Domino Data Lab

APRIL 3, 2019

That’s a lot of priorities – especially when you group together closely related items such as data lineage and metadata management which rank nearby. Also, while surveying the literature two key drivers stood out: Risk management is the thin-edge-of-the-wedge ?for Allows metadata repositories to share and exchange.

Data Governance 84

Data Governance Machine Learning Metadata Big Data 84

AWS Big Data

JANUARY 22, 2024

The majority of data produced by these accounts is used downstream for business intelligence (BI) purposes and in Amazon Athena , by hundreds of business users every day. Data as a product Treating data as a product entails three key components: the data itself, the metadata, and the associated code and infrastructure.

Data-driven 82

Data-driven Advertising Metadata Data Architecture 82

AWS Big Data

MARCH 1, 2024

One of the bank’s key challenges related to strict cybersecurity requirements is to implement field level encryption for personally identifiable information (PII), Payment Card Industry (PCI), and data that is classified as high privacy risk (HPR). End users access this data using third-party SQL clients and business intelligence tools.

Data Lake 95

Data Lake Data Warehouse Management Risk 95

Domino Data Lab

JULY 22, 2019

But the business logic kept getting more and more progressively rolled back into the middle layer, also called application servers, web servers, later being called middleware. Along with your database servers, you had, data warehousing and business intelligence. Those workflows would feedback into your business analytics.

Data Science 109

Data Science Machine Learning Data Governance Modeling 109

AWS Big Data

APRIL 28, 2023

This will include how to configure Okta, AWS Lake Formation , and a business intelligence tool to enable SAML-based federated use of Athena for an enterprise BI activity. There is a risk that two different groups could hash to the same character combination; however, we have checked that there are no collisions in the existing groups.

Data Governance 65

Data Governance Management Data-driven Data Lake 65

O'Reilly on Data

MAY 21, 2019

Here are some typical ways organizations begin using machine learning: Build upon existing analytics use cases: e.g., one can use existing data sources for business intelligence and analytics, and use them in an ML application. Metadata and artifacts needed for audits. Managing risk in machine learning”.

Machine Learning 238

Machine Learning Technology Deep Learning Data Science 238