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Prevent Customer Churn: Customer Retention in the Transition to Microsoft D365 F&SCM

Jet Global

These benefits come with a caveat, however. In this respect, we often hear references to “switching costs” and “stickiness.” When the cost of switching to a new product is high, customers tend to remain where they are. Ultimately, though, switching costs are not so much about absolute numbers as they are about relative costs.

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Migrate Microsoft Azure Synapse Analytics to Amazon Redshift using AWS SCT

AWS Big Data

The decoupled compute and storage architecture of Amazon Redshift enables you to build highly scalable, resilient, and cost-effective workloads. Amazon Redshift provides comprehensive data security at no extra cost. For Connection name , enter a name (for example, olap-azure-synapse ). When the test is successful, choose OK.

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The Ultimate Guide to Data Warehouse Automation and Tools

Jet Global

The Benefits of Data Warehouse Automation. Automation benefits teams across the organization from IT to the C-suite. Though there is no shortage of ways automation can improve operations, these are the five most important benefits of data warehouse automation. It essentially allows businesses to fail fast in their testing.

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Data Model Development Using Jinja

Sisense

Data warehouses provide a consolidated, multidimensional view of data along with online analytical processing ( OLAP ) tools. OLAP tools help in the interactive and effective processing of data in a multidimensional space. The benefits of DBT with Jinja. Jinja’s important features. DBT + Sisense: A powerful combination.

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Data Mining – useful or not?

Jen Stirrup

One particular technology which is good for summarising and aggregating data is called OLAP (On Line Analytical Processing). Microsoft offers Data Mining at no extra cost as part of SQL Server 2005 and 2008, which is geared towards the average Excel user. Understanding the patterns in customer data can have many different applications.

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Themes and Conferences per Pacoid, Episode 11

Domino Data Lab

Moreover, they can be replaced with machine learning models to improve performance dramatically: “We have demonstrated that machine learned models have the potential to provide significant benefits over state-of-the-art indexes, and we believe this is a fruitful direction for future research.” That represents runtime overhead.

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Unleashing the power of Presto: The Uber case study

IBM Big Data Hub

Presto’s cost-based query optimizer, dynamic filtering and extensibility through user-defined functions make it a versatile tool in Uber’s analytics arsenal. The cost and constraints of traditional analytics soon reached their limit, forcing Uber to look elsewhere for a solution. This way, the queries run much faster.

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