Remove Data Warehouse Remove OLAP Remove Online Analytical Processing Remove Reference
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What Are OLAP (Online Analytical Processing) Tools?

Smart Data Collective

One of the most valuable tools available is OLAP. This tool can be great for handing SQL queries and other data queries. Every data scientist needs to understand the benefits that this technology offers. Using OLAP Tools Properly. Several or more cubes are used to separate OLAP databases. see more ).

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

AWS Big Data

Deriving business insights by identifying year-on-year sales growth is an example of an online analytical processing (OLAP) query. These types of queries are suited for a data warehouse. Amazon Redshift is fully managed, scalable, cloud data warehouse.

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How to Build a Performant Data Warehouse in Redshift

Sisense

This blog is intended to give an overview of the considerations you’ll want to make as you build your Redshift data warehouse to ensure you are getting the optimal performance. 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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Use the new SQL commands MERGE and QUALIFY to implement and validate change data capture in Amazon Redshift

AWS Big Data

Amazon Redshift is a fully managed, petabyte-scale data warehouse service in the cloud. Tens of thousands of customers use Amazon Redshift to process exabytes of data every day to power their analytics workloads. To learn about authoring and running notebooks, refer to Authoring and running notebooks.

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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. Benefits of BI BI helps business decision-makers get the information they need to make informed decisions.

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Navigating Data Entities, BYOD, and Data Lakes in Microsoft Dynamics

Jet Global

Its solution was to replicate data from the production database, using data entities, into a traditional relational database. Microsoft referred to this approach as “bring your own database” (BYOD). For more sophisticated multidimensional reporting functions, however, a more advanced approach to staging data is required.

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

AWS Big Data

Flexible and easy to use – The solutions should provide less restrictive, easy-to-access, and ready-to-use data. And unlike data warehouses, which are primarily analytical stores, a data hub is a combination of all types of repositories—analytical, transactional, operational, reference, and data I/O services, along with governance processes.