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Data science vs. machine learning: What’s the difference?

IBM Big Data Hub

Data from various sources, collected in different forms, require data entry and compilation. That can be made easier today with virtual data warehouses that have a centralized platform where data from different sources can be stored. One challenge in applying data science is to identify pertinent business issues.

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And the winners are…. Congratulations to the Sixth Annual Data Impact Awards winners

Cloudera

AbbVie, one of the world’s largest global research and development pharmaceutical companies, established a big data platform to provide end-to-end operations visibility, agility, and responsiveness. Modern Data Warehousing: Barclays (nominated together with BlueData ). IQVIA is re-envisioning healthcare using a data-driven approach.

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Themes and Conferences per Pacoid, Episode 8

Domino Data Lab

Fun fact : I co-founded an e-commerce company (realistically, a mail-order catalog hosted online) in December 1992 using one of those internetworking applications called Gopher , which was vaguely popular at the time. Most of the data management moved to back-end servers, e.g., databases. We keep feeding the monster data.

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The DataOps Vendor Landscape, 2021

DataKitchen

RightData – A self-service suite of applications that help you achieve Data Quality Assurance, Data Integrity Audit and Continuous Data Quality Control with automated validation and reconciliation capabilities. QuerySurge – Continuously detect data issues in your delivery pipelines. Production Monitoring Only.

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Themes and Conferences per Pacoid, Episode 11

Domino Data Lab

Scale the problem to handle complex data structures. Part of the back-end processing needs deep learning (graph embedding) while other parts make use of reinforcement learning. Some may ask: “Can’t we all just go back to the glory days of business intelligence, OLAP, and enterprise data warehouses?”

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