8 FEATURES of Data Virtualisatie
in a modern data architecture
Instead of responding to past events and events, today’s data-driven organizations
are proactively working to optimize results.
according to ERIK FRANSEN
Erik Fransen & Antoine Stelma
Connected Data Group initiators are experts in the field of Business Intelligence, DataWarehousing, Big Data and Data Virtualization.
Connected Data Group offers Data & Analytics services for the digital transformation and is specialized in Agile Data Architecture and Data Management.
They blog about current topics and developments in the field. The intention of the blog is to share knowledge and engage with peers.
MODERN DATA ARCHITECTURE
Organizations that want to work data-driven are working to adapt their data architecture. New methods and techniques make it possible to mine the wealth of information that is present and thus optimize services and management.
In this blog Erik discusses a number of the key features and properties of a modern data architecture with a focus on data virtualization.
MODERN DATA ARCHITECTURE
The modern data architecture defines an organization’s set of methods, tools, techniques and processes for capturing, processing and delivering an (enormous) amount of data. This data needs to be analyzed more and more frequently in real time. The modern data architecture is able to quickly deliver the right data, in the right form, meaning and quality, based on the diversity of information needs.
Data needs to be available faster and in greater quantities, so Data Scientists, Data Analysts, and other Information consumers can create insights. The static architectures of the past, such as a data warehouse, are unable to support the large amount and diversity of data and information queries. A data warehouse can still be part of the complete data architecture in modern data architecture. A data architecture, where the combination of cloud and on-premise solutions, applications, and services form the total architecture plate. One important component for accessing data that should not be missing is Data Virtualization.
DATA VIRTUALIZATION AS A CORE COMPONENT
Data virtualization provides fast, cost-effective, and centralized access to and integration of all data sources that are important to an organization. It makes data available 24/7, secure, under governance and just-in-time to declining parties and systems.
Regardless of where the data view is located, data becomes available through a single logical virtual layer. In addition to data from traditional systems this includes data from IoT, SAAS, PAAS, and IAAS applications, as well as sensor data, external data, and big data solutions such as data lakes. This virtual layer makes data quickly, easily and consistently accessible and available to users.
Data is retrieved directly from the source and is no longer moved to other data layers as. usal. Moving data is a conscious choice when using Data Virtualization: data analysis, compliance, or performance may require moving data. Data virtualization uses available components such as data warehouses, data lakes, or cloud storage. Integration of source data with data warehouses, data lakes, or cloud storage is provided virtually via Data Virtualization.
8 FEATURES OF DATA VIRTUALIZATION
Data virtualization in a modern data architecture is characterized by:
- BUSINESS VALUE
Instant access to all data in the organization helps to develop actionable real-time insights and respond quickly. Users have more options at their disposal. This is because they can easily select virtual data. In addition, users can use their favorite analytics tools to visually view the data and make decisions. This reduces the time spent searching and integrating data. As a result, there is more time to analyze and create business value.
Data virtualization makes it possible to respond rapidly to new developments in many areas. This is up to 10 times faster than traditional data warehousing and ETL techniques. Data virtualization enables you to respond immediately to new data requests by providing integrated virtual data objects. This eliminates the need to copy data to different data layers, but only makes it available virtually.
- ACCELERATION TIME-TO-MARKET FROM SOURCE DATA TO FINAL PRODUCT
Creating virtual data objects, which incorporate integrated data, is much faster than existing ETL tools and databases. This helps customers access the data they need more quickly.
- REDUCE DATA & ANALYTICS COSTS AND INCREASE BUSINESS VALUE
Having the correct data available more quickly gives organizations the opportunity to make informed decisions faster. This saves costs are and increases the value for the business.
- CLEARLY COMBINE DATA FROM A DIVERSITY OF SOURCES
Distributed data from Data Warehouses, Big Data Platforms, Data lakes, Cloud Solutions and Machine Learning can easily be integrated into user-required data objects via the virtual data layer.
- CLEARLY COMBINE DATA FROM DIFFERENT “TIME ZONES”
Various data timelines such as real-time data, historical data, snapshot data and closed data are accessible to users via the virtual architecture layer. Depending on the demand, the correct data set, consisting of one or more of the mentioned time zones, is retrieved and made available ‘on-the-fly’.
- SUPPORT OF SELF-SERVICE BI, ANALYTICS AND DATA SCIENCE
Data virtualization provides the ability to quickly address new and additional data issues. Expanding existing virtual datasets or creating new virtual datasets is easy to realize.
- SECURITY IN ONE PLACE
Access to data becomes possible in one place in the modern data architecture. The virtual layer that provides access to all organizational data makes it possible to secure data down to row and column level. Using data masking, anonymizing and pseudonymization of data makes it possible to authorize multiple user groups on a single virtual dataset.
DATA VIRTUALIZATION AS A FIXED PART OF THE MODERN DATA ARCHITECTURE
Modern, future fixed data architectures contain data-centric solutions. Data routes determine which architectural components, tools, techniques and models are required. Data is accessible to users and systems through a single virtual data portal and is made available through Data Virtualization. Data virtualization makes it possible to make enterprise-wide data available via a single data portal, to set up data security and data to leave data in the source and only collect it when requested. It makes it possible to be data-agile and to make data objects available at high speed, so that data science, analytics and self-service reporting questions can be answered quickly.
WANT TO KNOW MORE?
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