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What is business intelligence? Transforming data into business insights

CIO Business Intelligence

Business intelligence definition Business intelligence (BI) is a set of strategies and technologies enterprises use to analyze business information and transform it into actionable insights that inform strategic and tactical business decisions.

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How OLAP and AI can enable better business

IBM Big Data Hub

Online analytical processing (OLAP) database systems and artificial intelligence (AI) complement each other and can help enhance data analysis and decision-making when used in tandem. Identifying best practices and benefits In the realm of OLAP, AI’s role is increasingly important.

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Unleashing the power of Presto: The Uber case study

IBM Big Data Hub

Presto is an open source distributed SQL query engine for data analytics and the data lakehouse, designed for running interactive analytic queries against datasets of all sizes, from gigabytes to petabytes. It excels in scalability and supports a wide range of analytical use cases.

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Build an Amazon Redshift data warehouse using an Amazon DynamoDB single-table design

AWS Big Data

This approach comes with a heavy computational cost in terms of processing and distributing the data across multiple tables while ensuring the system is ACID-compliant at all times, which can negatively impact performance and scalability. This is inefficient from both a cost and performance perspective.

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Unlocking Data Storage: The Traditional Data Warehouse vs. Cloud Data Warehouse

Sisense

The traditional data warehouses solved the problem of processing and synthesizing large data volumes, but they presented new challenges for the analytics process. Cloud data warehouses took the benefits of the cloud and applied them to data warehouses — bringing massive parallel processing to data teams of all sizes.

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How to Build a Performant Data Warehouse in Redshift

Sisense

First, we’ll dive into the two types of databases: OLAP (Online Analytical Processing) and OLTP (Online Transaction Processing). This approach made sense during a time in which the cost of storage was high, so normalizing tables reduced the total footprint. So let’s dive in! OLTP vs OLAP.