Data silos represent a major business challenge, as noted by 60.9% of organizations in a recent Trends in Data Management survey. Without shared information, companies risk duplication, poor data quality, and missed opportunities for innovation. Consequently, many companies turn to modern and integrated data architectures. When doing so, organizations often consider two main approaches: data mesh and data fabric. Data […]
Data Architecture Best Practices
The term “data architecture” refers to a collection of implementable standards and protocols that govern the collection, storage, preparation, sharing, and distribution of data. This predefined set of standards and protocols is designed to enhance the scope and purpose of data analysis in the busy business environment. One major problem with traditional data management systems […]
Implementing Data Fabric: 7 Key Steps
Understanding the importance of data integration is vital in the intricate process of implementing a data fabric. Data fabric architecture aims to create a unified and integrated environment for managing an organization’s data sprawl across various platforms and systems. Within this context, the data integration step ensures disparate data sources are seamlessly connected, enabling a fluid exchange […]
Demystifying Metadata Management for Tech and Business
Metadata is of critical interest in today’s data-driven business environment, and there is a growing awareness of the need for both business and technical users to embrace it, says Donna Burbank, Managing Director of Global Data Strategy and author of “Data Modeling for the Business” and “Data Modeling Made Simple.” Speaking at a previous Data Architecture Online conference, Burbank talked about practical […]
Cloud Computing vs. Data Security
Cloud computing has, in recent years, become both an essential service used in many industries and a ubiquitous part of the daily lives of consumers. By offering remote access to computing services that can be rented out on a flexible, efficient, as-needed basis, it gives companies access to greater computer power and storage capabilities than […]
The Role of AI in Developing Green Data Centers
In an era where sustainability is the focus, green data centers stand as pillars of strength behind the mass protest against environmental degradation. These facilities, designed for efficiency and a minimal ecological footprint, reduce energy consumption and minimize greenhouse gas emissions. By harnessing renewable energy sources, advanced cooling technologies, and optimized server utilization, green data centers […]
Understanding the Modern Data Stack
The modern data stack is a collection of tools used to collect, store, and analyze data. Understanding the components of a modern data stack is crucial in grasping how contemporary data ecosystems function. At its core, data engineering plays a pivotal role by focusing on the practical application of data collection, storage, and retrieval. This discipline ensures […]
Automated Data Management Tools
“Why?” a chief technology officer (CTO) may ask when the subject of automated Data Management tools arises. After all, their organization has probably already been storing, archiving, and backing up enterprise data day after day with success. For example, setting up a Database-as-a-Service (DBaaS) from a reputable cloud computing provider, with appropriate access to data, […]
Types of Data Integrity
Over time, different types of data integrity systems and methods for promoting data integrity have been developed. Data integrity emphasizes confirming the data remains unchanged and consistent over the data’s entire lifecycle. In essence, the data remains pure and uncorrupted. Security plays an important role in ensuring the data is not altered and maintains its […]
Data Lakehouse Architecture 101
A data lakehouse, in the simplest terms, combines the best functionalities of a data lake and a data warehouse. It offers a unified platform for seamlessly integrating both structured and unstructured data, providing businesses agility, scalability, and flexibility in their data analytics processes. Unlike traditional data warehouses that rely on rigid schemas for organizing and […]