I recently wrote about the potential benefits of data mesh. As I noted, data mesh is not a product that can be acquired, or even a technical architecture that can be built. It’s an organizational and cultural approach to data ownership, access and governance. While the concept of data mesh is agnostic to the technology used to implement it, technology is clearly an enabler for data mesh. For many organizations, new technological investment and evolution will be required to facilitate adoption...
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Topics:
Analytics,
Business Intelligence,
Data Governance,
Data Integration,
Data,
data operations,
AI & Machine Learning,
data platforms,
Streaming Data & Events
I recently described the use cases driving interest in hybrid data processing capabilities that enable analysis of data in an operational data platform without impacting operational application performance or requiring data to be extracted to an external analytic data platform. Hybrid data processing functionality is becoming increasingly attractive to aid the development of intelligent applications infused with personalization and artificial intelligence-driven recommendations. These...
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Topics:
Analytics,
Business Intelligence,
Cloud Computing,
Data,
Digital Technology,
AI & Machine Learning,
data platforms,
Analytics & Data
The server is a key component of enterprise computing, providing the functional compute resources required to support software applications. Historically, the server was so fundamentally important that it – along with the processor, or processor core – was also a definitional unit by which software was measured, priced and sold. That changed with the advent of cloud-based service delivery and consumption models.
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Topics:
Data,
Digital Technology,
digital business,
data platforms,
Analytics & Data
Over a decade ago, I coined the term NewSQL to describe the new breed of horizontally scalable, relational database products. The term was adopted by a variety of vendors that sought to combine the transactional consistency of the relational database model with elastic, cloud-native scalability. Many of the early NewSQL vendors struggled to gain traction, however, and were either acquired or ceased operations before they could make an impact in the crowded operational data platforms market....
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Topics:
Business Continuity,
Cloud Computing,
Data,
Digital Technology,
digital business,
data platforms,
Analytics & Data
I recently wrote about the importance of data pipelines and the role they play in transporting data between the stages of data processing and analytics. Healthy data pipelines are necessary to ensure data is integrated and processed in the sequence required to generate business intelligence. The concept of the data pipeline is nothing new of course, but it is becoming increasingly important as organizations adapt data management processes to be more data driven.
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Topics:
business intelligence,
Analytics,
Data Governance,
Data Integration,
Data,
Digital Technology,
Digital transformation,
data lakes,
data operations,
AI & Machine Learning,
digital business,
data platforms,
Analytics & Data,
Streaming Data & Events
I recently described the growing level of interest in data mesh which provides an organizational and cultural approach to data ownership, access and governance that facilitates distributed data processing. As I stated in my Analyst Perspective, data mesh is not a product that can be acquired or even a technical architecture that can be built. Adopting the data mesh approach is dependent on people and process change to overcome traditional reliance on centralized ownership of data and...
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Topics:
Business Continuity,
business intelligence,
Analytics,
Data Governance,
Data Integration,
Data,
Digital Technology,
data lakes,
digital business,
data platforms,
Analytics & Data
Data mesh is the latest trend to grip the data and analytics sector. The term has been rapidly adopted by numerous vendors — as well as a growing number of organizations —as a means of embracing distributed data processing. Understanding and adopting data mesh remains a challenge, however. Data mesh is not a product that can be acquired, or even a technical architecture that can be built. It is an organizational and cultural approach to data ownership, access and governance. Adopting data mesh...
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Topics:
business intelligence,
Analytics,
Data Governance,
Data Integration,
Data,
Digital Technology,
Digital transformation,
data lakes,
data operations,
digital business,
data platforms,
Analytics & Data,
Streaming Data & Events
Despite widespread and increasing use of the cloud for data and analytics workloads, it has become clear in recent years that, for most organizations, a proportion of data-processing workloads will remain on-premises in centralized data centers or distributed-edge processing infrastructure. As we recently noted, as compute and storage are distributed across a hybrid and multi-cloud architecture, so, too, is the data it stores and relies upon. This presents challenges for organizations to...
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Topics:
business intelligence,
Analytics,
Data Governance,
Data,
data operations,
AI & Machine Learning,
data platforms
I recently examined how evolving functionality had fueled the adoption of NoSQL databases, recommending that organizations evaluate NoSQL databases when assessing options for data transformation and modernization efforts. This recommendation was based on the breadth and depth of functionality offered by NoSQL database providers today, which has expanded the range of use cases for which NoSQL databases are potentially viable. There remain a significant number of organizations that have not...
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Topics:
NoSQL,
Data,
data platforms,
Use Cases