Remove Dashboards Remove Data Science Remove OLAP Remove Online Analytical Processing
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Build a real-time analytics solution with Apache Pinot on AWS

AWS Big Data

Online Analytical Processing (OLAP) is crucial in modern data-driven apps, acting as an abstraction layer connecting raw data to users for efficient analysis. It organizes data into user-friendly structures, aligning with shared business definitions, ensuring users can analyze data with ease despite changes.

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The Future of AI in the Enterprise

Jet Global

The optimized data warehouse isn’t simply a number of relational databases cobbled together, however—it’s built on modern data storage structures such as the Online Analytical Processing (or OLAP) cubes. So how is the data extracted?

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How gaming companies can use Amazon Redshift Serverless to build scalable analytical applications faster and easier

AWS Big Data

Consumption This pillar consists of various consumption channels for enterprise analytical needs. It includes business intelligence (BI) users, canned and interactive reports, dashboards, data science workloads, Internet of Things (IoT), web apps, and third-party data consumers.

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The Future of AI in the Enterprise

Jet Global

The optimized data warehouse isn’t simply a number of relational databases cobbled together, however—it’s built on modern data storage structures such as the Online Analytical Processing (or OLAP) cubes. So how is the data extracted?

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What is business intelligence? Transforming data into business insights

CIO Business Intelligence

BI tools access and analyze data sets and present analytical findings in reports, summaries, dashboards, graphs, charts, and maps to provide users with detailed intelligence about the state of the business. Business intelligence examples Reporting is a central facet of BI and the dashboard is perhaps the archetypical BI tool.

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How to Build a Performant Data Warehouse in Redshift

Sisense

Fundamentally they are different than transactional databases we’ve seen in the past, and before we jump into how to build your data warehouse, it’s important to understand that distinction. OLTP vs OLAP. First, we’ll dive into the two types of databases: OLAP (Online Analytical Processing) and OLTP (Online Transaction Processing).

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Unleashing the power of Presto: The Uber case study

IBM Big Data Hub

Uber chose Presto for the flexibility it provides with compute separated from data storage. As a result, they continue to expand their use cases to include ETL, data science , data exploration, online analytical processing (OLAP), data lake analytics and federated queries.

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