ABSTRACT
Business agility is a must during these pandemic times. Business agility requires data-driven decisions. Data-driven decision making requires data agility and the modernization of data management to enable business-led analytics. The most common, successful, and scalable data management modernizations involve data virtualization, IPaaS, and data hub technologies to provide a data layer.
ABSTRACT
Digital transformation requires business and execution agility. Modern data management solutions frequently provide six types of optimizations. Push down optimizations, remote function execution and functional compensation enable full leveraging of the environment. Automation, such as auto ETL, no hard-code and the ability to leverage autoscaling without significant configuration, enables agility and simplifies even the most complex global hybrid environments.
ABSTRACT
Copy data management solutions are being added to architectures to address the demands of CCPA and GDPR. Copy data management is a capability available as a stand-alone data management tool, as well as provided within data virtualization platforms. Advantages of copy data management discussed in this research include the ability to stop orphan data copies, eliminate extra storage costs and keep copies up to date. Advantages discussed also include the ability for copies to be kept equal to each other, simplified governance and accelerated development.
ABSTRACT
Data virtualization platforms have numerous benefits to organizations that add it to their architecture. This research examines five quantifiable advantages enterprises that adopt data virtualization experience. Quantifiable advantages include user access to data, copy data management, centralized governance, increased agility, and reduced infrastructure costs. Data Virtualization platforms provide measurable advantages to the digitally transformed and significantly contribute to the return on investment (ROI) realized by the architecture.
ABSTRACT
When selecting technologies for your data architecture, it is important to understand common use cases enabled by the technology. This research examines six use cases enabled by data validation and the architecture capabilities used to support the use case. To this end, we examine the validation of ingestion and transformations, the data migration, as well as cloud update use cases. The use cases for production monitoring, completeness of data sets, the ability to compare BI tools’ values, as well as data DevOps are also evaluated.
ABSTRACT
Modern data virtualization platforms are increasingly becoming a critical part of data architectures for the digitally transformed. Many data virtualization platforms provide data as a service, data preparation, data catalog, logically centralized governance, the ability to join disparate data sets, and an extensive list of query performance optimizations. Likewise, data virtualization platforms are increasingly being used to support six different and critically important use cases. This research evaluates and ranks the various modern data virtualization platforms according to their architectural capabilities and ability to successfully meet popular use cases.
ABSTRACT
Digitally transformed organizations expect reliable, data-driven decisions, but data-driven decisions require reliable data if the business is to maintain decision integrity. Don’t stop short with only providing data quality that is responsible for standardizing data. All data integrations must be verified and transformations must be validated to ensure decision integrity. Historical methods of “stare and compare” or leveraging SQL scripts are not efficient, nor do they meet governance requirements. Some modern data validation tools can validate even the largest data sets.
ABSTRACT
When creating your data architecture, it is valuable to understand the extent of data engineering capabilities provided by modern data virtualization platforms. Many data virtualization platforms provide data as a service, data preparation, data catalog, logically centralized governance, the ability to join disparate data sets and an extensive list of query performance optimizations.
ABSTRACT
Digital transformation is a highly sought-after end-game for corporations. Scalable digital transformation cannot occur without the modernization of data management to enable business-led analytics. The most common, successful, and scalable data management modernizations involve data virtualization, IPaaS, and data hub technologies to provide a data layer.