DataKitchen

NOVEMBER 4, 2021

Data organizations often have a mix of centralized and decentralized activity. DataOps concerns itself with the complex flow of data across teams, data centers and organizational boundaries. It expands beyond tools and data architecture and views the data organization from the perspective of its processes and workflows.

Data Processing 246

Data Processing Data Lake Cost-Benefit Testing 246

Cloudera

JUNE 18, 2022

Cloudera customers run some of the biggest data lakes on earth. These lakes power mission critical large scale data analytics, business intelligence (BI), and machine learning use cases, including enterprise data warehouses. On data warehouses and data lakes.

Data Lake 90

Data Lake Data Warehouse Machine Learning Cost-Benefit 90

DataKitchen

JANUARY 3, 2022

There’s a recent trend toward people creating data lake or data warehouse patterns and calling it data enablement or a data hub. DataOps expands upon this approach by focusing on the processes and workflows that create data enablement and business analytics.

Business Analytics 245

Business Analytics Analytics Testing Dashboards 245

Cloudera

JANUARY 13, 2021

Engaging employees in a digital journey is something Cloudera applauds, as being truly data-driven often requires a shift in the mindset of an entire organisation. Putting data at the heart of the organisation. The platform is built on a data lake that centralises data in UOB business units across the organisation.

Digital Transformation 56

Digital Transformation Data-driven Data Lake Big Data 56

DataKitchen

NOVEMBER 29, 2021

Data Gets Meshier. 2022 will bring further momentum behind modular enterprise architectures like data mesh. The data mesh addresses the problems characteristic of large, complex, monolithic data architectures by dividing the system into discrete domains managed by smaller, cross-functional teams.

Testing 245

Testing Data Lake Data Architecture Manufacturing 245

Ontotext

AUGUST 4, 2023

A data fabric utilizes an integrated data layer over existing, discoverable, and inferenced metadata assets to support the design, deployment, and utilization of data across enterprises, including hybrid and multi-cloud platforms. Data fabric does not replace data warehouses, data lakes, or data lakehouses.

Metadata 52

Metadata Data-driven Data Architecture Data Quality 52