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The Role Of Data Warehousing In Your Business Intelligence Architecture

datapine

BI architecture has emerged to meet those requirements, with data warehousing as the backbone of these processes. One of the BI architecture components is data warehousing. What Is Data Warehousing And Business Intelligence? BI Architecture Framework In Modern Business. Data integration. Data storage.

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Run Apache XTable in AWS Lambda for background conversion of open table formats

AWS Big Data

This post was co-written with Dipankar Mazumdar, Staff Data Engineering Advocate with AWS Partner OneHouse. Data architecture has evolved significantly to handle growing data volumes and diverse workloads. In practice, OTFs are used in a broad range of analytical workloads, from business intelligence to machine learning.

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What is a business intelligence analyst? A key role for data-driven decisions

CIO Business Intelligence

Business intelligence (BI) analysts transform data into insights that drive business value. What does a business intelligence analyst do? The role is becoming increasingly important as organizations move to capitalize on the volumes of data they collect through business intelligence strategies.

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How Eightfold AI implemented metadata security in a multi-tenant data analytics environment with Amazon Redshift

AWS Big Data

As part of the Talent Intelligence Platform Eightfold also exposes a data hub where each customer can access their Amazon Redshift-based data warehouse and perform ad hoc queries as well as schedule queries for reporting and data export.

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How EUROGATE established a data mesh architecture using Amazon DataZone

AWS Big Data

In this post, we show you how EUROGATE uses AWS services, including Amazon DataZone , to make data discoverable by data consumers across different business units so that they can innovate faster. From here, the metadata is published to Amazon DataZone by using AWS Glue Data Catalog.

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When is data too clean to be useful for enterprise AI?

CIO Business Intelligence

Once the province of the data warehouse team, data management has increasingly become a C-suite priority, with data quality seen as key for both customer experience and business performance. But along with siloed data and compliance concerns , poor data quality is holding back enterprise AI projects.

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Metadata-Driven Data Warehouses are Ideal

TDAN

A metadata-driven data warehouse (MDW) offers a modern approach that is designed to make EDW development much more simplified and faster. It makes use of metadata (data about your data) as its foundation and combines data modeling and ETL functionalities to build data warehouses.