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Solution overview OnlineAnalyticalProcessing (OLAP) is an effective tool for today’s data and business analysts. It helps you see your mission-critical metrics at different aggregation levels in a single pane of glass. In this post, we discuss how to use these extensions to simplify your queries in Amazon Redshift.
This includes the ETL processes that capture source data, the functional refinement and creation of data products, the aggregation for business metrics, and the consumption from analytics, business intelligence (BI), and ML. KPIs evaluate the operational metrics, cost metrics, and end-user response time metrics.
This post provides guidance on how to build scalable analytical solutions for gaming industry use cases using Amazon Redshift Serverless. This service is the core of this reference architecture on AWS and can address most analytical needs out of the box. You can collect metrics and events and analyze them for operational efficiency.
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 OnlineAnalyticalProcessing (or OLAP) cubes. Cubes are multi-dimensional datasets that are optimized for analyticalprocessing applications such as AI or BI solutions.
First, we’ll dive into the two types of databases: OLAP (OnlineAnalyticalProcessing) and OLTP (Online Transaction Processing). Think of it like something that houses the metrics used to power daily, weekly, or monthly business KPIs. OLTP vs OLAP. roll-ups of many rows of data).
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 OnlineAnalyticalProcessing (or OLAP) cubes. Cubes are multi-dimensional datasets that are optimized for analyticalprocessing applications such as AI or BI solutions.
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