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.
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. Nonetheless, the potential benefits of data platforms that span both on-premises and cloud resources remain. As I recently noted, many of the new operational database vendors have now adopted the term “distributed SQL” to describe their offerings. In addition to new terminology, a key trend that separates distributed SQL vendors from the NewSQL providers that preceded them is a greater focus on developers, laying the foundation for the next generation of applications that will depend on horizontally scalable, relational-database functionality. Yugabyte is a case in point.
I recently described how the operational data platforms sector is in a state of flux. There are multiple trends at play, including the increasing need for hybrid and multicloud data platforms, the evolution of NoSQL database functionality and applicable use-cases, and the drivers for hybrid data processing. The past decade has seen significant change in the emergence of new vendors, data models and architectures as well as new deployment and consumption approaches. As organizations adopted strategies to address these new options, a few things remained constant – one being the influence and importance of Oracle. The company’s database business continues to be a core focus of innovation, evolution and differentiation, even as it expanded its portfolio to address cloud applications and infrastructure.
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.
Topics: Analytics, Business Intelligence, Data Governance, Data Integration, Data, Digital Technology, Digital transformation, data lakes, AI and Machine Learning, data operations, Digital Business, data platforms, Analytics & Data, Streaming Data & Events
Data governance is an issue that impacts all organizations large and small, new and old, in every industry, and every region of the world. Data governance ensures that an organization’s data can be cataloged, trusted and protected, improving business processes to accelerate analytics initiatives and support compliance with regulatory requirements. Not all data governance initiatives will be driven by regulatory compliance; however, the risk of falling foul of privacy (and human rights) laws ensures that regulatory compliance influences data-processing requirements and all data governance projects. Multinational organizations must be cognizant of the wide variety of regional data security and privacy requirements, not least the European Union’s General Data Protection Regulation (GDPR). The GDPR became enforceable in 2018, protects the privacy of personal or professional data, and carries with it the threat of fines of up to 20 million euros ($22 million) or 4% of a company’s global revenue. Europe is not alone in regulating against the use of personally identifiable information (other similar regulations include The California Consumer Privacy Act) but Ventana Research’s Data Governance Benchmark Research illustrates that there are differing attitudes and approaches to data governance on either side of the Atlantic.