Remove Cost-Benefit Remove Interactive Remove OLAP Remove Online Analytical Processing
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The Future of AI in the Enterprise

Jet Global

While the technology behind enabling computers to simulate human thought has been developing, at times slowly, over the past half-century, the cost of implementation, readily available access to cloud computing, and practical business use cases are primed to help AI make a dramatic impact in the enterprise over the next few years.

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The Future of AI in the Enterprise

Jet Global

While the technology behind enabling computers to simulate human thought has been developing, at times slowly, over the past half-century, the cost of implementation, readily available access to cloud computing, and practical business use cases are primed to help AI make a dramatic impact in the enterprise over the next few years.

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

Sisense

While the architecture of traditional data warehouses and cloud data warehouses does differ, the ways in which data professionals interact with them (via SQL or SQL-like languages) is roughly the same. The reduced overhead and cost of ownership with cloud data warehouses often makes them much cheaper than traditional warehouses.

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Data Model Development Using Jinja

Sisense

The data model facilitates interaction among these groups by reformatting and restructuring data in order to define the relationship among datasets. . Data warehouses provide a consolidated, multidimensional view of data along with online analytical processing ( OLAP ) tools. The benefits of DBT with Jinja.

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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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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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