IBM Big Data Hub
SEPTEMBER 19, 2023
How effectively and efficiently an organization can conduct data analytics is determined by its data strategy and data architecture , which allows an organization, its users and its applications to access different types of data regardless of where that data resides.
Jet Global
APRIL 2, 2019
Which problems do disparate data points speak to? And how can the data collected across multiple touchpoints, from retail locations to the supply chain to the factory be easily integrated? Enter data warehousing.
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IBM Big Data Hub
DECEMBER 16, 2022
It uses metadata and data management tools to organize all data assets within your organization. It synthesizes the information across your data ecosystem—from data lakes, data warehouses, and other data repositories—to empower authorized users to search for and access business-ready data for their projects and initiatives.
Jet Global
AUGUST 14, 2019
Which problems do disparate data points speak to? And how can the data collected across multiple touchpoints, from retail locations to the supply chain to the factory be easily integrated? Enter data warehousing.
Jet Global
OCTOBER 2, 2019
Which problems do disparate data points speak to? And how can the data collected across multiple touchpoints, from retail locations to the supply chain to the factory be easily integrated? Enter data warehousing.
CIO Business Intelligence
NOVEMBER 4, 2022
Our vision for the data lake is that we want to be able to connect every group, from our genetic center through manufacturing through clinical safety and early research. That’s hard to do when you have 30 years of data.” At the data pipeline level, scientists use Apigee, Airflow, NiFi, and Kafka.
O'Reilly on Data
OCTOBER 19, 2021
Data is at the core of any ML project, so data infrastructure is a foundational concern. ML use cases rarely dictate the master data management solution, so the ML stack needs to integrate with existing data warehouses.
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