erwin
FEBRUARY 13, 2020
When an organization’s data governance and metadata management programs work in harmony, then everything is easier. Data governance is a complex but critical practice. There’s always more data to handle, much of it unstructured; more data sources, like IoT, more points of integration, and more regulatory compliance requirements.
AWS Big Data
MARCH 7, 2023
Data lakes are more focused around storing and maintaining all the data in an organization in one place. And unlike data warehouses, which are primarily analytical stores, a data hub is a combination of all types of repositories—analytical, transactional, operational, reference, and data I/O services, along with governance processes.
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AWS Big Data
JANUARY 8, 2024
In the subsequent post in our series, we will explore the architectural patterns in building streaming pipelines for real-time BI dashboards, contact center agent, ledger data, personalized real-time recommendation, log analytics, IoT data, Change Data Capture, and real-time marketing data.
Jet Global
JULY 2, 2019
The sheer scale of data being captured by the modern enterprise has necessitated a monumental shift in how that data is stored. From the humble database through to data warehouses , data stores have grown both in scale and complexity to keep pace with the businesses they serve, and the data analysis now required to remain competitive.
Cloudera
MAY 12, 2021
This category is open to organizations that have tackled transformative business use cases by connecting multiple parts of the data lifecycle to enrich, report, serve, and predict. . DATA FOR ENTERPRISE AI. SECURITY AND GOVERNANCE LEADERSHIP.
Jet Global
JUNE 5, 2019
Toiling Away in the Data Mines. If data is the fuel driving opportunities for optimization, data mining is the engine—converting that raw fuel into forward motion for your business. Store and manage: Next, businesses store and manage the data in a multidimensional database system, such as OLAP or tabular cubes.
Cloudera
AUGUST 30, 2018
Complicating matters is the increasing focus on data protection and the far-reaching implications of IoT (e.g. How do I get to the next level in the data-driven journey fast enough? How do I meet a growing demand for self-serve BI, while not exploding my data warehouse budgets? Tough decisions. Complex scenarios.
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