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OnlineAnalyticalProcessing (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.
This post provides guidance on how to build scalable analytical solutions for gaming industry use cases using Amazon Redshift Serverless. The following diagram is a conceptual analytics data hub reference architecture. External processes are the spokes feeding data to and from the hub. Data repositories represent the hub.
Each data entity provides an abstract representation of businessobjects within the database, such as, customers, general ledger accounts, or purchase orders. Data warehouses gained momentum back in the early 1990s as companies dealing with growing volumes of data were seeking ways to make analytics faster and more accessible.
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.
As data volumes continue to grow exponentially, traditional data warehousing solutions may struggle to keep up with the increasing demands for scalability, performance, and advanced analytics. The data warehouse is highly business critical with minimal allowable downtime.
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.
StarTree is a managed alternative that offers similar benefits for real-time analytics use cases. We highlight the key distinctions between open-source Pinot and StarTree, and provide valuable insights for organizations considering a more streamlined approach to their real-time analytics infrastructure.
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