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Optimize queries using dataset parameters in Amazon QuickSight

AWS Big Data

With dataset parameters, authors can optimize the experience and load time of dashboards that are connected live to external SQL-based sources. Optimized custom SQL in direct queries With dataset parameters, you no longer have to trade-off between the flexibility of using custom SQL logic and the performance of an optimized SQL query.

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In Times of Rapid Change, Business Process Modeling Becomes a Critical Tool

erwin

The key point is that useful change comes from routinely looking at process models and spotting a sub-optimality. For business process architects, being able to manage change and address key issues is what keeps the job function highly relevant to stakeholders.

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The Complete Digital Analytics Ecosystem: How To Win Big

Occam's Razor

Digital Analytics Ecosystem: Optimal Execution: Three Phases. Digital Analytics Ecosystem: Optimal Execution: Timing Expectations. You need to slice! You need to dice! Repeat after me: Slice, dice, drill!! The landing page optimization team will demand regular reports of all entry points to the site/app.

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Data Visualization Inspiration: Analysis To Insights To Action, Faster!

Occam's Razor

Short story #4: Multi-dimensional Slicing and Dicing! And sometimes they are indeed optimal: 7 Data Presentation Tips: Think, Simplify, Calibrate, Visualize. Better than the table, but perhaps less optimal than the Treemap. We know the optimization that is required. Short story #5: Segmented Stacked Square Charts.

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Super-charged pivot tables in Amazon QuickSight

AWS Big Data

This involved migrating complex tables and pivot tables, helping them slice and dice large datasets and deliver pixel-perfect views of their data to their stakeholders. For example, a customer 360 report sliced by different regions. Recently, Amazon FinTech migrated all their financial reporting to QuickSight.

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Sisense Q4 2020: Analytics for Every User With AI-Powered Insights

Sisense

Often, to find those types of insights, you slice, dice, and filter. Code-savvy users can crack open the optimized SQL code that drives live visualizations to easily understand and validate the logic. However, this requires a level of familiarity with the data itself. Deeper understanding with next-level reporting.

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Sisense’s Q2 Release: A Modern Data Experience Across the Analytics Continuum

Sisense

In-Warehouse Data Prep provides builders with the advanced functionality they need to rapidly transform and optimize raw data creating materialized views on cloud data warehouses. Additional capabilities.