What is data virtualization?
For decades, companies have tried to break down silos by copying data from different operational systems into central data stores for analysis, such as data marts, data warehouses and data lakes. This is costly and prone to error. Most struggle to manage an average of 33 unique data sources, which are diverse in structure and type and are often trapped in data silos that are hard to find and access.
With data virtualization, you can query data across many systems without having to copy and replicate data, which reduces costs. It also can simplify your analytics and make them more up to date and accurate because you’re querying the latest data at its source.
Why IBM Cloud Pak for Data?
Access current data
Get always-current analytics across distributed data sources, with no need to store data outside your data center. Experience a single data repository where your SQL applications can connect and run.
Unprecedented speed
Leverage networked devices for polynomial processing gains. Automatically self-organize your data nodes into a collaborative network for computational efficiency. Define constellations with large or small data sources.
Security and privacy
Data isn’t cached in the cloud or on other devices. Credentials for your private databases are stored encrypted at the local device and are private to that device.
Flexibility
IBM Cloud™ Pak for Data supports multiple application query languages (SQL, stored procedure languages, R and Python) and data sources like Cloudera Impala, IBM Db2®, Db2 Event Store, IBM Informix®, Oracle, PostgreSQL, Microsoft SQL Server and Teradata.
Ease of use
Take advantage of a single web console with an interactive interface to query data, manage users and visualize data-node constellations. System optimization is automated through machine learning and adaptive algorithms.
Watch data virtualization in action
Learn from IBM and Intel how data virtualization accelerates innovation in AI, eliminates data silos and makes data available to the business for actionable insights.
Industry uses of data virtualization
Compliance analysis at financial branch locations
For financial institutions, quickly finding and stopping non-compliant transactions can have a positive impact on their bottom line. With data virtualization, institutions don’t have to move their data to a central data center or to cloud for processing and analysis. Querying microdata centers in financial institution branches enable analytics to happen in real time.
Mobile data thinning
How can a company quickly find which ad is having the most impact, while eliminating the noise happening around it? Data virtualization and edge analytics enable companies to better understand how to thin big data and process and analyze only the information necessary to the query, saving cost and time.
Retail customer behavior analysis
Brick-and-mortar stores are looking for any competitive advantage they can get over web-based retailers. Data virtualization enables near-instant edge analytics, providing unprecedented insights into consumer behavior. This helps retailers better target merchandise, sales and promotions, and do more to provide exceptional customer experiences.
IoT sensor data monitoring and analysis
IoT sensors are creating massive amounts of data. With the number of sensors collecting data growing, data volume is set to explode. Moving data analytics to the edge with a data platform that can analyze batch and streaming data speeds up and simplifies analytics, simultaneously — providing insights where and when needed.
Increasing manufacturing efficiencies
Automated manufacturing environments prioritize alarms by augmenting their quality and process techniques with meta-learning or rules. With data virtualization and machine-learning methods, manufacturers can increasingly sift through patterns of alarms and convert them into actionable information.
Remote monitoring and analysis for oil and gas operations
Data virtualization and edge computing can achieve reliable operations for the manufacturing industry. Having near real-time analytics performed at the site where data is being generated can help organizations identify issues promptly and, in so doing, prevent unexpected operational outages and interruptions.