virtualization enables you to access and integrate data from multiple sources, such as databases, files, web services, or APIs, in real-time, regardless of their location, format, or structure.

2 Why use data virtualization?

Data virtualization offers many advantages for data integration and data visualization, such as reducing the time and cost of data integration, simplifying the data architecture, improving data quality and consistency, enhancing data security and governance, and enabling data agility and flexibility. Data virtualization eliminates the need to create and maintain a physical data warehouse or data lake, as well as the need to use multiple tools or scripts to extract, transform, and load (ETL) the data. Moreover, it allows you to apply data validation, cleansing, and enrichment rules at the virtual layer. It also enables you to control the data access and usage policies at the virtual layer. Finally, it allows you to easily add, remove, or modify the data sources and the data transformations without affecting the data consumers.

3 How to implement data virtualization?

To implement data virtualization, you need a data virtualization platform that can connect to your data sources, create and manage the virtual layer, and expose the virtual data to your data consumers. There are many platforms available in the market, such as Denodo, Teiid, or Red Hat Data Virtualization. Each platform has its own features and capabilities, but generally they involve connecting to your data sources with the appropriate drivers or connectors, defining virtual data models that represent the logical structure and relationships of your data sources, applying data transformations that specify how to query and manipulate the data sources to create the virtual data, and publishing the virtual data as views or services that can be accessed by your data consumers using standard protocols or interfaces such as SQL, OData, or REST.

4 How to visualize data virtualization?

To visualize data virtualization, you need a data visualization tool that can connect to your data virtualization platform and consume the virtual data. There are many options available, such as Tableau, Power BI, or Qlik Sense. Generally, these tools follow these steps: connecting to your platform using drivers or connectors, selecting the virtual data views or services to visualize, dragging and dropping the data fields to create charts, graphs, or maps, and customizing the appearance, layout, and interactivity of your data visualization.

5 What are the best practices for data virtualization and data visualization?

To make the most out of data virtualization and data visualization, it's important to plan your strategy and objectives before selecting the tools and platforms. You should also document your data sources, models, transformations, and views or services to ensure clarity and consistency. Additionally, you should optimize performance by using caching, indexing, partitioning, and parallel processing techniques. Moreover, monitoring and auditing your data virtualization usage and activity is essential for security and compliance. Finally, testing and validating the results of your data virtualization and visualization is key to ensuring accuracy and reliability.

6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?